In this quick start guide we will walk through the steps of exporting data from a table in the Project Sandbox to a table in a Cosmic Frog model.
Please note that DataStar is currently in the Early Adopter phase, where only users who participate in the Early Adopter program have access to it. DataStar is rapidly evolving while we work towards the General Availability release later this year. For any questions about DataStar or the Early Adopter program, please feel free to reach out to Optilogic’s support team on support@optilogic.com.
This quick start guide builds upon a previous one where unique customers were created from historical shipments using a Leapfrog-generated Run SQL task. Please follow the steps in that quick start guide first if you want to follow along with the steps in this one. The starting point for this quick start is therefore a project named Import Historical Shipments, which contains a macro called Import Shipments. This macro has an Import task and a Run SQL task. The project has a Historical Shipments data connection of type = CSV, and the Project Sandbox contains 2 tables named rawshipments (42,656 records) and customers (1,333 records).
The steps we will walk through in this quick start guide are:
First, we will create a new Cosmic Frog model which does not have any data in it. We want to use this model to receive the data we export from the Project Sandbox.
As shown with the numbered steps in the screenshot below: while on the start page of Cosmic Frog, click on the Create Model button at the top of the screen. In the Create Frog Model form that comes up, type the model name, optionally add a description, and select the Empty Model option. Click on the Create Model button to complete the creation of the new model:
Next, we want to create a connection to the just created empty Cosmic Frog model in DataStar. To do so: open your DataStar application, then click on the Create Data Connection button at the top of the screen. In the Create Data Connection form that comes up, type the name of the connection (we are using the same name as the model, i.e. “Empty CF Model for DataStar Export”),optionally add a description, select Cosmic Frog Models in the Connection Type drop-down list, click on the name of the newly created empty model in the list of models, and click on Add Connection. The new data connection will now be shown in the list of connections on the Data Connections tab (shown in list format here):
Now, go to the Projects tab, and click on the “Import Historical Shipments” project to open it. We will first have a look at the Project Sandbox and the empty Cosmic Frog model connections, so click on the Data Connections tab:
The next step is to add and configure an Export Task to the Import Shipments macro. Click on the Macros tab in the panel on the left-hand side, and then on the Import Shipments macro to open it. Click on the Export task in the Tasks panel on the right-hand side and drag it onto the Macro Canvas. If you drag it close to the Run SQL task, it will automatically connect to it once you drop the Export task:
The Configuration panel on the right has now become the active panel:
Click on the AutoMap button, and in the message that comes up, select either Replace Mappings or Add New Mappings. Since we have not mapped anything yet, the result will be the same in this case. After using the AutoMap option, the mapping looks as follows:
We see that each source column is now mapped to a destination column of the same name. This is what we expect, since in the previous quick start guide, we made sure to tell Leapfrog when generating the Run SQL task for creating unique customers to match the schema of the customers table in Cosmic Frog models (“the Anura schema”).
If the Import Shipments macro has been run previously, we can just run the new Export Customers task by itself (hover over the task in the Macro Canvas and click on the play button that comes up), otherwise we can choose to run the full macro by clicking on the green Run button at the right top. Once completed, click on the Data Connections tab to check the results:
Above, the AutoMap functionality was used to map all 3 source columns to the correct destination columns. Here, we will go into some more detail on manually mapping and additional options users have to quickly sort and filter the list of mappings.
Cosmic Frog’s new breakpoints feature enables users to create maps which relay even more supply chain data in just one glance. Lines and points can now be styled based on field values from the underlying input or output table the lines/points are drawn from.
In this Help Center article, we will cover where to find the breakpoints feature for both point and line layers and how to configure them. A basic knowledge of how to configure maps and their layers in Cosmic Frog is assumed; users unfamiliar with maps in Cosmic Frog are encouraged to first read the “Getting Started with Maps” Help Center article.
First, we will walk through how to apply breakpoints to map layers of type = line, which are often used to show flows between locations. With breakpoints we can style the lines between origins and destinations for example based on how much is flowing in terms of quantity, volume or weight. It is also possible to style the lines on other numeric fields, like costs, distances or time.
Consider the following map showing flows (dark green lines) to customers (light green circles):
Next, we will go to the Layer Style pane on which breakpoints can be turned on and configured:
Once the Breakpoints toggle has been turned on (slide right, the color turns blue), the breakpoint configuration options become visible:
One additional note is that one can use tab to navigate through the cells in the Breakpoints table.
The next screenshot shows breakpoints based on the Flow Quantity field (in the Optimization Flow Summary) for which the Max Values have been auto generated:
Users can customize the style of each individual breakpoint:
Please note:
Configuring and applying breakpoints on point layers is very similar to those on line layers. We will walk through the steps in the next 4 screenshots in slightly less detail. In this example we will base the size of the customer locations on the map on the total demand they have been served:
Next, we again look at the Layer Style pane of the layer:
Lastly, user would like to gradually increase the color of the customer circles from light to dark green and the size from small to bigger based on the breakpoint the customer belongs to:
As always, please feel free to reach out to Optilogic support at support@optilogic.com should you have any questions.
In this quick start guide we will show how users can seamlessly go from using the Resource Library, Cosmic Frog and DataStar applications on the Optilogic platform to creating visualizations in Power BI. The example covers cost to serve analysis using a global sourcing model. We will run 2 scenarios in this Cosmic Frog model with the goal to visualize the total cost difference between the scenarios by customer on a map. We do this by coloring the customers based on the cost difference.
The steps we will walk through are:
Please note that DataStar is currently in the Early Adopter phase, where only users who participate in the Early Adopter program have access to it. DataStar is rapidly evolving while we work towards the General Availability release later this year. For any questions about DataStar or the Early Adopter program, please feel free to reach out to Optilogic’s support team on support@optilogic.com.
We will first copy the model named “Global Sourcing – Cost to Serve” from the Resource Library to our Optilogic account (learn more about the Resource Library in this help center article):
Now that the model is in the user’s account, it can be opened in the Cosmic Frog application:
We will only have a brief look at some high-level outputs in Cosmic Frog in this quick start guide, but feel free to explore additional outputs. You can learn more about Cosmic Frog through these help center articles. Let us have a quick look at the Optimization Network Summary output table and the map:
Our next step is to import the needed input table and output table of the Global Sourcing – Cost to Serve model into DataStar. Open the DataStar application on the Optilogic platform by clicking on its icon in the applications list on the left-hand side. In DataStar, we first create a new project named “Cost to Serve Analysis” and set up a data connection to the Global Sourcing – Cost to Serve model, which we will call “Global Sourcing C2S CF Model”. See the Creating Projects & Data Connections section in the Getting Started with DataStar help center article on how to create projects and data connections. Then, we want to create a macro which will calculate the increase/decrease in total cost by customer between the 2 scenarios. We build this macro as follows:
The configuration of the first import task, C2S Path Summary, is shown in this screenshot:
The configuration of the other import task, Customers, uses the same Source Data Connection, but instead of the optimizationcosttoservepathsummary table, we choose the customers table as the table to import. Again, the Project Sandbox is the Destination Data Connection, and the new table is simply called customers.
Instead of writing SQL queries ourselves to pivot the data in the cost to serve path summary table to create a new table where for each customer there is a row which has the customer name and the total cost for each scenario, we can use Leapfrog to do it for us. See the Leapfrog section in the Getting Started with DataStar help center article and this quick start guide on using natural language to create DataStar tasks to learn more about using Leapfrog in DataStar effectively. For the Pivot Total Cost by Scenario by Customer task, the 2 Leapfrog prompts that were used to create the task are shown in the following screenshot:
The SQL Script reads:
DROP TABLE IF EXISTS total_cost_by_customer_combined;
CREATE TABLE total_cost_by_customer_combined AS
SELECT
pathdestination AS customer,
SUM(CASE WHEN scenarioname = 'Baseline' THEN pathcost ELSE 0 END)
AS total_cost_baseline,
SUM(CASE WHEN scenarioname = 'OpenPotentialFacilities' THEN pathcost ELSE 0 END)
AS total_cost_openpotentialfacilities
FROM c2s_path_summary
WHERE scenarioname IN ('Baseline', 'OpenPotentialFacilities')
GROUP BY pathdestination
ORDER BY pathdestination;
To create the Calculate Cost Savings by Customer task, we gave Leapfrog the following prompt: “Use the total cost by customer table and add a column to calculate cost savings as the baseline cost minus the openpotentalfacilities cost”. The resulting SQL Script reads as follows:
ALTER TABLE total_cost_by_customer_combined ADD COLUMN cost_savings DOUBLE PRECISION;
UPDATE total_cost_by_customer_combined SET
cost_savings = total_cost_baseline - total_cost_openpotentialfacilities;
This task is also added to the macro; its name is "Calculate Cost Savings by Customer".
Lastly, we give Leapfrog the following prompt to join the table with cost savings (total_cost_by_customer_combined) and the customers table to add the coordinates from the customers table to the cost savings table: “Join the customers and total_cost_by_customer_combined tables on customer and add the latitude and longitude columns from the customers table to the total_cost_by_customer_combined table. Use an inner join and do not create a new table, add the columns to the existing total_cost_by_customer_combined table”. This is the resulting SQL Script, which was added to the macro as the "Add Coordinates to Cost Savings" task:
ALTER TABLE total_cost_by_customer_combined ADD COLUMN latitude VARCHAR;
ALTER TABLE total_cost_by_customer_combined ADD COLUMN longitude VARCHAR;
UPDATE total_cost_by_customer_combined SET latitude = c.latitude
FROM customers AS c
WHERE total_cost_by_customer_combined.customer = c.customername;
UPDATE total_cost_by_customer_combined SET longitude = c.longitude
FROM customers AS c
WHERE total_cost_by_customer_combined.customer = c.customername;We can now run the macro, and once it is completed, we take a look at the tables present in the Project Sandbox:
We will use Microsoft Power BI to visualize the change in total cost between the 2 scenarios by customer on a map. To do so, we first need to set up a connection to the DataStar project sandbox from within Power BI. Please follow the steps in the “Connecting to Optilogic with Microsoft Power BI” help center article to create this connection. Here we will just show the step to get the connection information for the DataStar Project Sandbox, which underneath is a PostgreSQL database (next screenshot) and selecting the table(s) to use in Power BI on the Navigator screen (screenshot after this one):
After selecting the connection within Power BI and providing the credentials again, on the Navigator screen, choose to use just the total_cost_by_customer_combined table as this one has all the information needed for the visualization:
We will set up the visualization on a map using the total_cost_by_customer_combined table that we have just selected for use in Power BI using the following steps:
With the above configuration, the map will look as follows:
Green customers are those where the total cost went down in the OpenPotentialFacilities scenario, i.e. there are savings for this customer. The darker the green, the higher the savings. White customers did not see a lot of difference in their total costs between the 2 scenarios. The one that is hovered over, in Marysville in Washington state, has a small increase of $149.71 in total costs in the OpenPotentialFacilities scenario as compared to the Baseline scenario. Red customers are those where the total cost went up in the OpenPotentialFacilities scenario (i.e. the cost savings are a negative number); the darker the red, the higher the increase in total costs. As expected, the customers with the highest cost savings (darkest green) are those located in Texas and Florida, as they are now being served from DCs closer to them.
To give users an idea of what type of visualization and interactivity is possible within Power BI, we will briefly cover the 2 following screenshots. These are of a different Cosmic Frog model for which a cost to serve analysis is performed too. Two scenarios were run in this model: Baseline DC and Blue Sky DC. In the Baseline scenario, customers are assigned to their current DCs and in the Blue Sky scenario, they can be re-assigned to other DCs. The chart on the top left shows the cost savings by region (= US state) that are identified in the Blue Sky DC scenario. The other visualizations on the dashboard are all on maps: the top right map shows the customers which are colored based on which DC serves them in the Baseline scenario, the bottom 2 maps shows the DCs used in the Baseline (left) and the DCs used in the Blue Sky scenario.
To drill into the differences between the 2 scenarios, users can expand the regions in the top left chart and select 1 or multiple individual customers. This is an interactive chart, and the 3 maps are then automatically filtered for the selected location(s). In the below screenshot, the user has expanded the NC region and then selected customer CZ_593_NC in the top left chart. In this chart, we see that the cost savings for this customer in the Blue Sky DC scenario as compared to the Baseline scenario amount to $309k. From the Customers map (top right) and Baseline DC map (bottom left) we see that this customer was served from the Chicago DC in the Baseline. We can tell from the Blue Sky DC map (bottom right) that this customer is re-assigned to be served from the Philadelphia DC in the Blue Sky DC scenario.
DataStar is Optilogic’s new AI-powered data product designed to help supply chain teams build and update models & scenarios and power apps faster & easier than ever before. It enables users to create flexible, accessible, and repeatable workflows with zero learning curve—combining drag-and-drop simplicity, natural language AI, and deep supply chain context.
Today, up to an estimated 80% of a modeler's time is spent on data—connecting, cleaning, transforming, validating, and integrating it to build or refresh models. DataStar shrinks that time by up to 50%, enabling teams to:
The 2 main goals of DataStar are 1) ease of use, and 2) effortless collaboration, these are achieved by:
DataStar is currently in the Early Adopter (EA) phase and is rapidly evolving while we work towards a General Availability release later this year. Therefore, this documentation will be regularly updated as new functionality becomes available. If you are interested in learning more about DataStar or the Early Adopter program, please contact the Optilogic support team at support@optilogic.com.
In this documentation, we will start with a high-level overview of the DataStar building blocks. Next, creating projects and data connections will be covered before diving into the details of adding tasks and chaining them together into macros, which can then be run to accomplish the data goals of your project.
Before diving into more details in later sections, this section will describe the main building blocks of DataStar, which include Data Connections, Projects, Macros, and Tasks.
As DataStar is currently in the Early Adopter phase, this document will be updated regularly as more features become available. In this section, references to future capabilities which are not yet released are included in order to paint the larger picture of how DataStar will work. In the text it is made clear which parts are and which are not yet available in the first Early Adopter release.
Since DataStar is all about working with data, Data Connections are an important part of DataStar. These enable users to quickly connect to and pull in data from a range of data sources. Data Connections in DataStar:
Projects are the main container of work within DataStar. Typically, a Project will aim to achieve a certain goal by performing all or a subset of importing specific data, then cleansing, transforming & blending it, and finally publishing the results to another file/database. The scope of DataStar Projects can vary greatly, think for example of following 2 examples:
Projects consist of one or multiple macros which in turn consist of 1 or multiple macros and/or tasks. Tasks are the individual actions or steps which can be chained together within a macro to accomplish a specific goal. In future, multiple macros can also be chained together in another macro in order to run a larger process. Tasks are split into the following 3 categories in DataStar:
The next screenshot shows an example Macro called Shipments which consists of 7 individual tasks that are chained together to create transportation policies for a Cosmic Frog model from imported Shipments and Costs data. As a last step, it also runs the model with the updated transportation policies:
Note that not all tasks to build a macro like this are yet available in the current Early Adopter version of DataStar.
Every project by default contains a Data Connection named Project Sandbox. This data connection is not global to all DataStar projects; it is specific to the project it is part of. The Project Sandbox is a Postgres database where users generally import the raw data from the other data connections into, perform transformations in, save intermediate states of data in, and then publish the results out to a Cosmic Frog model (which is a data connection different than the Project Sandbox connection). It is also possible that some of the data in the Project Sandbox is the final result/deliverable of the DataStar Project or that the results are published into a different type of file or system that is set up as a data connection rather than into a Cosmic Frog model.
The next diagram shows how Data Connections, Projects, and Macros relate to each other in DataStar:
For the remainder of this document, only current Early Adopter DataStar functionality is shown in the screenshots (with a few exceptions, which will be noted in the text). The text mostly just covers current functionality and will at times reference features which will be included in future DataStar versions. Within DataStar, users may notice buttons, options in drop-down and right-click menus that have been disabled (greyed out or cannot be clicked on), since new functionality is being worked on continuously. These will be enabled over time and other new features will also gradually be added.
On the start page of DataStar user will be shown the existing projects and data connections. They can be opened, or deleted here, and users also have the ability to create new projects and data connections on this start page.
The next screenshot shows the existing projects in card format:
New projects can be created by clicking on the Create Project button in the toolbar at the top of the DataStar application:
The next screenshot shows the Data Connections that have already been set up in DataStar in list view:
New data connections can be created by clicking on the Create Data Connection button in the toolbar at the top of the DataStar application:
The remainder of the Create Data Connection form will change depending on the type of connection that was chosen as different types of connections require different inputs (e.g. host, port, server, schema, etc.). In our example, the user chooses CSV Files as the connection type:
In our walk-through here, the user drags and drops a Shipments.csv file from their local computer on top of the Drag and drop area:
Now let us look at a project when it is open in DataStar. We will first get a lay of the land with a high-level overview screenshot and then go into more detail for the different parts of the DataStar user interface:
Next, we will dive a bit deeper into a macro:
The Macro Canvas for the Customers from Shipments macro is shown in the following screenshot (note that the Export task shown is not yet available in the Early Adopter release):
In addition to the above, please note following regarding the Macro Canvas:
We will move on to covering the 3 tabs on the right-hand side pane, starting with the Tasks tab:
Users can click on a task in the tasks list and then drag and drop it onto the macro canvas to incorporate it into a macro.
When adding a new task, it needs to be configured, which can be done on the Configuration tab. When a task is newly dropped onto the Macro Canvas its Configuration tab is automatically opened on the right-hand side pane. To make the configuration tab of an already existing task active, click on the task in the Macros tab on the left-hand side pane or click on the task in the Macro Canvas. The configuration options will differ by type of task, here the Configuration tab of an Import task is shown as an example:
Please note that:
Leapfrog in DataStar (aka D*AI) is an AI-powered feature that transforms natural language requests into executable DataStar tasks. Users can describe what they want to accomplish in plain language, and Leapfrog automatically generates the corresponding task or SQL query without requiring technical coding skills or manual inputs for task details. This capability enables both technical and non-technical users to efficiently manipulate data, build Cosmic Frog models, and extract insights through conversational interactions with DataStar.
Note that there are 2 appendices at the end of this documentation where 1) details around Leapfrog in DataStar's current features & limitations are covered and 2) Leapfrog's data usage and security policies are summarized.
Leapfrog’s response to this prompt is as follows:
DROP TABLE IF Exists customers;
CREATE TABLE customers AS
SELECT destination_store AS customer, AVG(destination_latitude) AS latitude, AVG(destination_longitude) AS longitude FROM rawshipments
GROUP BY destination_storeFinally, we will have a look at the Conversations pane:
Within a Leapfrog conversation, Leapfrog remembers the prompts and responses thus far. User can therefore build upon previous questions, for example by following up with a prompt along the lines of “Like that, but instead of using a cutoff date of August 10, 2025, use September 24, 2025”.
Additional helpful Leapfrog in DataStar links:
Users can run a Macro by selecting it and then clicking on the green Run button at the right top of the DataStar application:
Please note that:
Next, we will cover the Logs tab at the bottom of the Macro Canvas where logs of macros that are running/have been run can be found:
When a macro has not yet been run, the Logs tab will contain a message with a Run button, which can also be used to kick off a macro run. When a macro is running or has been run, the log will look similar to the following:
The next screenshot shows the log of an earlier run of the same macro where the first task ended in an error:
The progress of DataStar macro and task runs can also be monitored in the Run Manager application where runs can be cancelled if needed too:
Please note that:
In the Data Connections tab on the left-hand side pane the available data connections are listed:
Next, we will have a look at what the connections list looks like when the connections have been expanded:
The tables within a connection can be opened within DataStar. They are then displayed in the central part of DataStar where the Macro Canvas is showing when a macro is the active tab.
Please note: currently, a data preview of up to 10,000 records for a table is displayed for tables in DataStar. This means that any filtering or sorting done on tables larger than 10k records is done on this subset of 10k records. At the end of this section it is explained how datasets containing more than 10k records per table can be explored by using the SQL Editor application.
A table can be filtered based on values in one or multiple columns:
Columns can be re-ordered and hidden/shown as described in the Appendix; this can be done using the Columns fold-out pane too:
Finally, filters can also be configured from a fold-out pane:
Users can explore the complete dataset of connections with tables larger than 10k records in other applications on the Optilogic platform, depending on the type of connection:
Here is how to find the database and table(s) of interest on SQL Editor:
Here are a few additional links that may be helpful:
We hope you are as excited about starting to work with DataStar as we are! Please stay tuned for regular updates to both DataStar and all the accompanying documentation. As always, for any questions or feedback, feel free to contact our support team at support@optilogic.com.
The grids used in DataStar can be customized and we will cover the options available through the screenshot below. This screenshot is of the list of CSV files in user's Optilogic account when creating a new CSV File connection. The same grid options are available on the grid in the Logs tab and when viewing tables that are part of any Data Connections in the central part of DataStar.
Leapfrog's brainpower comes from:
All training processes are owned and managed by Optilogic — no outside data is used.
When you ask Leapfrog a question:
Your conversations (prompts, answers, feedback) are stored securely at the user level.
In this quick start guide we will show how Leapfrog AI can be used in DataStar to generate tasks from natural language prompts, no coding necessary!
Please note that DataStar is currently in the Early Adopter phase, where only users who participate in the Early Adopter program have access to it. DataStar is rapidly evolving while we work towards the General Availability release later this year. For any questions about DataStar or the Early Adopter program, please feel free to reach out to Optilogic’s support team on support@optilogic.com.
This quick start guide builds upon the previous one where a CSV file was imported into the Project Sandbox, please follow the steps in there first if you want to follow along with the steps in this quick start. The starting point for this quick start is therefore a project named Import Historical Shipments that has a Historical Shipments data connection of type = CSV, and a table in the Project Sandbox named rawshipments, which contains 42,656 records.
The Shipments.csv file that was imported into the rawshipments table has following data structure (showing 5 of the 42,656 records):
Our goal in this quick start is to create a task using Leapfrog that will use this data (from the rawshipments table in the Project Sandbox) to create a list of unique customers, where the destination stores function as the customers. Ultimately, this list of customers will be used to populate the Customers input table of a Cosmic Frog model. A few things to consider when formulating the prompt are:
Within the Import Historical Shipments DataStar project, click on the Import Shipments macro to open it in the macro canvas, you should see the Start and Import Raw Shipments tasks on the canvas. Then open Leapfrog by clicking on the Leapfrog tab, which is the right-most tab in the panel on the right-hand side of the macro canvas. Alternatively, click on the “How can I help you” speech bubble with the frog icon in the toolbar at the top of DataStar. Next, we will write our prompt in the “Write a message…” textbox.
Keeping in mind the 5 items mentioned above, the prompt we use is the following: “Use the raw shipments table to create unique customers (the destination column); average the latitudes and longitudes. Only use records with the ship date between July 1 2024 and June 30 2025. Match to the anura schema of the Customers table”. Please note that tips for writing successful prompts and variations of this prompt (some working, others not) can be found in the Tips for Prompt Writing section further below.
After clicking on the send icon to submit the prompt, Leapfrog will take a few seconds to consider the prompt and formulate a response. The response will look similar to the following screenshot, where we see from top to bottom:
The resulting SQL Script reads:
DROP TABLE IF EXISTS Customers;
CREATE TABLE Customers AS
SELECT Destination_Store AS CustomerName, AVG(Destination_Latitude) AS Latitude, AVG(Destination_Longitude) AS Longitude
FROM RawShipments
WHERE TO_DATE(Ship_Date, 'DD/MM/YYYY') BETWEEN '2024-07-01' AND '2025-06-30'
GROUP BY Destination_Store;
Those who are familiar with SQL, will be able to tell that this will indeed achieve our goal. Since that is the case, we can click on the Add to Macro button at the bottom of Leapfrog’s response to add this as a Run SQL task to our Import Shipments macro. When hovering over this button, you will see Leapfrog suggests where to put it on the macro canvas and to connect it to the Import Raw Shipments task, which is what we want. When next clicking on the Add to Macro button it will be added.
We can test our macro so far, by clicking on the green Run button at the right top of DataStar. Please note that:
Once the macro is done running, we can check the results. Go to the Data Connections tab, expand the Project Sandbox connection and click on the customers table to open it in the central part of DataStar:
We see that the customers table resulting from running the Leapfrog-created Run SQL task contains 1,333 records. Also notice that its schema matches that of the Customers table of Cosmic Frog models, which includes columns named customername, latitude, and longitude.
Writing prompts for Leapfrog that will create successful responses (e.g. the SQL Script generated will achieve what the prompt-writer intended) may take a bit of practice. This Mastering Leapfrog for SQL Use Cases: How to write Prompts that get Results post on the Frogger Pond community portal has some great advice which applies to Leapfrog in DataStar too. It is highly recommended to give it a read; the main points of advice follow here too:
As an example, let us look at variations of the prompt we used in this quick start guide, to gauge the level of granularity needed for a successful response. In this table, the prompts are listed from least to most granular:
Note that in the above prompts, we are quite precise about table and column names and no typos are made by the prompt writer. However, Leapfrog can generally manage well with typos and often also pick up table and column names when not explicitly used in the prompt. So while generally being more explicit results in higher accuracy, it is not necessary to always be extremely explicit and we just recommend to be as explicit as you can be.
For various reasons, many supply chains need to deal with returns. This can for example be due to packaging materials coming back to be reused at plants or DCs, retail customers returning finished goods that they are not happy with, defective products, etc. Previously, these returns could mostly be modelled within Cosmic Frog NEO (Network Optimization) models by using some tricks and workarounds. But with the latest Cosmic Frog release, returns are now supported natively, so that the reuse, repurposing, or recycling of these retuned products to help companies reduce costs, minimize waste, and improve overall supply chain efficiency can be taken into account easily.
This documentation will first provide an overview of how returns work in a Cosmic Frog model and then walk through an example model of a retailer which includes modelling the returns of finished goods. The appendix details all the new returns-related fields in several new tables and some of the existing tables.
When modelling returns in Cosmic Frog:
Users need to use 2 new input tables to set up returns:
The Return Ratios table contains the information on how much return-product is returned for a certain amount of product delivered to a certain destination:
The Return Policies table is used to indicate where returned products need to go to and the rules around multiple possible destinations. Optionally, costs can be associated with the returns here and a maximum distance allowed for returns can be entered on this table too.
Note that both these tables have Status and Notes fields (not shown in the screenshots), like most Cosmic Frog input tables have. These are often used for scenario creation where the Status is set to Exclude in the table itself and changed to Include in select scenarios based on text in the Notes field.
All columns on these 2 returns-related input table are explained in more detail in the appendix.
In addition to populating the Return Policies and Return Ratios tables, users need to be aware that additional model structure needed for the returned products may need to be put in place:
The Optimization Return Summary output table is a new output table that will be generated for Neo runs if returns are included in the modelling:
This table and all its fields are explained in detail in the appendix.
The Optimization Flow Summary output table will contain additional records for models that include returns; they can be identified by filtering the Flow Type field for “Return”:
These 2 records show the return flows and associated transportation costs for the Bag_1 and Bag_2 products from CZ_001, going to DC_Cincinnati, that we saw in the Optimization Return Summary table screenshot above.
In addition to the new Optimization Return Summary output table, and new records of Flow Type = Return in the Optimization Flow Summary output table, following existing output tables now contain additional fields related to returns:
The example Returns model can be copied from the Resource Library to a user’s Optilogic account (see this help center article on how to use the Resource Library). It models the US supply chain of a fashion bag retailer. The model’s locations and flows both to customers and between DCs are shown in this screenshot (returns are not yet included here):
Historically, the retailer had 1 main DC in Cincinnati, Ohio, where all products were received and all 869 customers were fulfilled from. Over time, 4 secondary DCs were added based on Greenfield analysis, 2 bigger ones in Clovis, California, and Jersey City, New Jersey, and 2 smaller ones in West Palm Beach, Florida, and Las Lomas, Texas. These secondary DCs receive product from the Cincinnati DC and serve their own set of customers. The main DC in Cincinnati and the 2 bigger secondary DCs (Clovis, CA, and Jersey City, NJ) can handle returns currently: returns are received there and re-used to fulfill demand. However, until now, these returns had not been taken into account in the modelling. In this model we will explore following scenarios:
Other model features:
Please note that in this model the order of columns in the tables has sometimes been changed to put those containing data together on the left-hand side of the table. All columns are still present in the table but may be in a different position than you are used to. Columns can be reset to their default position by choosing “Reset Columns” from the menu that comes up when clicking on the icon with 3 vertical dots to the right of a column name.
After running the baseline scenario (which does not include returns), we take a look at the Financials: Scenario Cost Comparison chart in the Optimization Scenario Comparison dashboard (in Cosmic Frog’s Analytics module):
We see that the biggest cost currently is the production cost at 394.7M (= procurement of all product into Cincinnati), followed by transportation costs at 125.9M. The total supply chain cost of this scenario is 625.3M.
In this scenario we want to include how returns currently work: Cincinnati, Clovis, and Jersey City customers return their products to their local DCs whereas West Palm Beach and Las Lomas customers return their products to the main DC in Cincinnati. To set this up, we need to add records to the Return Policies, Return Ratios, and Transportation Policies input tables. To not change the Baseline scenario, all new records will be added with Status = Exclude, and the Notes field populated so it can be used to filter on in scenario items that change the Status to Include for subsets of records. Starting with the Return Policies table:
Next, we need to add records to the Transportation Policies table so that there is at least 1 lane available for each site-product-destination combination set up in the return policies table. For this example, we add records to the Transportation Policies table that match the ones added to the Return Policies table exactly, while additionally setting Mode Name = Returns, Unit Cost = 0.04 and Unit Cost UOM = EA-MI (the latter is not shown in the screenshot below), which means the transportation cost on return lanes is 0.04 per unit per mile:
Finally, we also need to indicate how much product is returned in the Return Ratios table. Since we want to model different ratios by individual customer and individual product, this table does not use any groups. Groups can however be used in this table too for the Site Name, Product Name, Period Name, and Return Product Name fields.
Please note that adding records to these 3 tables and including them in the scenarios is sufficient to capture returns in this example model. For other models it is possible that additional tables may need to be used, see the Other Input Tables section above.
Now that we have populated the input tables to capture returns, we can set up scenario S1 which will change the Status of the appropriate records in these tables from Exclude to Include:
After running this scenario S1, we are first having a look at the map, where we will be showing the DCs, Customers and the Return Flows for scenario S1. This has been set up in the map named Supply Chain (S1) in the model from the Resource Library. To set this map up, we first copied the Supply Chain (Baseline) map and renamed it to Supply Chain (S1). Then clicked on the map’s name (Supply Chain (S1)) to open it and in the Map Filters form that is showing on the right-hand side of the screen changed the scenario to “S1 Include Returns” in the Scenario drop-down. To configure the Return Flows, we added a new Map Layer, and configured its Condition Builder form as follows (learn more about Maps and how to configure them in this Help Center article):
The resulting map is shown in this next screenshot:
We see that, as expected, the bulk of the returns are going back the main DC in Cincinnati: from its local customers, but also from the customers served by the 2 smaller DCs in Las Lomas and West Palm Beach DCs. The customers served by the Clovis and Jersey City DCs return their products to their local DCs.
To assess the financial impact of including returns in the model, we again look at the Financials: Scenario Cost Comparison chart in the Optimization Scenario Comparison dashboard, comparing the S1 scenario to the Baseline scenario:
We see that including returns in S1 leads to:
Seeing that the main driver for the overall supply chain costs being higher when including returns are the high transportation costs for returning products, especially those travelling long distances from the Las Lomas and West Palm Beach customers to the Cincinnati DC sparks the idea to explore if it would be more beneficial for the Las Lomas and/or West Palm Beach customers to return their products to their local DC, rather than the Cincinnati DC. This will be modelled in the next three scenarios.
Building upon scenario S1, we will run 2 scenarios (S2 and S3) where it will be examined if it is beneficial cost-wise for West Palm Beach customers to return their products to their local West Palm Beach DC (S2) and for Las Lomas customers to return their products to their local Las Lomas DC (S3) rather than to the Cincinnati DC. In order to be able to handle returns, the fixed operating costs at these DCs are increased by 0.5M to 3.5M:
Scenarios S2 and S3 are run, and first we look at the map to check the return flows for the West Palm Beach and Las Lomas customers, respectively (copied the map for S1, renamed it, and then changed the scenario by clicking on the map’s name and selecting the S2/S3 scenario from the Scenario drop-down in the Map Filters pane on the right-hand side):
As expected, due to how we set up these scenarios, now all returns from these customers go to their local DC, rather than to DC-Cincinnati which was the case in scenario S1.
Let us next look at the overall costs for these 2 scenarios and compare them back to the S1 and Baseline scenarios:
Besides some smaller reductions in the inbound and outbound costs in S2 and S3 as compared to S1, the transportation costs are reduced by sizeable amounts: 6.9M (S2 compared to S1) and 9.4M (S3 compared to S1), while the production (= procurement) costs are the same across these 3 scenarios. The reduction in transportation costs outweighs the 0.5M increase in fixed operating costs to be able to handle returns at the West Palm Beach and Las Lomas DCs. Also note that both scenario S2 and S3 have a lower total cost than the Baseline scenario.
Since it is beneficial to have the West Palm Beach and Las Lomas DCs handle returns, scenario S4 where this capability is included for both DCs is set up and run:
The S4 scenario increases the fixed operating costs at both these DCs from 3M to 3.5M (scenario items “Incr Operating Cost S2” and “Incr Operating Cost S3”), sets the Status of all records on the Return Ratios table to Include (the Include Return Ratios scenario item), and sets the Status to Include for records on the Return Policies and Transportation Policies tables where the Notes field contains the text “S4” (the “Include Return Policies S4” and “Include Return TPs S4” items), which are records where customers all ship their returns back to their local DC. We first check on the map if this is working as expected after running the S4 scenario:
We notice that indeed there are no more returns going back to the Cincinnati DC from Las Lomas or West Palm Beach customers.
Finally, we expect the costs of this scenario to be the lowest overall since we should see the combined reductions of scenarios S2 and S3:
Between S1 and S4:
In addition to looking at maps or graphs, users can also use the output tables to understand the overall costs and flows, including those of the returns included in the network.
Often, users will start by looking at the overall cost picture using the Optimization Network Summary output table, which summarizes total costs and quantities at the scenario level:
For each scenario, we are showing the Total Supply Chain Cost and Total Return Quantity fields here. As mentioned, the Baseline did not include any returns, whereas scenarios S1-4 did, which is reflected in the Total Return Quantity values. There are many more fields available on this output table, but in the next screenshot we are just showing the individual cost buckets that are used in this model (all other cost fields are 0):
How these costs increase/decrease between scenarios has been discussed above when looking at the “Financials: Scenario Cost Comparison” chart in the “Optimization Scenario Comparison” dashboard. In summary:
Please note that on this table, there is also a Total Return Cost field. It is 0 in this example model. It would be > 0 if the Unit Cost field on the Return Policies table had been populated, which is a field where any specific cost related to the return can be captured. In our example Returns model, the return costs are entirely captured by the transportation costs and fixed operating costs specified.
The Optimization Return Summary output table is a new output table that has been added to summarize returns at the scenario-returning site-product-return product-period level:
Looking at the first record here, we understand that in the S1 scenario, CZ_001 was served 8,850 units of Bag_1, while 876.15 units of Bag_1 were returned.
Lastly, we can also see individual return flows in the Optimization Flow Summary table by filtering the Flow Type field for “Return”:
Note that the product name for these flows is of the product that is being returned.
The example Returns model described above assumes that 100% of the returned Bag_1 and Bag_2 products can be reused. Here we will discuss through screenshots how the model can be adjusted to take into account that only 70% of Bag_1 returns and 50% of Bag_2 returns can be reused. To achieve this, we will need to add an additional “return” product for each finished good, set up bills of materials, and add records to the policies tables for the required additional model structure.
The tables that will be updated and for which we will see a screenshot each below are: Products, Groups, Return Policies, Return Ratios, Transportation Policies, Warehousing Policies, Bills of Materials, and Production Policies.
Two products are added here, 1 for each finished good: Bag_1_Return and Bag_2_Return. This way we can distinguish the return product from the sellable finished goods, apply different policies/costs to them, and convert a percentage back into the sellable items. The naming convention of adding “_Return” to the finished good name makes for easy filtering and provides clarity around what the product’s role is in the model. Of course, users can use different naming conventions.
The same unit value as for the finished goods is used for the return products, so that inventory carrying cost calculations are consistent. A unit price (again, same as the finished goods) has been entered too, but this will not actually be used by the model as these “_Return” products are not used to serve customer demand.
To facilitate setting up policies where the return products behave the same (e.g. same lanes, same costs, etc.), we add an “All_Return_Products” group to the Groups table, which consists of the 2 return products:
In the Return Policies table, the Return Product Name column needs to be updated to reflect that the products that are being returned are the “_Return” products. Previously, the Return Product Name was set to the All_Products group for each record, and it is now updated to the All_Return_Products group. Updating a field in all records or a subset of filtered records to the same value can be done using the Bulk Update Column functionality, which can be accessed by clicking on the icon with 3 vertical dots to the right of the column name and then choosing “Bulk Update this Column” in the list of options that comes up.
We keep the ratios of how much product comes back for each unit of Bag_1 / Bag_2 sold the same, however we need to update the Return Product Name field on all records to reflect that it is the “_Return” product that comes back. Since this table does not use groups because the return ratios are different for different customer-finished good combinations, the best way to update this table is to also use the bulk update column functionality:
Note that only 4 of the 1,738 records in this table are shown in the screenshot below.
Here, the records representing the lane back from the customers to the DC they send returns back to need to be updated so that the products going back are the “_Return” ones. Since the transportation costs of the return products are the same, we can keep using the grouped policies and just bulk update the Product Name column of the records where Mode Name equals Returns: change the values from the All_Products group to the All_Return_Products group.
We want to apply the same inbound and outbound handling costs for the return products as we do for the finished goods, so a record is added for the “All_Return_Products” group at All_DCs in the Warehousing Policies table:
We can use the Bills of Materials (BOM) table to convert the “_Return” products back into the finished goods, applying the desired percentage that will be suitable for reuse. For Bag_1, we want to set up that 70% of the returns can be reused as finished goods, this is done by setting up a BOM as follows (the first 2 records in the screenshot below):
Similarly, we set up the BOM “Reuse_Bag_2” where 1 unit of Bag_2_Return results in 0.5 units of Bag_2 (the 3rd and 4th record in the screenshot):
For the BOMs to be used, they need to be associated with the appropriate location-product combinations through production policies. So, we add 2 records to the Production Policies table, which set that at All_DCs the finished goods can be produced using the 2 BOMs. The Unit Cost set on this table represents the cost of inspecting each returned bag and deciding whether it can be reused.
With all the changes made on the input side, we can run the S1 Include Returns scenario (which was copied and renamed to “S1 Include Returns w BOM”). We will briefly look at how these changes affect the outputs.
In the Optimization Return Summary output table, users will notice that the Product Name is still either Bag_1 or Bag_2, but that the Return Product Name is either Bag_1_Return (for Bag_1) or Bag_2_Return (for Bag_2). The quantities are the same as before, since the return ratios are unchanged.
When looking at records of Flow Type = Return, we now see that the Product Name on these flows is that of the “_Return” products.
In this output table, we see that Bag_1 and Bag_2 are no longer only originating from the main DC in Cincinnati, but also at the 2 bigger local DCs that accept returns (Clovis, CA, and Jersey City, NJ) where a percentage of the returns is converted back into sellable finished goods through the BOMs.
In this appendix we will cover all fields on the 2 new input tables and the 1 new output table.
Users of the Optilogic platform can easily access all files they have in their Optilogic account and perform common tasks like opening, copying, and sharing them by using the built-in Explorer application. This application sits across all other applications on the Optilogic platform.
This documentation will walk users through how to access the Explorer, explain its folder and file structure, how to quickly find files of interest, and how to perform common actions.
By default, the Explorer is closed when users are logged into the Optilogic platform, they can open it at the top of the applications list:
Once the Explorer is open, your screen will look similar to the following screenshot:
This next screenshot shows the Explorer when it is open while the user is working inside the workspace of one of the teams they are part of, and not in their My Account workspace:
When a new user logs into their Optilogic account and opens the Explorer, they will find there are quite a few folders and files present in their account already. The next screenshot shows the expanded top-level folders:
As you may have noticed already, different file types can be recognized by the different icons to the left of the file’s name. The following table summarizes some of the common file types users may have in their accounts, shows the icon used for these in the Explorer, and indicates which application the file will be opened in when (left-)clicking on the file:
*When clicking on files of these types, the Lightning Editor application will be opened and a message stating that the file is potentially unsupported will be displayed. Users can click on a “Load Anyway” button to attempt to load the file in the Lightning Editor. If the user chooses to do so, the file will be loaded, but the result will usually be unintelligible for these file types.
Some file types can be opened in other applications on the Optilogic platform too. These options are available from the right-click context menus, see the “Right-click Context Menus” section further below.
Icons to the right of names of Cosmic Frog models in the Explorer indicate if the model is a shared one and if so, what type of access the user / team has to it. Hovering over these icons will show text describing the type of share too.
Learn more about sharing models and the details of read-write vs read-only access in the “Model Sharing & Backups for Multi-user Collaboration in Cosmic Frog” help center article.
While working on the Optilogic platform, additional files and folders can be created in / added to a user’s account. In this section we will discuss which applications create what types of files and where in the folder structure they can be found in the Explorer.
The Resource Library on the Optilogic platform contains example Cosmic Frog models, Cosmic Frog for Excel Apps, Python scripts, reference data, utilities, and additional tools to help make Optilogic platform users successful. Users can browse the Resource Library and copy content from there to their own account to explore further (see the “How to use the Resource Library” help center article for more details):
Please note that previously, files copied from the Resource Library were placed in a different location in users’ accounts and not in the Resource Library folder and its subfolders. The old location was a subfolder with the resource’s name under the My Files folder. Users who have been using the Optilogic platform for a while will likely still see this file structure for files copied from the Resource Library before this change was made.
Users can create new Cosmic Frog models from Cosmic Frog’s start page (see this help center article); these will be placed in a subfolder named “Cosmic Frog Models”, which sits under the My Files folder:
DataStar users may upload files to use with their data connections through the DataStar application (see this help center article). These uploaded files are then placed in a subfolder named DataStar, which sits under the My Files folder:
When working with any of the Cosmic Frog for Excel Apps (see also this help center article), the working files for these will be placed in subfolders under the My Files folder. These are named “z Working Folder for … App”:
In addition to the above-mentioned subfolders (Resource Library, Cosmic Frog Models, DataStar, and “z Working Folder for … App” folders) which are often present under the My Files top-level folder in a user’s Optilogic account, there are several other folders worth covering here:
Now that we have covered the folder and file structure of the Explorer including the default and common files and folders users may find here, it is time to cover how users can quickly find what they need using the options towards the top of the Explorer application.
There is a free type text search box at the top of the Explorer application, which users can use to quickly find files and folders that contain the typed text in their names:
There is a quick search option to find all Cosmic Frog models in the user’s account:
Users can create share links for folders in their Optilogic account to send a copy of the folder and all its contents to other users. See this “Folder Sharing” section in the “Model Sharing & Backups for Multi-User Collaboration in Cosmic Frog” help center article on how to create and use share links. If a user has created any share links for folders in their account, these can be managed by clicking on the View Share Links icon, which is the second of the 5 to the right of / underneath the search box:
When browsing through the files and folders in your Optilogic account, you may collapse and expand quite a few different folders and their subfolders. Users can at times lose track of where the file they had selected is located. To help with this, users have the “Expand to Selected File” option available to them:
In addition to using the Expand to Selected File option, please note that switching to another file in the Lightning Editor by for example clicking on the Periods.csv file will further expand the Explorer to show that file in the list too. If needed, the Explorer will also automatically scroll up or down to show the active file in the center of the list.
If you have many folders and subfolders expanded, it can be tedious to collapse them all one by one again. Therefore, users also have a “Collapse All” option at their disposal when working with the Explorer. The following screenshot shows the state of the Explorer before clicking on the Collapse All icon, which is the 4th of the 5 icons to the right of / underneath the Search box:
The user then clicks on the Collapse All icon and the following screenshot shows the state of the Explorer after doing so:
Note that the Collapse All icon has now become inactive and will remain so until any folders are expanded again.
Sometimes when deleting, copying, or adding files or folders to a user’s account, these changes may not be immediately reflected in the Explorer files & folders list as they may take a bit of time. The last one of the 5 icons to the right of / underneath the Search box provides users a “Refresh Files” option. Clicking on this icon will update the files and folders list such that all the latest are showing in the Explorer:
In this final section of the Explorer documentation, we will cover the options users have from the context menus that come up when right-clicking on files and folders in the Explorer. Screenshots and text will explain the options in the context menus for folders, Cosmic Frog models, text-based files, and all other files.
When right-clicking on a folder in the Explorer, users will see the following context menu come up (here the user right-clicked on the Model Library folder):
The options from this context menu are, from top to bottom:
Note that right-clicking on the Get Started Here folder gives fewer options: just the Copy (with the same 3 options as above), Share Link, and Delete Folder options are available for this folder.
Now, we will cover the options available from the context menu when right-clicking on different types of files, starting with Cosmic Frog models:
The options, from top to bottom, are:
Please note that the Cosmic Frog models listed in the Explorer are not actual databases, but pointer files. These are essentially empty placeholder files to let users visualize and interact with models inside the Explorer. Due to this, actions like downloading are not possible; working directly with the Cosmic Frog model databases can be done through Cosmic Frog or the SQL Editor.
When right-clicking on a Python script file, the following context menu will open:
The options, from top to bottom, are:
The next 2 screenshots show what it looks like when comparing 2 text-based files with each other:
Other text-based files, such as those with extensions of .csv, .txt, .md and .html have the same options in their context menus as those for Python script files, with the exception that they do not have a Run Module option. The next screenshot shows the context menu that comes up when right-clicking on a .txt file:
Other files, such as those with extensions of .pdf, .xls, .xlsx, .xlsm, .png, .jpg, .twb and .yxmd, have the same options from their context menus as Python scripts, minus the Compare and Run Module options. The following screenshot shows the context menu of a .pdf file:
As always, please feel free to let us know of any questions or feedback by contacting Optilogic support on support@optilogic.com.
User-defined variables (UDVs) are a transformative feature in Cosmic Frog’s transportation optimization algorithm (Hopper engine) that allow users to create and track custom metrics specific to their transportation needs. Once established, these variables can be seamlessly integrated into user-defined constraints and/or user-defined costs. Several example use cases are:
Before diving into Hopper’s user-defined variables, costs, and constraints, it is recommended users are familiar with the basics of building and running a Hopper model, see for example this “Getting Started with Hopper” help center article.
In this documentation, we will first describe the example model used to illustrate the UDV concepts in this help article. Next, we will cover the input and output tables available when working with user-defined variables, costs, and constraints. Finally, we will walk through the inputs and outputs of 4 UDV examples: the first two examples showcase the application of constraints to user-defined variables, while the last two examples cover how to model user-defined costs.
The characteristics of the model used to show the concepts of user-defined variables, costs, and constraints throughout this help article are as follows:
The optimized routes from the Baseline_UDV scenario are shown on this map, there are 10 routes with 10 stops each. The customers are color-coded based on the country they are in:
Filtering out the route which has stops in most countries, we find the following route which has stops on it in 4 countries Poland (1 dark blue stop), Czech Republic (7 yellow stops), Slovakia (1 orange stop), and Germany (1 red stop):
In the Input Tables part of Cosmic Frog’s Data module, there are 3 input tables in the Constraints section that can be used to configure user-defined variables, costs, and constraints:
We will take a closer look at each of these input tables now, and will also see more screenshots of these in the later sections that walk through several examples.
On this table we specify the term(s) of each variable which we wish to track or apply user-defined costs and/or constraints to. This first screenshot shows the fields which are used to define the variable, its term(s), and what the return condition is:
The next 2 screenshots show the other fields available on the Transportation User-Defined Variables input table, which are used to set the Filter Condition for the Scope. Note that several of these fields have accompanying Group Behavior fields, which are not shown in the screenshot. If a group name is used in the Asset Name, Site Name, Shipment ID, or Product Name field, the Group Behavior field specifies how the group should be interpreted: if the Group Behavior field is set to Aggregate (the default if not specified) the activity of the variable is summed over the members of the group, i.e. the variable is applied to the members of the group together. If the Group Behavior field is set to Enumerate, then an instance of the variable will be created for each member of the group individually.
Consider a route which picks up 4 shipments, Shipment #1, #2, #3, and #4, and delivers them to 3 stops on a route as shown in the following diagram. In all 3 examples that follow, the filter condition is set to Shipment ID = Shipment #3 and Site Type = Delivery. This first example shows what will be returned for the variable when Scope = Shipment and Type = Quantity:
The whole route is filtered for Delivery of Shipment #3 and we see that it is delivered to the Delivery 2 stop. Since Scope = Shipment and Type = Quantity, the resulting variable value is the quantity of this shipment, which is what the yellow outline indicates.
In the next example, we look at the same route and same filtering condition (Shipment #3, Delivery), but now Scope has been changed to Stop (Type is still Quantity):
Again, we filter the route for Delivery of Shipment #3 and we see that it is delivered to the Delivery 2 stop. Since Scope = Stop, now the variable value is the total quantity delivered to the stop (outlined in yellow again): quantity Shipment #2 + quantity Shipment #3.
The final visual example is for when the Scope is now changed to Route, while keeping all the other settings the same:
The route is again filtered for Delivery of Shipment #3, since the delivery of this shipment is on this route, we now calculate variable value as the total quantity of the route: quantity Shipment #1 + quantity Shipment #2 + quantity Shipment #3 + quantity Shipment #4, again outlined in yellow.
Next, we will also walk through a numbers example for different combinations of Scope and Type to see how these affect the calculation of the value of a variable’s term. Consider a route with 5 stops as follows:
We will calculate the value of the following 15 variables where the Scope, Type, and Product Name to filter for are set to different values. Note that all variables have just 1 term with coefficient 1, so the variable value = scaled term value.
If wanting to apply constraints to user-defined variables, this can be set up on the User-Defined Constraints input table:
To apply costs to a user-defined variable, this can be achieved by utilizing the User-Defined Costs input table:
There are 3 output tables related to user-defined costs and constraints:
We will cover each of these now and will see more screenshots of them in the sections that follow where we will walk through several example use cases.
This table lists the values of the terms of each user-defined variable. This next screenshot shows the values of the “ProductFlag” term of the “NumberOfProductsInRoute” variable for the routes of the Baseline_UDV scenario. How this variable and its term were set up can be seen in the screenshot of the transportation user-defined variables table above (Scope = Route, Type = Product Count, Coefficient = 1).
When setting up the Number Of Products In Route variable like above and not applying costs or constraints to it, it functions as a tracker so that user can easily get at this data rather than having to manipulate the transportation optimization output tables to calculate the number of products per route.
If we run a scenario “MaxOneProductPerRoute” where we include the maximum 1 product per route constraint that we have seen in the screenshot in the section further above on the User-Defined Constraints input table, the outputs in this table change as follows:
This table is a roll up to the variable level of the Optimization User-Defined Variable Term Summary output table discussed in the previous section. All the scaled terms of each variable have been added up to arrive at the variable’s value:
If costs have been applied to a user-defined variable, the results of that can be seen in this output table:
In this first example, we will see how we can track and limit the number of countries per route. For this purpose, a variable with 5 terms is set up in the Transportation User-Defined Variables table. Each term counts if a route has any stops in 1 of the 5 countries used in the model, 1 variable term for each country. Then we will apply constraints to this variable that limit the number of countries on each route to either 1 or 2. Let’s start with looking at the variable and its 5 terms in the Transportation User-Defined Variables table:
Next, we can add constraints that apply to this variable to change the behavior in the model and limit the number of countries a route is allowed to make stops in on any given route. We use the User-Defined Constraints table for this:
After running the Baseline_UDV scenario which does not include these constraints, we can have a look at the Optimization User-Defined Variable Summary output table:
We see that 3 routes make stops in just 1 country, 5 routes in 2 countries, and 1 route (route 9) makes stops in 4 countries when leaving the number of countries a route is allowed to make stops in unconstrained.
Now we want to see the impact of applying the Max One Country and Max Two Countries constraints through 2 scenarios and again we check the Optimization User-Defined Variable Summary output table after running these scenarios:
Maps are also helpful to visualize these outputs. As we saw in the introduction of the example model used throughout this documentation, these are the Baseline_UDV routes visualized on a map:
These routes change as follows in the MaxOneCountryPerRoute scenario:
Since some of these routes overlap on the map, let us filter a few out and color-code them based on the country to more easily see that indeed the routes each only make stops in 1 country:
In this example we will see how user-defined variables and constraints can be used to model truck compartments and their capacities. First, we set up 3 variables that track the amount of ambient, refrigerated, and frozen product on a route:
Without setting up any constraints that apply to these variables, they just track how much of each product is on a route, which can be within or over the actual compartment capacity. So, to set capacity limits, we can use the User-Defined Constraints table to setup constraints on these 3 variables that represent the capacity of the ambient, refrigerated, and frozen compartments of a truck:
After running the Baseline_UDV scenario where these constraints are not applied and another scenario, Compartment Capacity, where they are applied, we can take a look at the Optimization User-Defined Variable Summary output table to see the effect of the constraints (just showing routes 1 and 2 in the below screenshot):
Typically, when adding constraints, we expect routes to change – more routes may be needed to adhere to the constraints, and they may become less efficient. Overall, we would expect costs, distance, and time to increase. This is exactly what we see when comparing these outputs in the Transportation Summary output table for these 2 scenarios:
We have seen 2 examples of applying constraints to user-defined variables in the previous sections. Now, we will walk through 2 examples of applying costs to user-defined variables. The first example shows how to apply a variable cost based on how long a shipment sits on a route: we will specify a cost of $1 per hour the shipment spends on the route. First, we set up a variable that tracks how long a shipment spends on a route in the Transportation User-Defined Variables input table:
Next, the User-Defined Costs table is used to specify the cost of $1 per hour:
After running the CostPerShipmentTimeInTruck scenario which includes this user-defined cost, we can look at both the Transportation Shipment Summary and the Optimization User-Defined Cost Summary output tables to see this cost of $1 per hour has been applied:
Next, we open the Optimization User-Defined Cost Summary output table and filter for the same scenario and route (#4):
In our final example of this documentation, we will use the same variable ShipmentTimeInTruck from the previous example to set up a different type of cost. We will use it to find any shipments that are on a route for more than 10 hours and apply a penalty cost of $100 to each. This involves using a step cost for which we will also need to utilize the Step Costs table; we will start with looking at this table:
Next, we configure the penalty cost in the User-Defined Costs table:
After running a scenario in which we include the penalty cost, we can again look at the Transportation Shipment Summary and Optimization User-Defined Cost Summary output tables to see this cost in action:
Teams is an exciting new feature set on the Optilogic platform designed to enhance collaboration within Supply Chain Design, enabling companies to foster a more connected and efficient working environment. With Teams, users can join a shared workspace where all team members have seamless access to collective models and files. For a more elaborate introduction to and high-level overview of the Teams feature set, please see this “Getting Started with Optilogic Teams” help center article.
This guide will walk Administrators through the steps to set up their organization and create Teams within the Optilogic platform. For non-administrator users, there is also an “Optilogic Teams – User Guide” help center article available.
To begin, reach out to Optilogic Support at support@optilogic.com and let them know you would like to create your company’s Organization. Once they respond, they will ask you two key questions:
These questions help us determine who should have access to the Organization Dashboard, where organization administrators (“Org Admins”) can manage users, create Teams, invite Members, and more. Specifying your company’s domains also enables us to pre-populate a list of potential users—saving you time by not having to invite each colleague individually.
Once this information is confirmed, our development team will create your organization. When complete, you will be able to log in and begin using the Teams functionality.
If you have been assigned as an Organization Administrator, you can access the Organization Dashboard from the dropdown menu under your username in the top-right corner of the Optilogic platform. Click your name, then select Teams Admin from the list:
This will take you to your Organization Dashboard, where you can manage Teams and their Members.
We will first look at the Teams application within the Organization Dashboard. Here, all the organization’s teams are listed and can be managed. It will look similar to the following screenshot:
In List View format, the Teams application looks as follows and the same sections of the team edit form mentioned in the above bullets can be opened by clicking on different parts of the team’s record in the list:
In the Members application, all the organization’s members are listed, and they can be managed here:
The following diagram gives an overview of the different roles users can have when using Optilogic Teams:
From the Organization Dashboard, while in the Teams application, click the Create Team button (as seen in the screenshots in the “Teams Application for Admins” section above) to start building a new team. The Create New Team form will come up:
Once a new team is created, members will gain access to the team. If it is their first team, a new application called Team Hub will appear in their list of applications on the Optilogic platform:
Learn how to use the Team Hub application and about switching between teams and your own My Account in the “Optilogic Teams – User Guide”.
Org Admins can change existing teams by clicking on them in the Teams application while in the Organization Dashboard. Depending on where you click on the team’s card, one of 4 sections of the Edit Team form will be shown, as was also mentioned in the “Teams Application for Org Admins” section further above. When clicking on the name of the Team, the General section is shown:
The following screenshot shows the confirmation message that comes up in case an Org Admin clicks on the Delete Team button. If they want to go ahead with the removal of the team, they can click on the Delete button. Otherwise, the Cancel button can be used to not delete the Team at this time.
The second section in the Edit Team form concerns the members of the team:
In the third section of the Edit Team form the team’s appearance can be edited:
The fourth and last part of the Edit Team form is the Invites section:
Org Admins can add new users to the organization and/or to teams by clicking on the Invite Users button while in the Members application on the Organization Dashboard. The top part of the form that comes up (next screenshot), will be used to for example add a contractor who will help out your organization for an extended period of time – they become part of the organization and can be added to multiple teams:
In the second part of this form, people can be invited to a specific team without adding them to the overall organization; these are called Team-only users:
When someone has been emailed an invite to join a team, the email will look similar to the one in the following screenshot:
User can click on the “Click here” link to accept the invite. More on the next steps for a user to join a team can be found in the “Optilogic Teams – User Guide” help center article.
Roles of existing organization members and the teams they are part of can be updated by clicking on the team member in the list of Members:
In the Teams section of this form Org Admins can update which team(s) the member is part of and what role they have in those teams:
For Team-only members (people who are part of 1 or multiple specific teams, but who are not part of the Organization), a third section named “Invites” will be available on this form:
As a best practice, it is recommended to perform regular housekeeping (for example weekly) on your organization’s teams and their members, and your organization’s members. This will prevent situations like a previous employee or temporary consultant still having access to sensitive team contents.
A user with an Org Admin role can also be part of any of the organization’s teams and work inside those or their own My Account workspace. To leave the Organization Dashboard and get back to the Optilogic platform and its applications, they can click on their name at the right top of the organization dashboard and choose “Open Optilogic Platform” from the list:
Here the Admin user can start using the Team Hub application and work collaboratively in teams, the same way as other non-Admin users do. The “Optilogic Teams – User Guide” help center article documents this in more detail.
Once you have set up your teams and added content, you are ready to start collaborating and unlocking the full potential of Teams within Optilogic!
Let us know if you need help along the way—our support team (support@optilogic.com) has your back.
We take data protection seriously. Below is an overview of how backups work within our platform, including what’s included, how often backups occur, and how long they’re kept.
Every backup—whether created automatically or manually—contains a complete snapshot of your database at the time of the backup. This includes everything needed to fully restore your data.
We support two types of backups at the database level:
Often called “snapshots,” “checkpoints,” or “versions” by users:
We use a rolling retention policy that balances data protection with storage efficiency. Here’s how it works:
Retention Tier - Time Period - What’s Retained
Short-Term - Days 1–4 - Always keep the 4 most recent backups
Weekly - Days 5–7 - Keep 1 additional backup
Bi-Weekly - Days 8–14 - Keep the newest and oldest backups
Monthly - Days 15–30 - Keep the newest and oldest backups
Long-Term - Day 31+ - Keep the newest and oldest backups
This approach ensures both recent and historical backups are available, while preventing excessive storage use.
In addition to per-database backups, we also perform server-level backups:
These backups are designed for full-server recovery in extreme scenarios, while database-level backups offer more precise restore options.
To help you get the most from your backup options, we recommend the following:
If you have additional questions about backups or retention policies, please contact our support team at support@optilogic.com.
Teams is an exciting new feature set on the Optilogic platform designed to enhance collaboration within Supply Chain Design, enabling companies to foster a more connected and efficient working environment. With Teams, users can join a shared workspace where all team members have seamless access to collective models and files. For a more elaborate introduction to and high-level overview of the Teams feature set, please see this “Getting Started with Teams” help center article.
This guide will cover how to use and take advantage of the Teams functionality on the Optilogic Platform.
For organization administrators (Org Admins), there is an “Optilogic Teams – Administrator Guide” help center article available. The Admin guide details how Org Admins can create new Teams & change existing ones, and how they can add new Members and update existing ones.
When your organization decides to start using the Teams functionality on the Optilogic platform, they will appoint one or multiple users to be the organizations’s administrators (Org Admin) who will create the Teams and add Members to these teams. Once an Org Admin has added you to a team, you will see a new application called Team Hub when logged in on the Optilogic platform. You will also receive a notification on the Optilogic platform about having been added to a team:
Note that it is possible to invite people from outside an organization to join one of your organization’s teams. Think for example of granting access to a contractor who is temporarily working on a specific project that involves modelling in Cosmic Frog. An Org Admin can invite this person to a specific team, see the “Optilogic Teams – Administrator Guide” help center article on how to do this. If someone is invited to join a team, and they are not part of that organization, they will receive an email invitation to the team. The following screenshots show this from the perspective of the user who is being invited to join a team of an organization they are not part of.
The user will receive an email similar to the one shown below. In this case the user is invited to the “Onboarding” team.
Clicking on the “Click here” link will open a new browser tab where user can confirm to join the team they are invited to by clicking on the Join Team button:
After clicking on the Join Team button, user will be prompted to login to the Optilogic platform or to create an account if they do not have one already. Once logged in, they are part of the team they were invited to and they will see the Team Hub application (see next section).
They will also see a notification in their Optilogic account:
Clicking on the notifications bell icon at the top right of the Optilogic platform will open the notifications list. There will be an entry for the invite the user received to join the Onboarding team.
Should an Org Admin have deleted the invitation before the user accepts the invite, they will get the message “Failed to activate the invite” when clicking on the Join Team button:
The Team Hub is a centralized workspace where users can view and switch between the teams they belong to. At its core, Team Hub provides team members with a streamlined view of their team’s activity, resources, and members. When first opening the Team Hub application, it may look similar to the following screenshot:
Next, we will have a look at the team card of the Cosmic Frog Team:
Note that changing the appearance of a team changes it not just for you, but for all members of the team.
When clicking on a team or My Account in the Team Hub, user will be switching into that team and all the content will be that of the team. See also the next section “Content Switching with Team Hub” where this is explained in more detail. When switching between teams or My Account, first the resources of the team you are switching to will be loaded:
Once all resources are loaded, user can click on the Close button at the bottom or wait until it automatically closes after a few seconds. We will first have a look at what the Team Hub looks like for My Account, the user’s personal account, and after that also cover the Team Hub contents of a team.
The overview of a team in the Team Hub application can look similar to following screenshot:
Note that as a best practice, users can start using the team’s activity feed instead of written / verbal updates from team members to understand the details of who worked on what when.
One of the most important features of the Team Hub application is its role as a content switcher. By default, when you log into the Optilogic platform, you’ll see only your personal content (My Account)—similar to a private workspace or OneDrive.
However, once you enter Team Hub and select a specific team, the Explorer automatically updates to display all files and databases associated with that team. This team context extends across the entire Optilogic platform. For example, if you navigate to the Run Manager, you’ll only see job runs associated with the selected team.
By switching into a team, all applications and data within the platform are scoped to that team. We will illustrate this with the following screenshots where user has switched to the team named “Cosmic Frog Team”.
Besides the “Cosmic Frog Team” team, this user is also part of the Onboarding team, which they have now switched to using the Team Hub application. Next, they open Resource Library application:
Note that it is best practice to return to your personal space in My Account when finished working in a Team, to ensure workspace content is kept separate and files are not accidentally created in/added to the wrong team.
Once an organization and its teams are set up, the next step is to start populating your teams with content. Besides adding content by copying from the Resource Library as seen in the last screenshot above, there are two primary ways to add models or files to a team.
Navigate to the Team Hub and switch into your team space. From here, you can create new files, upload existing ones, or begin building new models directly within the team. Keep in mind that any files or models created within a team are visible to all team members and can be modified by them. If you have content that you would prefer not to be accessed or edited by others, we recommend either labeling it clearly or creating it within your personal My Account workspace.
When user is in a specific team (Cosmic Frog Team here), they can add content through the Explorer (expand by clicking on the greater than icon at the top left on the Optilogic Platform): right clicking in the Explorer brings up a context menu with options to create new files, folders, and Cosmic Frog Models, and to upload files. When using these options, these are all created in / added to the active team.
You can also quickly add content to your team by using Enhanced Sharing. This feature allows you to easily select entire teams or individual team members to share content with. When you open the share modal and click into the form, you’ll see intelligent suggestions—teams you belong to and members from your organization—appear automatically. Simply click on the teams or users listed to autofill the form. To learn more about the different ways of sharing content and content ownership, please see the “Model Sharing & Backups for Multi-User Collaboration” help center article.
Please note that, regardless of how a team’s content has been created/added:
Once you have been added to any teams and have added content, you are ready to start collaborating and unlocking the full potential of Teams within Optilogic!
Let us know if you need help along the way—our support team (support@optilogic.com) has your back.
With Optilogic’s new Teams feature set (see the "Getting Started with Optilogic Teams" help center article) working collaboratively on Cosmic Frog models has never been easier: all members of a team have access to all contents added to that team’s workspace. Centralizing data using Teams ensures there is a single source of truth for files/models which prevents version conflicts. It also enables real-time collaboration where files/models are seamlessly shared across all team members, and updates to any files/models are instantaneous for all team members.
However, whether your organization uses Teams or not, there can be a need to share Cosmic Frog models, for example to:
In this documentation we will cover how to share models, and the different options for sharing. Sharing models can be from an individual user or a team to an individual user or a team. As the risk of something undesirable happening with the model when multiple people work on it increases, it is important to be able to go back to a previous version of the model. Therefore, it is best practice to make a backup of a model prior to sharing it. Continue making backups when important/major changes are going to be made or when wanting to try out something new. How to make a backup of a model will be explained in this documentation too and will be covered first.
A backup of a model is a snapshot of its exact state at a certain point in time. Once a backup has been made, users can use them to revert to if needed. Initiating the creation of a backup of a Cosmic Frog model can be done from 3 locations within the Optilogic platform: 1) from the Models module within Cosmic Frog, 2) through the Explorer and 3) from within the Cloud Storage application on the Optilogic platform. The option from within Cosmic Frog will be covered first:
When in the Models module of Cosmic Frog (its start page), hover over the model you want to create a backup for, and click on the 5th icon that comes up at the bottom right of the model card.
From the Cloud Storage application it works as follows:
Through the Explorer, the process is similar:
Whether from the Models module within Cosmic Frog, through the Cloud Storage application or via the Explorer, in all 3 cases the Create Backup form comes up:
After clicking on Confirm, a notification at the top of the user’s screen will pop up saying that the creation of a backup has been started:
At the same time, a locked database icon with hover over text of “Backup in progress…” appears in the Status field of the model database (this is in the Cloud Storage application’s list of databases):
This locked database icon will disappear again once the backup is complete.
Users can check progress of the backup by going to the user’s Account menu under their username at the right top of the screen and selecting “Account Activity” from the drop-down menu:
To access any backups, users can expand individual model databases in the Cloud Storage application:
There are 2 more columns in the list of databases that are not shown in the screenshot above:
When choosing to restore a backup, the following form comes up:
Now that we have discussed how models can be backed up, we will cover how models can be shared. Note that it is best practice to make a backup of your model before sharing it.
If your organization uses Teams, first make sure you are in the correct workspace, either a Team’s or your personal My Account area, from which you want to share a model. You can switch between workspaces using the Team Hub application, which is explained in this "Optilogic Teams - User Guide" help center article.
Like making a backup of a model database, sharing a model can also be done through the Cloud Storage application and the Explorer. Starting with the Cloud Storage option:
The Share Model options can also be accessed through the Explorer:
Now we will cover the steps of sending a copy of a model to another user or team. The original and the copy are not connected to each other after the model was shared in this way: updates to one are not reflected in the other and vice versa.
After clicking on the Send Model Copy button, a message that says “Model Copy Sent Successfully” will be displayed in the Send Model Copy form. Users can go ahead and send copies of other models to other user(s)/teams(s) or close out of the form by clicking on the cross icon at the right top of the form.
In this example, a copy of the CarAssembly model was sent to the Onboarding team. In the Onboarding team’s workspace this model will then appear in the Explorer:
Next, we will step through transferring ownership of a model to another user or team. The original owner will no longer have access to the model after transferring ownership. In the example here, the Onboarding team will transfer ownership of the Tariffs model to an individual user.
After clicking on the Transfer Model Ownership button, a message that says “Transferred Ownership Successfully” will be displayed in the Transfer Model Ownership form. Users can go ahead and transfer ownership of other models to other user(s)/teams(s) or close out of the form by clicking on the cross icon at the right top of the form.
There will be a notification of the model ownership transfer in the workspace of the user/team that performed the transfer:
The model now becomes visible in the My Account workspace of the individual user the ownership of the model was transferred to:
Lastly, we will show the steps of sharing access to a model with a user or team. Note that Sharing Access to a model can be done from Explorer and from the Cloud Storage application (same as for the Send Copy and Transfer Ownership options), but can also be done from the Models module in Cosmic Frog:
In Cosmic Frog's Models module, hover over the model card of the model you wans to share access to and then click on the 4th icon that comes up in the bottom right of the model card.
In our walk-through example, an individual user will share access to a model called "Fleet Size Optimization - EMEA Geo" with the Onboarding team.
After the plus button was clicked to share access of the Fleet Size Optimization - EMEA Geo model with the Onboarding team, this team is now listed in the People with access list:
Now, in the Onboarding team’s workspace, we can access this model, of which the team receives a notification too:
Now that the Onboarding team has access to this model, they can share it with other users/teams too: they can either send a copy of it or share access, but they cannot transfer ownership as they are not the model’s owner.
In the Explorer of the workspace of the user/team who shared access to the model, a similar round icon with arrow inside it will be shown next to the model’s name. The icon colors are just inverted (blue arrow in white circle) and here the hover text is “You have shared this database”, see the screenshot below. There will also be a notification about having granted access to this model and to whom (not shown in the screenshot):
If the model owner decides to revoke access or change the permission level to a shared model, they need to open the Share Model Access form again by choosing Share Access from the Share Model options / clicking on the Share icon when hovering over the model's card on the Cosmic Frog start page:
If access to a model is revoked, the team/user that was previously granted access but now no longer will have access, receives a notification about this:
With Read-Only access, teammates and stakeholders can explore a shared model, view maps, dashboards, and analytics, and provide feedback — all while ensuring that the data remains unchanged and secure.
Read-Only mode is best suited for situations where protecting data integrity is a priority, for example:
See the Appendix for a complete list of actions and whether they are allowed in Read-Only Access mode or not.
Similar to revoking access to a previously shared model, in order to change the permission level of a shared model, user opens the Share Model Access form again by choosing Share Access from the Share Model options / clicking on the Share icon when hovering over the model's card on the Cosmic Frog start page:
Models with Read-Only access can be recognized on the Optilogic platform as follows:
Input tables of Read-Only Cosmic Frog models are greyed out (like output tables already are by default), and and write actions (insert, delete, modify) are disabled:
Read-Only models can be recognized as follows in other Optilogic applications:
When working with models that have shared access, please keep following in mind:
In addition to the various ways model files can be shared between users, there is a way to share a copy of all contents of a folder with another user/team too:
After clicking on the Create Share Link button, the share link is copied to the clipboard. A toast notification of this is temporarily displayed at the right top in the Optilogic platform. The user can paste the link and send it to the user(s) they want to share the contents of the folder with.
When a user who has received the share link copies it into their browser while logged into the Optilogic platform, the following form will be opened:
Folders copied using the share link option will be copied into a subfolder of the Sent To Me folder. The name of this subfolder will be the username / email of the user / team that sent the share link. The file structure of the sent folder will be maintained and appear the same as it was in the account of the sender of the share link.
See the View Share Links section in the Getting Started with the Explorer help center article on how to manage your share links.
Action - Allowed? - Notes:
Optilogic introduces the Lumina Tariff Optimizer – a powerful optimization engine that empowers companies to reoptimize supply chains in real-time to reduce the effects of tariffs. It provides instant clarity on today’s evolving tariff landscape, uncovers supply chain impacts, and recommends actions to stay ahead – now and into the future.
Manufacturers, distributors, and retailers around the world are faced with an enormous task trying to keep up with changing tariff policies and their supply chain impact. With Optilogic’s Lumina Tariff Optimizer, companies can illuminate their path forward by proactively designing tariff mitigation strategies that automatically consider the latest tariff rates.
With Lumina Tariff Optimizer, Optilogic users can stay ahead of tariff policy and answer critical questions to take swift action:
The following 7-minute video gives a great overview of the Lumina Tariff Optimizer tools:
Optilogic’s Lumina Tariff Optimization engine can be leveraged by modelers within Cosmic Frog or be leveraged within a Cosmic Frog for Excel app for other stakeholders across the business to evaluate the tariff impact to their end-to-end supply chain. Optilogic enables users to get started quickly with Lumina with several items in the Resource Library that include:
This documentation will cover each of these Lumina Tariff Optimizer tools, in the same order as listed above.
The first tool in the Lumina Tariff Optimizer toolset is the Tariffs example model which users can copy to their own account from the Resource Library. We will walk through this model, covering inputs and outputs, with emphasis on how to specify tariffs and their impact on the optimal solution when running network optimization (using the Neo engine) on the scenarios in the model.
Let us start by looking at the map of the Tariffs model, which is showing the model locations and flows for the Baseline scenario:
This model consists of the following sites:
Next, we will have a look at the Products table:
As mentioned above, raw materials RM1, RM2, and RM3 are supplied by Chinese suppliers and the others 6 raw materials by European suppliers, which we can confirm by looking at the Supplier Capabilities input table:
The Bills Of Materials input table shows that each finished good takes 3 of the Raw Materials to be manufactured; the Quantity field indicates how much of each is needed to create 1 unit of finished good:
Looking at the Production Policies input table, we see that both the US and Mexico factory can produce Consumables, but Rockets are only manufactured in Mexico and Space Suits only in the US:
To understand the outputs later, we also need to briefly cover the Flow Constraints input table, which shows that the El Bajio Factory in Mexico can at a maximum ship out 3.5M units of finished goods (over all products and the model horizon together):
To enter tariffs and take them into account in a network optimization (Neo) run, users need to populate the new Tariffs input table:
There are also 2 new Neo output tables that will be populated when tariffs are included in the model, the Optimization Path Flow Summary and the Optimization Tariff Summary tables:
Tariffs can be specified at multiple levels in Cosmic Frog, so users can choose the one that fits their modeling needs and available data best:
In order to model tariffs from/to a region or country, these fields need to be populated in the Customers, Facilities, and Suppliers tables:
In the Tariffs input table, all path origin location (furthest upstream) – path destination location (furthest downstream) – product combinations to which tariffs need to be applied are captured. There can be any number of echelons in between the path origin location and path destination location where the product flows through. Consider the following path that a raw material takes:
The raw material is manufactured/supplied from China (the path origin), it then flows through a location in Vietnam, then through a location in Mexico, before ending its path in the USA (the path destination, where it is consumed when manufacturing a finished good). In this case the tariff that is set up for this raw material with path origin = China, and path destination = USA will be applied. The tariff will be applied to the segment of the path where the product arrives in the region / country of its final destination. In the example here, that is on last leg (/lane / segment) of the path, e.g. on the Mexico to USA lane.
If we have a raw material that takes the same initial path, except it ends in Mexico to be consumed in a finished good, then the tariff that is set up for this raw material with path origin = China and path destination = Mexico will be applied. To continue from this example: then if this finished good manufactured in Mexico is shipped to the US and sold there, and if there is a path with a tariff set up from Mexico to USA for the finished good, then that tariff will be applied (path origin = Mexico, path destination = USA). I.e. in this last example the entire path is just the 1 segment between Mexico and USA.
So, now we will look how this can be set up in the Tariffs input table:
Please note:
Three scenarios were run in the Tariffs example model:
Now, we will look at the outputs for these 3 scenarios, first at a higher level and later on, we will dig into some details of how the tariff costs are calculated as well.
The Financials stacked bar chart in the standard Optimization Scenario Comparison dashboard in the Analytics module of Cosmic Frog can be used to compare all costs for all 3 scenarios in 1 graph:
To compare the Tariffs by path origin – path destination and product, a new “Optimization Tariffs Summary” dashboard was created. We will look at the Baseline New Tariffs scenario first, and the Optimized New Tariffs scenario next:
Note that in the Appendix it is explained how this chart can be created.
Next, we will take a closer look at some more detailed outputs. Starting with how much demand there is in the model for Rockets and Consumables, the 2 finished goods the Mexican factory in El Bajio can manufacture. The next screenshot shows the Optimization Demand Summary network optimization output table, filtered for Rockets and with a summation aggregation applied to it to show the total demand for Rockets at the bottom of the grid:
Next, we change the filter to look at the Consumables product:
In conclusion: the demand for Rockets is nearly 3.5M units and for Consumables nearly 10.5M. Rockets can only be produced in Mexico whereas Consumables can be produced by both factories. From the charts above we suspected a shift in production from US to Mexico for the Consumables finished good in the Optimized New Tariffs scenario, which we can confirm by looking at the Optimization Production Summary output table:
Since the production of Consumables requires raw materials RM1, RM2, and RM3, we expect to see the above production quantities for Consumables to be reflected in the amount of these raw materials that was moved from the suppliers in China to the US vs to Mexico. We can see this in the Optimization Flow Summary network optimization output table, which is filtered for the 2 scenarios with new tariffs, Port to Port lanes, and these 3 raw materials:
The custom Optimization Tariff Summary and Optimization Path Flow Summary output tables are automatically generated after running a network optimization on a model with a populated Tariffs table. The first of these 2 is shown in the next screenshot where we have filtered out the raw materials RM1, RM2, and RM3 again, plus also the Consumables finished good for the 2 scenarios that use the new tariffs:
Where the Optimization Tariff Summary output table summarizes the tariffs at the scenario - path origin – path destination – product level, the Optimization Path Flow Summary output table gives some more detail around the whole path, and on which segments the tariffs are applied. The next 2 screenshots show 6 records of this output table for the Tariffs example model:
For the 2 scenarios that use the new tariffs, records are filtered out for raw material RM1 where the Path Start Location represents the CN region and the Path End Location represents the MX region. These Path Start and End Locations are automatically generated based on the Path Origin Property and Value and Destination Property and Value set in the Tariffs input table. Scrolling right for these 6 records:
We see that the path for RM1 is the same in both scenarios: originate at location Guangzhou in China, moved to Shanghai Port (CN), from Shanghai Port moved to Altamira Port (MX), and from Altamira Port moved to the El Bajio Factory (MX). The calculations of the Tariff Cost based on the Flow Quantity are the same as explained above, and we see that the tariffs are applied on the second segment where the product arrives in the region / country of its final destination.
Wondering where to go from here? If you are wanting to start using tariffs in your own models, but are not exactly sure where to start, please see the “Cosmic Frog Utilities to Create the Tariffs Table” section further below, which also includes step-by-step instructions based on what data you have available.
In the next section, we will first discuss how quick sensitivity analyses around tariffs can be run using a Cosmic Frog for Excel App.
To enable Cosmic Frog users, and also managers and executives with no or limited knowledge of Cosmic Frog, to run quick sensitivity scenarios around changing tariffs, Optilogic has developed an Excel Application for this specific purpose. Users can connect to their Cosmic Frog model that contains a populated Tariffs input table and indicate which tariffs to increase/decrease by how much, run network optimization with these changed tariffs, and review the optimization tariff summary output table, all in 1 Excel workbook. Users can download this application and related files from the Resource Library.
The following represents a typical workflow when using the Tariffs Rapid Optimizer application:
For users to take advantage of the power of the Lumina Tariff Optimizer they will want to create their own network optimization model which includes a populated Tariffs input table (see also the “Tariffs Model – Tariffs Table” section earlier in this documentation). Depending on the data available to the user, populating the Tariffs input table can be a straightforward task or a difficult one in case no or little data around tariffs is known/available within the organization. Optilogic has developed 3 utilities to help users with this task. The utilities are available from within Cosmic Frog, which will be covered in this section of the documentation, and they are also available through the Cosmic Frog for Excel Tariffs Builder App, which will be covered in the next section. Here follows a short description of each utility, they will each be covered in more detail later in this section:
In Cosmic Frog, they are accessible from the Utilities module (click on the 3 horizontal bars icon at the top left in Cosmic Frog to open the Module menu drop-down and select Utilities):
The utilities are listed under System Utilities > Tariff.
The latter 2 utilities hook into Avalara APIs, and users need to use / obtain their own Avalara API keys for each to be able to use these utilities from within Cosmic Frog or the Tariffs Builder Excel App.
The following list shows the recommended steps for users with varying levels of Tariffs data available to them from least to most data available (assuming an otherwise complete Cosmic Frog model has been built):
To populate the Tariffs table with all possible path origin – path destination – product combinations, based on the contents of the Transportation Policies input table, use this first utility:
Consider a small model with 1 customer in the US, 2 facilities (1 DC and 1 factory) both in the US, 1 supplier in China, and 2 products (1 finished good and 1 component):
After running the 1 Generate Tariff Paths utility (using Region as the data to use for the path origin and path destination), the Tariffs table is generated and populated as shown in the next 2 screenshots:
All combinations for path origin region, path destination region, and product have been added to the Tariffs table. Scrolling further right, we see the remaining fields of this table:
To update the HS Code field in the Tariffs table, we can use the second utility:
Users can find the full path of a file uploaded to their Optilogic account as follows:
The file containing the product master data needs to have the same columns as shown in the next screenshot:
Note that columns B-F contain information of products that do not match the product name in Cosmic Frog as this is just an example to show how the utility works.
After running the 2 HS Code Classification utility, we see that the HS Code field in the Tariffs table is now populated:
To use the HS Code field to next look up duty rates we can use the third utility:
After running the 3 Lookup Duty Rates utility, we see that the Duty Rate field in the Tariffs table is now populated:
The raw output from the API is placed in the Duty Rate field and user needs to update this so that the field contains just a number representing the total duty rate. For the second record (US region to China region for product RM), the total duty rate is 35% (25% + 10%), and user needs to enter 35 in this field. For the third record (China region to US region for product Rockets), the duty rate is 27.5% (7.5% + 20%), and user needs to enter 27.5 in this field. For the fourth record (China region to US region for product RM), the total duty rate is 25%, and user needs to enter 25 in this field.
When running a utility in Cosmic Frog, user can track the progress in the Model Activity window:
The 3 utilities covered in the previous section to generate and populate the Tariffs input table are also made available in the Cosmic Frog for Excel Tariffs Builder App, which we will cover in this section. Users can download this application and related files from the Resource Library.
The following represents a typical workflow when using this Tariffs Builder application:
The next screenshot shows the Tariffs table after just running the Build Tariff workflow (bullet 4 in the list above):
The next screenshot shows the Product Master worksheet which contains the product information to be used by the HS Code Classification workflow, it needs to be in this format and users should enter as much product information in here as possible:
After also running the HS Code Classification and the Duty Rate Lookup workflows (bullets 6 and 7 in the list further above), we see that these fields are now also populated on the Tariffs worksheet:
We hope users feel empowered to take on the challenging task of incorporating tariffs into their optimization workflows. For any questions, please do not hesitate to contact Optilogic support on support@optilogic.com.
In this appendix we will show users how to create a stacked bar chart for each path origin – path destination pair, showing the tariff costs by product.
In the Analytics drop-down menu in the toolbar while in the Analytics module of Cosmic Frog, select New Dashboard, give it a name (e.g. Optimization Tariff Summary), then click on the blue Visualization button on the top right to create a new chart for the dashboard. In the New Visualization configuration form that comes up, type “tariff” in the Tables Search box, then check the box for the Optimization Tariff Summary table in the list, and click on Select Data.
To create the OD Path calculated field, click on the plus icon at the top right of the Fields list and select Calculated Field which brings up the Edit Calculated Field configuration window:
Cosmic Frog supports importing and exporting both CSV and Excel files directly through the application. This enables users to for example:
In this documentation we will cover how users can import and export data into and out of Cosmic Frog, and illustrate this with multiple examples.
There are 2 methods of importing Excel/CSV data into Cosmic Frog’s input tables available to users:
Pointers on how data to be imported needs to be formatted will be covered first, including some tips and call outs of specifics to keep in mind when using the upsert import method. Next, the steps to import a CSV/Excel file will be walked through step by step.
Data is mapped from CSV/Excel files based on matching column names and table names matching to the file name (CSV) or worksheet name (Excel):
Data preparation tips:
CSV vs Excel: CSV files only have 1 “worksheet”, so it can only contain data to be imported into 1 table, whereas Excel files can have multiple worksheets with data to be imported to different tables in Cosmic Frog.
Please take note of how existing records are treated when using the upsert import method to import to a table which already has some data in it:
We will illustrate these behaviors through several examples too.
Users can import 1 or multiple CSV or Excel files simultaneously, please take note of how the import will work for following situations:
Once ready to import the prepared CSV/Excel file(s), user has 2 ways of accessing the import and export methods: from the File menu in the toolbar and from the right-click context menu of an input table. It looks like this from the File menu to import a file:
And when using the right-click context menu the steps to import a file are as follows:
When using the replace import method, a confirmation message will now be shown on which user can click Import to continue the import or Cancel to abort.
Next, a file explorer window opens in which user can browse to and select the CSV/Excel file(s) to import:
Once the import starts, a status message shows at the top of the active table:
The Model Activity log will also have an entry for each import action:
User can see the results of the import by opening and inspecting the affected input table(s), and by looking at the row counts for the tables in the input tables list, outlined in green in this screenshot:
A common way to start building a new model in Cosmic Frog is to make use of the replace import method to populate multiple tables simultaneously with data from Excel or CSV files. These files have typically been prepared from ERP extracts which have been manipulated to match the Cosmic Frog table and column names. This way, users do not need to enter data manually into the Cosmic Frog input tables, which would be very laborious. Note that it can be helpful to first export empty tables from a new, empty Cosmic Frog model to have a template to start filling out (see the “Exporting to CSV/Excel Files” section further below on how to do this).
Starting with an empty new model in Cosmic Frog:
User has prepared the following Excel .xlsx file:
After importing this file into Cosmic Frog, we notice that the Customers, Facilities and Products tables now have row counts that match the number of records we had in the Excel file that was used for the import, and we can open the individual tables to see the imported records:
Consider user is modelling a sports equipment company and has populated the Products table of a Cosmic Frog model with 8 products as follows:
After working with the model for a while, the user realizes a few things:
As item number 1 will change the product names, a column that is part of the primary key of the Products table, user will need to use the replace import method to make these changes as the upsert method does not change the values of columns that are part of the primary key. Following is the .xlsx file user prepares to replace the data in the Products table with:
After importing the file using the replace method, the Products table looks like this:
We see the records are the exact same as what was contained in the Products.xlsx file that was imported, and the row count for the Products table has correctly gone up to 10 with the 2 new products added.
Continuing from the Products table in the last screenshot above, user now wants to make a few additional changes as follows:
To make these changes to the Products table, the user prepares the following Products file to be upserted to the Products table, where the green numbers in the screenshot below match the items described in the bullet point list directly above:
After using the upsert import method for this file into the Products table, it contains following records. The ones changed / added are listed at the bottom:
In the boxes outlined in green we see that all the expected changes and the insertion of the 1 new record have been made.
Let us also illustrate what will happen when files with invalid /missing data are imported. We will use the replace import method for the example here, but similar results will be seen when using the upsert method. Following screenshot shows a Products table that has been prepared in Excel, where we can see several issues already: a blank Product Name, a negative value for Unit Price, etc.
After this file is imported to the Products table using the replace method, the Products table will look as follows:
The cells that are outlined in red contain invalid values. Hovering over each cell will show a tooltip message describing the problem.
For tables with many records, it may be hard to find the fields in red outline manually. To help with this, there is a standard filter user can apply that will show all records that have 1 or multiple input data errors:
In conclusion, Cosmic Frog will let a user import invalid data, and then helps user identify the data issues with the red outlines, hover over tooltips, and the Show Input Data Errors filter.
Consider following Transportation Policies table:
There is now a change where from MFG_1 all Racket products need to be shipped by Parcel for a fixed cost of $50. User creates 2 Named Filters (see the Named Filters in Cosmic Frog help center article) in the Products table: 1 that filters out all racket products (those products that have a product name that start with FG_Racket) which is named Rackets and 1 that filters out all non-racket products (those products that do not contain racket in the product name) which is named AllExceptRackets. Next, user prepares following TransportationPolicies.csv file to upsert into the Transportation policies table with the intention to update the first 2 records in the existing table to be specific for the AllExceptRackets products and add 2 new ones for the Rackets products:
The result of using this file to upsert to the Transportation Policies table is as follows:
This example shows that users need to be mindful of which fields are part of the table’s primary key and remember that values of primary key fields cannot be changed by the upsert import method. An example workflow that will achieve the desired changes to the Transportation Policies table is as follows:
It is possible to export a single table or multiple tables (input and output tables) to CSV or Excel from Cosmic Frog. Similar to importing data from CSV/Excel, user can access the export options in 2 ways: from the File menu in the toolbar and from the context menus that come up when right-clicking on tables in the input/output/custom tables lists.
Please note:
The steps to export multiple tables to an Excel file are as follows:
Once the export starts, following message appears at the top of the active table:
Once the export is complete, the exported file can be found in the folder where user’s downloaded files are saved:
When exporting multiple tables to Excel or CSV, the downloaded file will be a .zip file with an automatically generated name based on the model’s Cosmic Frog ID. Extracting the zip-file will show an .xlsx file of the same name, which can be opened in Excel:
These are the steps to export multiple tables to CSV:
When the export starts, the same “File is exporting…” message as shown in the previous section will be showing at the top of the active table. Once the export process is finished, the exported file can again be found in the folder where user’s downloaded files are saved:
The file is again a zip-file, and it has the same name based on the model’s Cosmic Frog ID, just appended with (1), as there is already a zip-file of the same name in the Downloads folder from the previous export to Excel. Unzipping the file creates a new sub-folder of the same name in the Downloads folder:
Exporting a single table to Excel can also be done from the File menu, in the same way as multiple tables are exported to Excel, which was shown above in the “Export Multiple Tables to Excel” section. Now, we will show the second way of doing this by using the context menu that comes up when right-clicking on a table:
When the export starts, the same “File is exporting…” message as shown above will be showing at the top of the active table. Once the export process is finished, the exported file can again be found in the folder where user’s downloaded files are saved:
The name of the exported CSV file matches that of the table that was exported.
Exporting a single table to CSV can also be done from the File menu, in the same way as multiple tables are exported to CSV, which was shown above in the “Export Multiple Tables to CSV” section. Now, we will show the second way of doing this by using the context menu that comes up when right-clicking on a table:
When the export starts, the same “File is exporting…” message as shown above will be showing at the top of the active table. Once the export process is finished, the exported file can again be found in the folder where user’s downloaded files are saved:
For single tables exported to CSV, the name of the file is the same as the name of the exported table. If the Cosmic Frog table was filtered, the file name is appended with “_filtered” like it is here to remind user that only the filtered rows are contained in this exported file.
Tax systems can be complex, like for example those in Greece, Colombia, Italy, Turkey, and Brazil are considered to be among the most complex ones. It can however be important to include taxes, whether as a cost or benefit or both, in supply chain modeling as they can have a big impact on sourcing decisions and therefore overall costs. Here we will showcase an example of how Cosmic Frog’s User Defined Variables and User Defined Costs can be used to model Brazilian ICMS tax benefits and take these into account when optimizing a supply chain.
The model that is covered in this documentation is the “Brazil Tax Model Example” which was put together by Optilogic’s partner 7D Analytics. It can be downloaded from the the Resource Library. Besides the Cosmic Frog model, the Resource Library content also links to this “Cosmic Frog – BR Tax Model Video” which was also put together by 7D Analytics.
A helpful additional resource for those unfamiliar with Cosmic Frog’s user defined variables, costs, and constraints is this “How to use user defined variables” help article.
In this documentation the setup of the example model will first be briefly explained. Next, the ICMS tax in Brazil will be discussed at a high level, including a simplified example calculation. In the third section, we will cover how ICMS tax benefits can be modelled in Cosmic Frog. And finally, we will look at the impact of including these ICMS tax benefits on the flows and overall network costs.
One quick note upfront is that the screenshots of Cosmic Frog tables used throughout this help article may look different when comparing to the same model in user’s account after taking it from the Resource Library. This is due to columns having been moved or hidden and grids being filtered/sorted in specific ways to show only the most relevant information in these screenshots.
In this example model, 2 products are included: Prod_National to represent products that are made within Brazil at the MK_PousoAlegre_MG factory and Prod_Imported to represent products that are imported, which is supplied from SUP_Itajai_SC within the model, representing the seaport where imported products would arrive. There are 6 customer locations which are in the biggest cities in Brazil; their names start with CLI_. There are also 3 distribution centers (DCs): DC_Barueri_SP, DC_Contagem_MG, and DC_FeiraDeSantana_BA. Note that the 2 letter postfixes in the location names are the abbreviations of the states these locations are in. Please see the next screenshot where all model locations are shown on a map of Brazil:
The model’s horizon is all of 2024 and the 6 customers each have demand for both products, ranging from 100 to 600 units. The SUP_ location (for Prod_Imported) and MK_ location (for Prod_National) replenish the DCs with the products. Between the DCs, some transfers are allowed too. The demand at the customer locations can be fulfilled by 1, 2 or all 3 DCs, depending on the customer. The next screenshot of the Transportation Policies table (filtered for Prod_National) shows which procurement, replenishment, and customer fulfillment flows are allowed:
For the other product modelled, Prod_Imported, the same customer fulfillment, DC-DC transfer, and supply options are available, except:
In Brazil, the ICMS tax (Imposto sobre Circulaçao de Mercadorias e Serviços, or Tax on Commerce and Services) is levied by the states. It applies to movement of goods, transportation services between several states or municipalities, and telecommunication services. The rate varies and depends on the state and product.
When a company sells a product, the sales price includes ICMS, which results in an ICMS debit for the company (the company owes this to the state). Likewise, when purchasing or transferring product, the ICMS is included in what the company pays the supplier. This creates ICMS credit for the company. The difference between the ICMS debits and credits is what the company will pay as ICMS tax.
The next diagram shows an ICMS tax calculation example, where company also has a 55% tax benefit which is a discount on the ICMS it needs to pay.
In order to include ICMS tax benefits in a model, we need to be able to calculate ICMS debits and credits based on the amount of flow between locations in different states for both national and imported products. As different states and different products can have different ICMS rates, we need to define these individual flow lanes as variables and apply the appropriate rate to each. This can be done by utilizing the User Defined Variables and User Defined Costs input tables, which can be found in the “Constraints” section of the Cosmic Frog input tables, shown in the below screenshot (here user entered a search term of “userdef” to filter out these 2 tables):
In the User Defined Variables table, we will define 3 variables related to DC_Contagem_MG: one that represents the ICMS Debits, one that represents the ICMS Credits, and one that represents the ICMS Balance (= ICMS Debits – ICMS Credits) for this DC. The ICMS Debits and ICMS Credits variables have multiple terms that each represents a flow out of or a flow into the Contagem DC, respectively. Let us first look at the ICMS Debits variable:
Still looking at the same top records that define the DC_Contagem_MG|ICMS_Debit variable, but freezing the Variable Name and Term Name columns and scrolling right, we can see more of the columns in the User Defined Variables table:
Note that there are quite a few custom columns in this table (not shown in the screenshots; can be added through Grid > Table > Create Custom Column), which were used to calculate the ICMS rates outside of the model. These are helpful to keep in the model, should changes need to be made to the calculations.
Next, we will have a look at the ICMS Credit variable, which is made up of 3 terms, where each term represents a possible supply/replenishment flow into the Contagem DC:
The last step on the User Defined Variables table is to combine the ICMS Credit and ICMS Debit variables to calculate the ICMS balance:
To finalize the setup, we need to add 1 record to the User Defined Costs table, where we will specify that the company has a 55% discount (tax incentive) for the ICMS it pays relating to the Contagem DC:
As mentioned in the previous section, all records in the User Defined Variables and User Defined Costs tables have their Status set to Exclude. This way, when the Baseline scenario is run, the ICMS tax incentive is not included, and the network will be optimized just based on the costs included in the model (in this case only transportation costs). We want to include the ICMS tax incentive in a scenario and then compare the outputs with the Baseline scenario. This “IncludeDCMGTaxBenefit” scenario is set up as follows:
Next, we have a look at the second scenario item that is part of this scenario:
With the scenario set up, we run a network optimization (using the Neo engine) on both scenarios and then first look in the Optimization Network Summary output table:
Notice that the Baseline scenario as expected only contains transportation costs, while the IncludeDCMGTaxBenefits scenario also contains user defined costs, which represent the calculated ICMS tax benefit and have a negative value. So, overall, the IncludeDCMGTaxBenefit scenario has about R$ 331k lower total cost as compared to the Baseline scenario, even though the transportation costs are close to R$ 47k higher. Since the transportation costs are different between the 2 scenarios, we expect some of the flows have changed.
There are 3 network optimization output tables that contain the outputs related to User Defined Variables and Costs:
We will first discuss the Optimization User Defined Variable Term Summary output table:
The Optimization User Defined Variable Summary output table contains the outputs at the variable level (e.g. the individual terms of the variables have been aggregated):
Finally, the Optimization User Defined Cost Summary output table shows the cost based on the 55% benefit that was set:
The DC_Contagem_MG_TaxIncentive benefit is calculated from the DC_Contagem_MG|ICMS_Balance variable, where the Variable Value of R$ 686,980 is multiplied by -0.55 to arrive at the Cost value of R$ -377,839.
Now that we understand at a high level the cost impact of the ICMS tax incentive and the details of how this was calculated, let us look at more granular outputs, starting with looking at the flows between locations. Navigate to the Maps module within Cosmic Frog and open the maps named Baseline and Include DC MG Tax Benefit, which show outputs from the Baseline and IncludeDCMGTaxBenefit scenarios, respectively. The next 2 screenshots show the flows from DCs to customer locations: Baseline flows in the top screenshot and scenario “Include DC MG Tax Benefit” flows in the bottom screenshot:
We see that in the Baseline the customer in Rio de Janeiro is served by the DC in Sao Paulo. This changes in the scenario where the tax benefit is included: now the Rio de Janeiro customer is served by the Contagem DC (located close to Belo Horizonte). The other customer fulfillment flows are the same between the 2 scenarios.
This model also has 2 custom dashboards set up in the Analytics module; the 1. Scenarios Overview dashboard contains 2 graphs:
This Summary graph shows the cost buckets for each scenario as a bar chart. As discussed when looking at the Optimization Network Summary output table, the IncludeDCMGTaxBenefit scenario has an overall lower cost due to the tax benefit, which offsets the increased transportation costs as compared to the Baseline scenario.
This Site Summary bar chart shows the total outbound quantity for each DC / Factory / Supplier by scenario. We see that the outbound flow for the DC in Barueri is reduced by 500 units in the IncludeDCMGTaxBenefit scenario as compared to the Baseline scenario, whereas the Contagem DC has an increased outbound flow, from 1,000 to 2,500 units. We can examine these shifts in further detail in the second custom dashboard named 2. Outbound Flows by Site, as shown in the next 2 screenshots:
This first screenshot of the dashboard shows the amount of flow from the 3 DCs and the factory to the 6 customer locations. As we already noticed on the map, the only shift here is that the Rio De Janeiro customer is served by the Barueri DC in the Baseline scenario and this changes to it being served by the Contagem DC in the IncludeDCMGTaxBenefit scenario.
Scrolling further right in this table, we see the replenishment flows from the 3 DCs and the Factory to the 3 DCs. There are some more changes here where we see that the flow from the factory to the Barueri DC is reduced by 500 units in the scenario, whereas the flow from the factory to the Contagem DC is increased by 500 units. In the Baseline, the Barueri DC transferred a total of 1,000 units to the other 2 DCs (500 each to the Contagem and Feira de Santana DCs), and the other 2 DCs did not make DC transfers. In the Tax Benefit scenario, the Barueri DC only transfers to the Contagem DC, but now for 1,500 units. We also see that the Contagem DC now transfers 500 units to the Feira de Santana DC, whereas it did not make any transfers in the Baseline scenario.
We hope this gives you a good idea of how taxes and tax incentives can be considered in Cosmic Frog models. Give it a go and let us know of any feedback and/or questions!
Utilities enable powerful modelling capabilities for use cases like integration to other services or data sources, repeatable data transformation or anything that can be supported by Python! System Utilities are available as a core capability in Cosmic Frog for use cases like LTL rate lookups, TransitMatrix time & distance generation, and copying items like Maps and Dashboards from one model to another. More useful System Utilities will become available in Cosmic Frog over time. Some of these System Utilities are also available in the Resource Library where they can be downloaded from, and then customized and made available to modelers for specific projects or models. In this Help Article we will cover both how to use use System Utilities as well as how to customize and deploy Custom Utilities.
The “Using and Customizing Utilities” resource in the Resource Library includes a helpful 15-minute video on Cosmic Frog Model Utilities and users are encouraged to watch this.
In this Help Article, System Utilities will be covered first, before discussing the specifics of creating one’s own Utilities. Finally, how to use and share Custom Utilities will be explained as well.
Users can access utilities within Cosmic Frog by going to the Utilities section via the Module Menu drop-down:
Once in the Utilities section, user will see the list of available utilities:
The appendix of this Help Article contains a table of all System Utilities and their descriptions.
Utilities vary in complexity by how many input parameters a user can configure and range from those where no parameters need to be set at all to those where many can be set. Following screenshot shows the Orders to Demand utility which does not require any input parameters to be set by the user:
The Copy map to a model utility shown in the next screenshot does require several parameters to be set by the user:
When the Run Utility button has been clicked, a message appears beneath it briefly:
Clicking on this message will open the Model Activity pane to the right of the tab(s) with open utilities:
Users will not only see activities related to running utilities in the Model Activity list. Other actions that are executed within Cosmic Frog will be listed here too, like for example when user has geocoded locations by using the Geocode tool on the Customers / Facilities / Suppliers tables or when user makes a change in a master table and chooses to cascade these changes to other tables.
Please note that the following System Utilities have separate Help Articles where they are explained in more detail:
The utilities that are available in the Resource Library can be downloaded by users and then customized to fit the user’s specific needs. Examples are to change the logic of a data transformation, apply similar logic but to a different table, etc. Or users may even build their own utilities entirely. If a user updates a utility or creates a new one, they can share these back with other users so they can benefit from them as well.
Utilities are Python scripts that contain a specific structure which will be explained in this section. They can be edited directly in the Atlas application on the Optilogic platform or users can download the Python file that is being used as a starting point and edit it using an IDE (Integrated Development Environment) installed on their computer. A rich text editor geared towards coding, like for example Visual Studio Code, will work fine too for most. An advantage of working locally is that user can take advantage of code completion features (auto-completion while typing, showing what arguments functions need, catch incorrect syntax/names, etc.) by installing an extension package like for example IntelliSense (for Visual Studio Code). The screenshots of the Python files underlying the utilities that follow in this documentation are taken while working with them in Visual Studio Code locally and on a machine that has the IntelliSense extension package installed.
A great resource on how to write Python scripts for Cosmic Frog models is this “Scripting with Cosmic Frog” video. In this video, the cosmicfrog Python library, which adds specific functionality to the existing Python features to work with Cosmic Frog models, is covered in some detail.
We will start by looking at the Python file of the very simple Hello World utility. In this first screenshot, the parts that can stay the same for all utilities are outlined in green:
Next, onto the parts of the utility’s Python script that users will want to update when customizing / creating their own scripts:
Now, we will discuss how input parameters, which users can then set in Cosmic Frog, can be added to the details function. After that we will cover different actions that can be added to the run function.
If a utility needs to be able to take any inputs from a user before running it, these are created by adding parameters in the details function of the utility’s Python script:
We will take a closer look at a utility that uses parameters and map the arguments of the parameters back to what the user sees when the utility is open in Cosmic Frog, see the next 2 screenshots: the numbers in the script screenshot are matched to those in the Cosmic Frog screenshot to indicate what code leads to what part of the utility when looking at it in Cosmic Frog. These screenshots use the Copy dashboard to a model utility of which the Python script (Copy dashboard to a model.py) was downloaded from the Resource Library.
Note that Python lists are 0-indexed, meaning that the first parameter (Destination Model in this example) is referenced by typing params[0], the second parameter (Replace of Append dashboards) by typing params[1], etc. We will see this in the code when adding actions to the run function below too.
Now let’s have a look at how the above code translates to what a user sees in the Cosmic Frog user interface for the Copy dashboard to a model System Utility (note that the numbers in this screenshot match with those in the above screenshot):
The actions a utility needs to perform are added to the run function of the Python script. These will be different for different types of utilities. We will cover the actions the Copy dashboard to a model utility uses at a high level and refer to Python documentation if user is interested in understanding all the details. There are a lot of helpful resources and communities online where users can learn everything there is to know about using & writing Python code. A great place to start is on the Python for Beginners page on python.org. This page also mentions how more experienced coders can get started with Python. Also note that text in green font that follows a hash sign are comments to add context to code.
For a custom utility to be showing in the My Utilities category of the utilities list in Cosmic Frog, it needs to be saved under My Files > My Utilities in the user’s Optilogic account:
Note that if a Python utility file is already in user’s Optilogic account, but in a different folder, user can click on it and drag it to the My Utilities folder.
For utilities to work, a requirements.txt file which only contains the text cosmicfrog needs to be placed in the same My Files > My Utilities folder (if not there already):
A customized version of the Copy dashboard to a model utility was uploaded here, and a requirements.txt file is present in the same folder too.
Once a Python utility file is uploaded to My Files > My Utilities, it can be accessed from within Cosmic Frog:
If users want to share custom utilities with other users, they can do so by right-clicking on it and choosing the “Send Copy of File” option:
The following form then opens:
When a custom utility has been shared with you by another user, it will be saved under the Sent To Me folder in your Optilogic account:
Should you have created a custom utility that you feel a lot of other users can benefit from and you are allowed to share outside of your organization, then we encourage you to submit it into Optilogic’s Resource Library. Click on the Contribute button at the left top of the Resource Library and then follow the steps as outlined in the “How can I add Python Modules to the Resource Library?” section towards the end of the “How to use the Resource Library” help article.
Utility names and descriptions by category:
When demand fluctuates due to for example seasonality, it can be beneficial to manage inventory dynamically. This means that when the demand (or forecasted demand) goes up or down, the inventory levels go up or down accordingly. To support this in Cosmic Frog models, inventory policies can be set up in terms of days of supply (DOS): for example for the (s,S) inventory policy, the Simulation Policy Value 1 UOM and Simulation Policy Value 2 UOM fields can be set to DOS. Say for example that reorder point s and order up to quantity S are set to 5 DOS and 10 DOS, respectively. This means that if the inventory falls to or below the level that is the equivalent of 5 days of supply, a replenishment order is placed that will order the amount of inventory to bring the level up to the equivalent of 10 days of supply. In this documentation we will cover the DOS-specific inputs on the Inventory Policies table, how a day of supply equivalent in units is calculated from these and walk through a numbers example.
In short, using DOS lets users be flexible with policy parameters; it is a good starting point for estimating/making assumptions about how inventory is managed in practice.
Note that it is recommended you are familiar with the Inventory Policies table in Cosmic Frog already before diving into the details of this help article.
The following screenshot shows the fields that set the simulation inventory policy and its parameters:
For the same inventory policy, the next screenshot shows the DOS-related fields on the Inventory Policies table; note that the UOM fields are omitted in this screenshot:
As mentioned above, when using forecasted demand for the DOS calculations, this forecasted demand needs to be specified in the User Defined Forecasts Data and User Defined Forecasts tables, which we will discuss here. This next screenshot shows the first 15 example records in the User Defined Forecasts Table:
Next, the User Defined Forecasts table lets a user configure the time-period to which a forecast is aggregated:
Let us now explain how the DOS calculations work for different DOS settings through the examples shown in the next screenshot. Note that for all these examples the DOS Review Period First Time field has been left blank, meaning that the first 1 DOS equivalent calculation occurs at the start of this model (on January 1st) for each of these examples:
Now that we know how to calculate the value of 1 DOS, we can apply this to inventory policies which use DOS as their UOM for the simulation policy value fields. We will do a numbers example with the one shown in the screenshot above (in the Days of Supply Settings section) where reorder point s is 5 DOS and order up to quantity S is 10 DOS. Let us assume the same settings as in the last example for the 1 DOS calculations in the screenshot above, explained in bullet #6 above: forecasted demand is used with a 10 day DOS Window, a 5 day DOS Leadtime, and a 5 day DOS Review Period, so the calculations for the equivalent of 1 DOS are the numbers in the last row shown in the screenshot, which we will use in our example below. In addition to this, we will assume a 2 day Review Period for the inventory policy, meaning inventory levels are checked every other day to see if a replenishment order needs to be placed. DC_1 also has 1,000 units of P1 on hand at the start of the simulation (specified in the Initial Inventory field):
Leapfrog helps Cosmic Frog users explore and use their model data via natural language. View data, make changes, create & run scenarios, analyze outputs, learn all about the Anura schema that underlies Cosmic Frog models, and a whole lot more!
Leapfrog combines an extensive knowledge of PostgreSQL with the complete knowledge of Optilogic’s Anura data schema, and all the natural language capabilities of today’s advanced general purpose LLMs.
For a high-level overview and short video introducing Leapfrog, please see the Leapfrog landing page on Optilogic’s website.
In this documentation, we will first get users oriented on where to find Leapfrog and how to interact with it. In the section after, Leapfrog’s capabilities will be listed out with examples of each. Next, the Tips & Tricks section will give users helpful pointers so they can get the most out of Leapfrog. Finally, we will step through the process of building, running, and analyzing a Cosmic Frog model start to finish by only using Leapfrog!
Dive in if you’re ready to take the leap!
Start using Leapfrog by opening the module within Cosmic Frog:
Once the Leapfrog module is open, users’ screens will look similar to the following screenshot:
The example prompts when using the Anura Help LLM are shown here:
When first starting to use Leapfrog, users will also see the Privacy and Data Security statement, which reads as follows:
“Leapfrog AI Training: Optilogic does not use your model data to train Leapfrog. We do collect and store conversational data so it can be accessed again by the user, as well as to understand usage patterns and areas of strength/weakness for the LLM. Included in this data: natural language input prompts, text and SQL responses, as well as feedback from users. This information is maintained by Optilogic, not shared with third parties, and all of the conversation data is subject to the data security and privacy terms of the Optilogic platform.”
This message will stay visible within Leapfrog whenever it is being used, unless user clicks on the grey cross button on the right to close the message. Once closed, the message will not be shown again while using Leapfrog.
Conversation history is stored on the platform at the user level - not in the model database - so it does not get shared when a model is shared. Note that if you are working in a Team rather than in your My Account (see documentation on Teams on the Optilogic platform here), the Leapfrog conversations you are creating will be available to the other team members when they are working with the same model.
As mentioned in the previous section, Leapfrog currently makes use of 2 large language models (LLMs): Text2SQL and Anura Help (also referred to as Anura Aficionado or A2). They will be explained in some more detail here. There is also an appendix to this documentation where for a few example personas Leapfrog questions and responses are listed, which showcases how some users may predominantly use one model, while others may switch back and forth between them. Of course, when unsure, users can try a specific prompt using both LLMs to see which provides the most helpful response.
Please note that in future users will not need to indicate which LLM they want to run a prompt against as Leapfrog will recognize which one will be most suitable to use based on the prompt.
The Text2SQL LLM combines extensive knowledge of PostgreSQL with Optilogic’s Anura data schema, and all the natural language capabilities of today’s advanced general purpose LLMs. It has been further fine-tuned on a large set of prompt-response pairs hand-crafted by supply chain modeling experts. This allows the Text2SQL model to generate SQL queries from natural language prompts.
Prompts for which it is best to use the Text2SQL model often imply an action: “Show me X”, “Add Y”, “Delete Z”, “Run scenario A”, “Create B”, etc. See also the example prompts listed when starting a new conversation and those in the Prompt Library on the Frogger Pond community.
Leapfrog responses using this model are usually actionable: run the returned SQL query to add / edit / delete data, create a scenario or model, run a scenario, geocode locations, etc.
A full list of the capabilities of both LLMs is covered in the section “Leapfrog Capabilities” further below.
Anura Help (also referred to as Anura Aficionado or A2) is a specialized assistant that leverages advanced natural language processing to help users navigate and understand the Anura schema within Optilogic's Cosmic Frog application. The Anura schema is the foundational framework powering Cosmic Frog's optimization, simulation, and risk assessment capabilities. Anura Help eliminates traditional barriers to schema understanding by providing immediate, authoritative guidance for supply chain modelers, developers, and analysts.
Anura Help’s architecture uses the Retrieval Augmented Generation (RAG) approach: based on the natural language prompt, first the most relevant documents of those in its knowledge base are retrieved (e.g. schema details or engine awareness details). Next, it uses them to generate a natural language response.
Use the Anura Help model when wanting to learn about specific fields, tables or engines in Cosmic Frog. Its core capabilities include:
Responses from Leapfrog when using the Anura Help model are text-based and generated from retrieved documents shown in the context section. This context can for example be of the category “column info” where all details for a specific field are listed.
A full list of the capabilities of both LLMs is covered in the section “Leapfrog Capabilities” further below.
The following list compares the 2 LLMs available in Leapfrog today:
Depending on the type of question, Leapfrog’s response to it can take different forms: text, links, SQL queries, data grids, and options to create models, scenarios, scenario items, groups, run scenarios, or geocode locations. We will look at several examples of questions that result in these different types of responses in this section. This is not an exhaustive list; the next section “Leapfrog Capabilities” will go through the types of prompt-response pairs Leapfrog is capable of today.
For our first question, we used the first Text2SQL example prompt “What are the top 3 products by demand?” by clicking on it. After submitting the prompt, we see that Leapfrog is busy formulating a response:
And Leapfrog’s response to the prompt is as follows:
The metadata included here are:
Clicking on the icon with 3 dots again will collapse the response metadata.
This first prompt asked a question about the input data contained in the Cosmic Frog model. Let us now look at a slightly different type of prompt, which asks to change model input:
We are going to run the SQL query of the above response to our “Increase demand by 20%” prompt. Before doing so, let’s review a subset of 10 records of the Customer Demand input table (under the Data Module, in the Input Tables section):
Next, we will run the SQL query:
After clicking the Run SQL button at the bottom of the SQL Query section in Leapfrog’s response, it becomes greyed out so it will not accidentally be run again. Hovering over the button also shows text indicating the query was already run:
Note that closing and reopening the model or refreshing the browser will revert the Run SQL button’s state so it is clickable again.
Opening the Customer Demand table again and looking at the same 10 records, we see that the Quantity field has indeed been changed to its previous value multiplied by 1.2 (the first record’s value was 643, and 643 * 1.2 = 771.6, etc.):
Running the SQL query to increase the demand by 20% directly in the master data worked fine as we just saw. However, if we do not want to change the master data, but rather want to increase the demand quantity as part of a scenario, this is possible too:
After navigating to the Scenarios module within our Cosmic Frog model, we can see the scenario and its item have been created:
Note that if desired, the scenario and scenario item names auto-generated by Leapfrog can be changed in the Scenarios module of Cosmic Frog: just select the scenario or item and then choose “Rename” from the Scenario drop-down list at the top.
As a final example of a question & answer pair in this section, let us look at one where we use the Anura Help LLM, and Leapfrog responds with text plus context:
There is a lot of information listed here; we will explain the most commonly used information:
Prompts and their responses are organized into conversations in the Leapfrog module:
Users can organize their conversations with Leapfrog by using the options from the Conversations drop-down at the top of the Leapfrog module:
Users can rate Leapfrog responses by clicking on the thumbs up (like) and thumbs down (dislike) buttons and, optionally, providing additional feedback. This feedback is used to continuously improve Leapfrog. Giving a thumbs up to indicate the response is what you expected helps reinforce correct answers from Leapfrog. When a response is not what was expected or wrong, users can help improve Leapfrog’s underlying LLMs by giving the response a thumbs down. Especially thumbs down ratings & additional feedback will be reviewed so Leapfrog can learn and become more useful all the time.
When a response is not as expected as was the case in the following screenshot, user is encouraged to click the thumbs down button:
After clicking on the Send button, the detailed feedback is automatically added to the conversation:
The next screenshot shows an example where user gave Leapfrog’s response a thumbs up as it was what user expected. This feedback can then be used by Leapfrog to reinforce correct answers. User also had the option to provide detailed feedback again, using any of the following 4 optional tags: Showcase Example, Surprising, Fun, and Repeatable Use Case. In this example, user decided not to give detailed feedback and clicked on the Close button after the detailed feedback form came up:
If you have any additional Leapfrog feedback (or questions) beyond what can be captured here, you can feel free to send an email to Leapfrog@Optilogic.com. You are also very welcome to ask questions, share your experiences, and provide feedback on Leapfrog in the Frogger Pond Community.
We will now go back to our first prompt “What are the top 3 products by demand” to explore some of the options users have when Data Grids are included in a Leapfrog response, which is the case when Leapfrog’s SQL Query response is a SELECT statement.
When clicking on the Download File button, a zip file with the name of the active Cosmic Frog model appended with an ID, is downloaded to the user’s Downloads folder. The zip contains:
After clicking on Save, following message appears beneath the Data Grid in Leapfrog’s response:
Looking in the Custom Tables section (#2 in screenshot below) of the Data module (#1 in screenshot below), we indeed see this newly created table named top3products (#3 in screenshot below) with the same contents as the Data Grid of the Leapfrog response:
If we choose to save the Data Grid as a view instead of a table, it goes as follows:
We choose Save as View and give it the name of Top3Products_View. The message that comes up once the view is created reads as follows:
Going to the Analytics module in Cosmic Frog, choosing to add a new dashboard and in this new dashboard a new visualization, we can find the top3products_view in the Views section:
We will go back to the original Data Grid in Leapfrog’s response to explore a few more options user has here:
Please note:
In this section we will list out what Leapfrog is capable of and give examples of each capability. These capabilities include (the LLM each capability applies to is listed in parentheses):
Each of these capabilities will be discussed in the following sections, where a brief description of each capability is given, several example prompts illustrating the capability are listed, and a few screenshots showing the capability are included as well. Please remember that many more example prompts can be found in the Prompt Library on the Frogger Pond community.
Interrogate input and output data using natural language. Use it to check completeness of input data, and to summarize input and/or output data. Leapfrog responds with SELECT Statements and shows a Data Grid preview as we have seen above. Export the data grid or save it as a table or view for further use, which has been covered above already too.
Example prompts:
The following 3 screenshots show examples of checking input data (first screenshot), and interrogating output data (second and third screenshot):
Tell Leapfrog what you want to edit in the input data of your Cosmic Frog model, and it will respond with UPDATE, INSERT, and DELETE SQL Statements. User can opt to run these SQL Queries to permanently make the change in the master input data. For UPDATE SQL Queries, Leapfrog’s response will also include the option to create a scenario and scenario item that will make the change, which we will focus on in the next section.
Example prompts:
The following 3 screenshots show examples changing values in the input data (first screenshot), adding records to an input table (second screenshot), and deleting records from an input table (third screenshot):
Make changes to input data, but through scenarios rather than updating the master tables directly. Prompts that result in UPDATE SQL Queries will have a Scenarios part in their responses and users can easily create a new scenario that will make the input data change by one click of a button.
Example prompts:
The following 3 screenshots show example prompts with responses from which scenarios can be created: create a scenario which makes a change to all records in 1 input table (first screenshot), create a scenario which makes a change to records in 1 input table that match a condition (second screenshot), and create a scenario that makes changes in 2 input tables (third screenshot):
The above screenshots show examples of Leapfrog responses that contain a Scenarios section and from which new scenarios and scenario items can be created by clicking on the Create Scenario button. In addition to the above, users can also use Leapfrog to manage scenarios by using prompts that specifically create scenarios and/or items and assigning specific scenario items to specific scenarios. These result in INSERT INTO SQL Statements which can then be implemented by using the Run SQL button. See the following 2 screenshots for examples of this, where 1) a new scenario is created and an existing scenario item is then assigned to it, and 2) a new scenario item is created which is then assigned to an already existing scenario:
Leapfrog can create new groups and add group members to new and existing groups. Just specify the group name and which members it needs to have in the prompt and Leapfrog’s response will be one or multiple INSERT INTO SQL Statements.
Example prompts:
The following 4 screenshots show example prompts of creating groups and group members: 1) creates a new products group and adds products that have names with a certain prefix (FG_) to it, 2) creates a new periods group and adds 3 specific periods to it, 3) creates a new suppliers group and adds all suppliers that are located in China to it, and 4) adds a new member to an existing facilities group, and in addition explicitly sets the Status and Notes field of this new record in the Groups table:
Leapfrog can create a new, blank model. Leapfrog's response will ask user to confirm if they want to create the new model before creating it. If confirmed, the response will update to contain a link which takes user to the Leapfrog module in this newly created model in a new tab of the browser.
Example prompts:
Following 2 screenshots show an example where a new model named “FrogsLeaping” is created:
You can ask Leapfrog to kick off any model runs for you. Optionally you can specify the scenario(s) you want to be run, which engine to use, and what resource size to use. For Neo (network optimization) runs, user can additionally indicate if the infeasibility check should be turned on. If no scenario(s) are specified, all scenarios present in the model will be run. If no engine is specified, the Neo engine (network optimization) will be used. If no resource size is specified, S will be used. If for Neo runs it is not specified if the infeasibility check should be turned on or off it will be off by default.
Leapfrog’s response will summarize the scenario(s) that are to be run, the engine that will be used, the resource size that will be used, and for Neo runs if the infeasibility check will be on or off. If user indeed wants to run the scenario(s) with these settings, they can confirm by clicking on the Run button. If so, the response will change to contain a link to the Run Manager application on Optilogic’s platform, which will be opened in a new tab of the browser when clicked. In the Run Manager, users can monitor the progress of any model runs.
The engines available in Cosmic Frog are:
The resource sizes available are as follows, from smallest to largest: Mini, 4XS, 3XS, 2XS, XS, S, M, L, XL, 2XL, 3XL, 4XL, Overkill. Guidance on choosing a resource size can be found here.
Example prompts:
The following 2 screenshots show an example prompt where the response is to run the model: only the scenario name is specified in which case a network optimization (Neo) is run using resource size S with the infeasibility check turned off (False):
Leapfrog's response now indicates the run has been kicked off and provides a link (click on the word "here") to check the progress of the scenario run(s) in the Run Manager.
The next screenshot shows a prompt asking to specifically run Greenfield (Triad) on 2 scenarios, where the resource size to be used is specified in the prompt too:
The last screenshot in this section shows a prompt to run a specific scenario with the infeasibility check turned on:
Leapfrog can find latitude & longitude pairs for locations (customers, facilities, and suppliers) based on the location information specified in these input tables (e.g. Address, City, Region, Country). Leapfrog’s response will ask user to confirm they want to geocode the specified table(s). If so, the response will change to contain a link which will open a Cosmic Frog map showing the locations that have been geocoded in a new tab of the browser.
Example prompts:
Notes on using Leapfrog for geocoding locations:
In the following screenshot, user asks Leapfrog to geocode customers:
As geocoding a larger set of locations can take some time, it may look like the geocoding was not done or done incompletely if looking at the map or in the Customers / Facilities / Suppliers input tables shortly after kicking off the geocoding. A helpful tool which shows the progress of the geocoding (and other tools / utilities within Cosmic Frog) is the Model Activity list:
Leapfrog can teach users all about the Anura schema that underlies Cosmic Frog models, including:
Example prompts:
The following 4 screenshots show examples of these types of prompts & Leapfrog’s responses: 1) ask Leapfrog to teach us about a specific field on a specific table, 2) find out which table to use for a specific modelling construct, 3) understand the SCG to Cosmic Frog’s Anura mapping for a specific field on a specific table, and 4) ask about breaking changes in the latest Anura schema update:
Anura Help provides information around system integration, which includes:
Example prompts:
The following 4 screenshots show examples of these types of prompts & Leapfrog’s responses: 1) ask which tables are required to run a specific engine, 2) find out which engines use a specific table, 3) learn which table contains a certain type of outputs, and 4) ask about availability of template models for a specific purpose:
Leapfrog knows about itself, Optilogic, Cosmic Frog, the Anura database schema, LLM’s, and more. Ask Leapfrog questions so it can share its knowledge with you. For most general questions both LLMs will generate the same or a very similar answer, whereas for questions that are around capabilities, each may only answer what is relevant to it.
Example prompts:
The following 5 screenshots show examples of these types of prompts & Leapfrog’s responses: 1) ask both LLMs about their version, 2) ask a general question about how to do something in Cosmic Frog (Text2SQL), 3) ask Anura Help for the Release Notes, and 4 & 5) ask both LLMs about what they are good at and what they are not good at:
Even though this documentation and Leapfrog example prompts are predominantly in English, Leapfrog supports many languages so users can ask questions in their most natural language. Where the Leapfrog response is in text form, it can respond in the language the question was asked in. Other response types like a standard message with a link or names of scenarios and scenario items will be in English.
The following 3 screenshots show: 1) a list of languages Leapfrog supports, 2) a French prompt to increase demand by 20%, and 3) a Spanish prompt asking Leapfrog to explain the Primary Quantity UOM field on the Model Settings table:
To get the most out of Leapfrog, please take note of these tips & tricks:
After this is turned on, you can start using it by pressing the keyboard’s Windows key + H. A bar with a microphone which shows messages like “initializing”, “listening”, “thinking” will show up at the top of your active monitor:
Now you can speak into your computer’s microphone, and your spoken words will be turned into text. If you put your cursor in Leapfrog’s question / prompt area, click on the microphone in the bar at the top so your computer starts listening, and then say what you want to ask Leapfrog, it will appear in the prompt area. You can then click on the send icon to submit your prompt / question to Leapfrog.
The following screenshots show several examples of how one can build on previous prompts and responses and try to re-direct Leapfrog as described in bullets 6 and 7 of the Tips & Tricks above. In the first example user wants to delete records from an input table whereas Leapfrog’s initial response is to change the Status of these records to Exclude. The follow-up prompt clarifies that user wants to remove them. Note that it is not needed to repeat that it is about facilities based in the USA, which Leapfrog still knows from the previous prompt:
In the following example shown in the next 3 screenshots, user starts by asking Leapfrog to show the 2 DCs with the highest throughput. The SQL query response only looks at Replenishment flows, but user wants to include Customer Fulfillment flows too. Also, the SQL Query does not limit the list to the top 2 DCs. In the follow-up prompt the user clarifies this (“Like that, but…”) without needing to repeat the whole question. However, Leapfrog only picks up on the first request (adding the Customer Fulfillment flows), so in the third prompt user clarifies further (again: “Like that, but…”), and achieves what they set out to do:
In the next 2 screenshots we see an example of first asking Leapfrog to show outputs that meet certain criteria (within 3%), and then essentially wanting to ask the same question but with the criteria changed (within 6%). There is no need to repeat the first prompt, it suffices to say something like “How about with [changed criteria]?”:
When Leapfrog only does part of what a user intends to do, it can often still be achieved in multiple steps. See the following screenshots where user intended to change 2 fields on the Production Count Constraints table and initially Leapfrog only changes one. The follow-up prompt simply consists of “And [change 2]”, building on the previous prompt. In the third prompt user was more explicit in describing the 2 changes and then Leapfrog’s response is what user intended to achieve:
Here we will step through the process of building a complete Cosmic Frog demo model, creating an additional scenario, running this new scenario and the Baseline scenario, and interrogating some of the scenarios’ outputs, all by using only Leapfrog.
Please note that if you are trying the same steps using Leapfrog in your Cosmic Frog:
We will first list the prompts that were used to build, run, and analyze the model, and then review the whole process step-by-step through (lots of!) screenshots. Here is the list of prompts that were submitted to Leapfrog (all of them used the Text2SQL LLM):
And here is the step-by-step process shown through screenshots, starting with the first prompt given to Leapfrog to create a new empty Cosmic Frog model with the name “US Distribution”:
Clicking on the link in Leapfrog’s response will take user to the Leapfrog module in this newly created US Distribution model:
In the next prompt (the third one from the list), distribution center (DC) and manufacturing (MFG) locations are added to the Facilities table, and customer locations to the Customers table. Note the use of a numbered list to help Leapfrog break the response up into multiple INSERT INTO statements:
After running the SQL of that Leapfrog response, user has a look in the Facilities and Customers tables and notices that as expected all Latitude and Longitude values are blank:
Since all Facilities and Customers have blank Latitudes and Longitudes, our next (fourth) prompt is to geocode all sites:
Once the geocoding completes (which can be checked in the Model Activity list), user clicks on one of the links in the Leapfrog response. This opens the Supply Chain map of the model in a new tab in the browser, showing Facilities and Customers, which all look to be geocoded correctly:
We can also double-check this in the Customers and Facilities tables, see for example next screenshot of a subset of 5 customers which now have values in their Latitude and Longitude fields:
For a (network optimization - Neo) model to work, we will also need to add demand. As this is an example/demo model, we can use Leapfrog to generate random demand quantities for us, see this next (fifth) prompt and response:
After clicking the Run SQL button, we can have a look in the Customer Demand input table, where we find the expected 200 records (50 customers which each have demand for 4 products) and eyeballing the values in the Quantity field we see the numbers are as expected between 10 and 1000:
Our sixth prompt sets the model end and start dates, so the model horizon is all of 2025:
Again, we can double-check this after running the SQL response by having a look in the Model Settings input table:
We also need Transportation Policies, the following prompt (the seventh from our list) takes care of this and creates lanes from all MFGs to all DCs and from all DCs to all customers:
We see the 6 enumerated MFG (2 locations) to DC (3 locations) lanes when opening and sorting the Transportation policies table, plus the first few records of the 150 enumerated DC to customer lanes. No Unit Costs are set so far (blank values):
Our eighth prompt sets the transportation unit costs on the transportation policies created in the previous step. All use a unit of measure of EA-MI which means the costs entered are per unit per mile, and the cost itself is 1 cent on MFG to DC lanes and 2 cents on DC to customer lanes:
Clicking the Run SQL button will run the 4 UPDATE statements, and we can see the changes in the Transportation Policies input table:
In order to run, the model also needs Production Policies, which the next (ninth) prompt takes care of: both MFG locations can produce all 4 products:
Again, double-checking after running the SQL from the response, we see the 8 expected records in the Production Policies input table:
Our 3 DCs have an upper limit as to how much throughput they can handle over the year, this is 50,000 for the DCs in Reno and Memphis and 100,000 for the DC in Jacksonville. Prompt number 10 sets these:
We can see these numbers appear in the Throughput Capacity field on the Facilities input table after running the SQL of Leapfrog’s response:
We want to explore what happens if the maximum throughput of the DC in Memphis is increased to 100,000; this is what the eleventh prompt asks to do:
Leapfrog’s response has both a SQL UPDATE query, which would change the throughput at DC_Memphis in the Facilities input table, and a Scenarios section. We choose to click on the Create Scenario button so a new scenario is created (Increase Memphis DC Capacity) which will contain 1 scenario item (set_dc_memphis_capacity_to_100000) that sets the throughput capacity at DC_Memphis to 100,000:
Our small demo model is now complete, and we will use Leapfrog (using our twelfth prompt) to run network optimization (using the Neo engine) on the Baseline and Increase Memphis DC Capacity scenarios:
While the scenarios are running, we are thinking about what will be interesting outputs to review, and ask Leapfrog about how one can compare customers flows between scenarios (prompt number 13):
This information can come in handy in one of the next prompts to direct Leapfrog on where to look.
Using the link from the previous Leapfrog response where we started the optimization runs for both scenarios, we open the Run Manager in a new tab of the browser. Both scenarios have completed successfully as their State is set to Done:
Looking in the Optimization Network Summary output table, we also see there are results for both scenarios:
In the next few prompts Leapfrog is used to look at outputs of the 2 scenarios that have been run. The prompt (number 14 from our list) in the next screenshot aims to get Leapfrog to show us which customers have a different source in the Increase Memphis DC Capacity scenario as compared to the Baseline scenario:
Leapfrog’s response is almost what we want it to be, however it has duplicates in the Data Grid. Therefore, we follow our previous prompt up with the next one (number 15), where we ask to see only distinct combinations. Instead of “distinct” we could have also used the word “unique” in our prompt:
We see that the source for around 11-12 customers changed from the DC in Jacksonville in the Baseline to the DC in Memphis in the Increase Memphis DC Capacity scenario.
Cost comparisons between scenarios are usually interesting too, so that is what prompt number 16 asks about:
We notice that increasing the throughput capacity at DC_Memphis leads to a lower total supply chain cost by about 56.5k USD. Next, we want to see how much flow has shifted between the DCs in the Baseline scenario compared to the Increase Memphis DC Capacity scenario, which is what the last prompt (number 17) asks about:
This tells us that the throughput at DC_Reno is the same in both scenarios, but that increasing the DC_Memphis throughput capacity allows a shift of about 24k units from the DC in Jacksonville to the DC in Memphis (which was at its maximum 50k throughput in the Baseline scenario). This volume shift is what leads to the reduction in total supply chain cost.
We hope this gives you a good idea of what Leapfrog is capable of today. Stay tuned for more exciting features to be added in future releases!
Do you have any Leapfrog questions or feedback? Feel free to use the Frogger Pond Community to ask questions, share your experiences, and provide feedback. Or, shoot us an email at Leapfrog@Optilogic.com.
Happy Leapfrogging!
PERSONA: Alex is an experienced supply chain modeler who knows exactly what to analyze but often spends too much time pulling and formatting outputs. They are looking to be more efficient in summarizing results and identifying key drivers across scenarios. While confident in their domain expertise, they want help extracting insights faster without losing control or accuracy. They see AI as a time-saving partner that helps them focus on decision-making, not data wrangling.
USE CASE: After running multiple scenarios in Cosmic Frog, Alex wants to quickly understand the key differences in cost and service across designs. Instead of manually exporting data or writing SQL queries, Alex uses Leapfrog to ask natural-language questions which saves Alex hours and lets them focus on insight generation and strategic decision-making.
Model to use: Global Supply Chain Strategy (available under Get Started Here in the Explorer).
Prompt #1
Prompt #2
Prompt #3
Prompt #4
Prompt #5
PERSONA: Chris is an experienced supply chain modeler with a well-established, repeatable workflow that pulls data from internal systems to rebuild models every quarter. He relies on consistency in the model schema to keep his automation running smoothly.
USE CASE: With an upcoming schema change in Cosmic Frog, Chris is concerned about disruptions or errors in his process and wants Leapfrog to help provide info on the changes that may require him to update his workflow.
Model to use: any.
Prompt #1
Prompt #2
Prompt #3
PERSONA: Larry Loves LLMs – he wants to use an LLM to find answers.
USE CASE: I need to understand the outputs of this model someone else built. I want to know how many products come from each supplier for each network configuration. Can Leapfrog help with that?
Yes, Leapfrog can help with that! Let's use Anura Help to better understand which tables have that data and then ask Text2SQL to pull the data.
Model to use: any; Global Supply Chain Strategy (available under Get Started Here in the Explorer).
Prompt #1
Prompt #2
To enable users to build basic Cosmic Frog for Excel Applications to interact directly with Cosmic Frog from within Excel without needing to write any code, Optilogic has developed the Cosmic Frog for Excel Application Builder (also referred to as App Builder in this documentation). In this App Builder, users can build their own workflows using common actions like creating a new model, connecting to an existing model, importing & exporting data, creating & running scenarios, and reviewing outputs. Once a workflow has been established, the App can be deployed so it can be shared with other users. These other users do not need to build the workflow of the App again, they can just use the App as is. In this documentation we will take a user through the steps of a complete workflow build, including App deployment.
You can download the Cosmic Frog for Excel – App Builder from the Resource Library. A video showing how the App Builder is used in a nutshell is included; this video is recommended viewing before reading further. After downloading the .zip file from the Resource Library and unzipping it on your local computer, you will find there are 2 folders included: 1) Cosmic_Frog_For_Excel_App_Builder, which contains the App Builder itself and this is what this documentation will focus on, and 2) Cosmic_Frog_For_Excel_Examples, which contains 3 examples of how the App Builder can be used. This documentation will not discuss these examples in detail; users are however encouraged to browse through them to get an idea of the types of workflows one can build with the App Builder.
The Cosmic_Frog_For_Excel_App_Builder folder contains 1 subfolder and 1 Macro-enabled Excel file (.xlsm):
When ready to start building your first own basic App, open the Cosmic_Frog_For_Excel_Builder_v1.xlsm file; the next section will describe the steps a user needs to take to start building.
When you open the Cosmic_Frog_For_Excel_App_Builder_v1.xlsm file in Excel, you will find there are 2 worksheets present in the workbook, Start and Workflow. The top of the Start worksheet looks like this:
Going to the Workflow worksheet and clicking on the Cosmic Frog tab in the ribbon, we can see the actions that are available to us to create our basic Cosmic Frog for Excel Applications:
We will now walk through building and deploying a simple App to illustrate the different Actions and their configurations. This workflow will: connect to a Greenfield model in my Optilogic account, add records to the Customer and CustomerDemand tables, create a new scenario with 2 new scenario items in it, run this new scenario, and then export the Greenfield Facility Summary output table from the Cosmic Frog model into a worksheet of the App. As a last step we will also deploy the App.
On the Workflow worksheet, we will start building the workflow by first connecting to an existing model in my Optilogic account:
The following screenshot shows the Help tab of the “Connect To Or Create Model Action”:
In the remainder of the documentation, we will not show the Help tab of each action. Users are however encouraged to use these to understand what the action does and how to configure it.
After creating an action, the details of it will be added to 2 columns in the Workflow worksheet, see screenshot below. The first action of the workflow will use columns A & B, the next action C & D, etc. When adding actions, the placement on the Workflow worksheet is automatic and user does not need to do or change anything. Blue fields contain data that cannot be changed, white fields are user inputs when setting up the action and can be changed in the worksheet itself too.
The United States Greenfield Facility Selection model we are connecting to contains about 1.3k customer locations in the US which have demand for 3 products: Rockets, Space Suits, and Consumables. As part of this workflow, we will add 10 customers located in the state of Ontario in Canada to the Customers table and add demand for each of these customers for each product to the CustomerDemand table. The next 2 screenshots show the customer and customer demand data that will be added to this existing model.
First, we will use an Import Data action to append the new customers to the Customers table in the model we are connecting to:
Next, use the Import Data Action again to upsert the data contained in the New_CustomerDemand worksheet to the CustomerDemand table in the Cosmic Frog model, which will be added to columns E & F. After these 2 Import Data actions have been added, our workflow now looks like this:
Now that the new customers and their demand have been imported into the model, we will add several actions to create a new scenario where the new customers will be included. In this scenario, we will also remove the Max Number of New Facilities value, so the Greenfield algorithm can optimize the number of new facilities just based on the costs specified in the model. After setting up the scenario, an action will be added to run it.
Use the Create Scenario action to add a new scenario to the model:
Then, use 2 Create Item Actions to 1) include the Ontario customers and 2) remove the Max Number Of New Facilities value:
After setting up the scenario and its 2 items, the next step of the workflow will be to run it. We add a Run Scenario action to the workflow to do so:
The configuration of this action takes following inputs:
We now have a workflow that connects to an existing US Greenfield model, adds Ontario customers and their demand to this model, then creates and runs a new scenario with 2 items in this Cosmic Frog model. After running the scenario, we want to export the Optimization Greenfield Facility Summary output table from the Cosmic Frog model and load it into a new worksheet in the App. We do so by adding an Export Data Action to the workflow:
After adding the above actions to the workflow, the workflow worksheet now looks like the following 2 screenshots from column G onwards (columns A-F contain the first 3 actions as shown in a screenshot further above):
Columns G-H contain the details of the action that created the new ON Customers Cost Optimized scenario, and columns I-J & K-L contain the details of the actions that added the 2 scenario items to this scenario.
Columns M-N contain the details of the action that will run the scenario that was added and columns O-P those of the action that will export the selected output table (Optimization Greenfield Facility Summary) into the GF_Facility_Summary worksheet of the App.
To run the completed Workflow, all we need to do is click on the Run Workflow action and confirm we want to run it:
After kicking off the workflow, if we switch to the Start worksheet, details of the run and its progress are shown in rows 9-11:
Looking on the Optilogic Platform, we can also check the progress of the App run and the Cosmic Frog model changes:
Once the run is done all 3 jobs will have their State changed to Done, unless an error occurred in which case the State will say Error.
Checking the United Stated Greenfield Facility Selection model itself in the Cosmic Frog application on cosmicfrog.com:
Once the App is finished running, we see that a worksheet named GF_Facility_Summary was added to the App Builder:
There are several other actions that users of the App Builder can incorporate into a workflow or use to facilitate workflow building. We will cover these now. Feel free to skip ahead to the “Deploying the App” section if your workflow is complete at this stage.
Additional actions that can be incorporated into workflows are the Run Utility, Upload File, and Download File actions. The Run Utility action can be used to run a Cosmic Frog Utility (a Python script), which currently can be a Utility downloaded from the Resource Library or a Utility specifically built for the App.
There are currently 4 Utilities available in the Resource Library:
After downloading the Python file of the Utility you want to use in your workflow, you need to copy it into the working_files_do_not_change folder that is located in the same folder as where you saved the App Builder. Now you can start using it as part of the Run Utility action. In the below example, we will use the Python script from the Copy Map to a Model Resource Library Utility to copy a map and all its settings from one model (“United States Greenfield Facility Selection”, the model connected to in a previous action) to another (“European Greenfield Facility Selection”):
The parameters of the Copy Dashboard to a Model Utility are the same as those of the Copy Map to a Model Utility:
The Orders to Demand and Delete SaS Scenarios utilities do not have any parameters that need to be set, so the Utility Params part of the Run Utility action can be left blank when using these utilities.
The Upload File action can be used to take a worksheet in the App Builder and upload it as a .csv file to the Optilogic platform:
Files that get uploaded to the Optilogic platform are placed in a specific working folder related to the App Builder, the name and location of which are shown in this screenshot:
The Download File action can be used to download a .txt file from the Optilogic platform and load it into a worksheet in the App:
Other actions that facilitate workflow building are the Move an Action, Delete an Action, and Run Actions actions, which will be discussed now. If the order of some actions needs to be changed, you do not need to remove and re-add them, you can use the Move an Action action to move them around:
It is also possible that an action needs to be removed from a Workflow. For this, the “Delete an Action” action can be used, rather than manually deleting it from the Workflow worksheet and trying to move other actions in its place:
Instead of running a complete workflow, it is also possible to only run a subset of the actions that are part of the workflow:
Once a workflow has been completed in the Cosmic Frog for Excel App Builder, it can be deployed so other users can run the same workflow without having to build it first. This section covers the Deployment steps.
The following message will come up after the app had been deployed:
Looking in the folder mentioned in this message, we see the following contents:
Congratulations on building & deploying your own Cosmic Frog for Excel App!
If you want to build Apps that go beyond what can be done using the App Builder, you can do so too. This may require some coding using Excel VBA, Python, and/or SQL. Detailed documentation walking through this can be found in this Getting Started with Cosmic Frog for Excel Applications article on Optilogic’s Help Center.
Hopper is the Transportation Optimization algorithm within Cosmic Frog. It designs optimal multi-stop routes to deliver/pickup a given set of shipments to/from customer locations at the lowest cost. Fleet sizing and balancing weekly demand can be achieved with Hopper too. Example business questions Hopper can answer are:
Hopper’s transportation optimization capabilities can be used in combination with network design to test out what a new network design means in terms of the last-mile delivery configuration. For example, questions that can be looked at are:
With ever increasing transportation costs, getting the last-mile delivery part of your supply chain right can make a big impact on the overall supply chain costs!
It is recommended to watch this short Getting Started with Hopper video before diving into the details of this documentation. The video gives a nice, concise overview of the basic inputs, process, and outputs of a Hopper model.
In this documentation we will first cover some general Cosmic Frog functionality that is used extensively in Hopper, next we go through how to build a Hopper model which discusses required and optional inputs, how to run a Hopper model is explained, Hopper outputs in tables, on maps and analytics are covered as well, and finally references to a few additional Hopper resources are listed. Note that the use of user-defined variables, costs and constraints for Hopper models is covered in a separate help article.
To not make this document too repetitive we will cover some general Cosmic Frog functionality here that applies to all Cosmic Frog technologies and is used extensively for Hopper too.
To only show tables and fields in them that can be used by the Hopper transportation optimization algorithm, disable all icons except the 4th (“Transportation”) in the Technologies Selector from the toolbar at the top in Cosmic Frog. This will hide any tables and fields that are not used by Hopper and therefore simplifies the user interface:
Many Hopper related fields in the input and output tables will be discussed in this document. Keep in mind however that a lot of this information can also be found in the tooltips that are shown when you hover over the column name in a table, see following screenshot for an example. The column name, technology/technologies that use this field, a description of how this field is used by those algorithm(s), its default value, and whether it is part of the table’s primary key are listed in the tooltip.
There are a lot of fields with names that end in “…UOM” throughout the input tables. How they work will be explained here so that individual UOM fields across the tables do not need to be explained further in this documentation as they all work similarly. These UOM fields are unit of measure fields and often appear to the immediate right of the field that they apply to, like for example Distance Cost and Distance Cost UOM in the screenshot above. In these UOM fields you can type the Symbol of a unit of measure that is of the required Type from the ones specified in the Units Of Measure table. For example, in the screenshot above, the unit of measure Type for the Distance Cost UOM field is Distance. Looking in the Units of Measure table, we see there are multiple of these specified, like for example Mile (Symbol = MI), Yard (Symbol = YD) and Kilometer (Symbol = KM), so we can use any of these in this UOM field. If we leave a UOM field blank, then the Primary UOM for that UOM Type specified in the Model Settings table will be used. For example, for the Distance Cost UOM field in the screenshot above the tooltip says Default Value = {Primary Distance UOM}. Looking this up in the Model Settings table shows us that this is set to MI (= mile) in our current model. Let’s illustrate this with the following screenshots of 1) the tooltip for the Distance Cost UOM field (located on the Transportation Assets table), 2) units of measure of Type = Distance in the Units Of Measure table and 3) checking what the Primary Distance UOM is set to in the Model Settings table, respectively:
Note that only hours (Symbol = HR) is currently allowed as the Primary Time UOM in the Model Settings table. This means that if another Time UOM, like for example minutes (MIN) or days (DAY), is to be used, the individual UOM fields need to be used to set these. Leaving them blank would mean HR is used by default.
With few exceptions, all tables in Cosmic Frog contain both a Status field and a Notes field. These are often used extensively to add elements to a model that are not currently part of the supply chain (commonly referred to as the “Baseline”), but are to be included in scenarios in case they will definitely become part of the future supply chain or to see whether there are benefits to optionally include these going forward. In these cases, the Status in the input table is set to Exclude and the Notes field often contains a description along the lines of ‘New Market’, ‘New Product’, ‘Box truck for Scenarios 2-4’, ‘Depot for scenario 5’, ‘Include S6’, etc. When creating scenario items for setting up scenarios, the table can then be filtered for Notes = ‘New Market’ while setting Status = ‘Include’ for those filtered records. We will not call out these Status and Notes fields in each individual input table in the remainder of this document, but we definitely do encourage users to use these extensively as they make creating scenarios very easy. When exploring any Cosmic Frog models in the Resource Library, you will notice the extensive use of these fields too. The following 2 screenshots illustrate the use of the Status and Notes fields for scenario creation: 1) shows several customers on the Customers table where CZ_Secondary_1 and CZ_Secondary_2 are not currently customers that are being served but we want to explore what it takes to serve them in future. Their Status is set to Exclude and the Notes field contains ‘New Market’; 2) a scenario item called ‘Include New Market’ shows that the Status of Customers where Notes = ‘New Market’ is changed to ‘Include’.
The Status and Notes fields are also often used for the opposite where existing elements of the current supply chain are excluded in scenarios in cases where for example locations, products or assets are going to go offline in the future. To learn more about scenario creation, please see this short Scenarios Overview video, this Scenario Creation and Maps and Analytics training session video, this Creating Scenarios in Cosmic Frog help article, and this Writing Scenario Syntax help article.
A subset of Cosmic Frog’s input tables needs to be populated in order to run Transportation Optimization, whereas several other tables can be used optionally based on the type of network that is being modelled, and the questions the model needs to answer. The required tables are indicated with a green check mark in the screenshot below, whereas the optional tables have an orange circle in front of them. The Units Of Measure and Model Settings tables are general Cosmic Frog tables, not only used by Hopper and will always be populated with default settings already; these can be added to and changed as needed.
We will first discuss the tables that are required to be populated to set up a basic Hopper model and then cover what can be achieved by also using the optional tables and fields. Note that the screenshots of all input and output tables mostly contain the fields in the order they appear in in the Cosmic Frog user interface, however on occasion the order of the fields was rearranged manually. So, if you do not see a specific field in the same location as in a screenshot, then please scroll through the table to find it.
The Customers table contains what for purposes of modelling are considered the customers: the locations that we need to deliver a certain amount of certain product(s) to or pick a certain amount of product(s) up from. The customers need to have their latitudes and longitudes specified so that distances and transport times of route segments can be calculated, and routes can be visualized on a map. Alternatively, users can enter location information like address, city, state, postal code, country and use Cosmic Frog’s built in geocoding tool to populate the latitude and longitude fields. If the customer’s business hours are important to take into account in the Hopper run, its operating schedule can be specified here too, along with customer specific variable and fixed pickup & delivery times. Following screenshot shows an example of several populated records in the Customers table:
The pickup & delivery time input fields can be seen when scrolling right in the Customers table (the accompanying UOM fields are omitted in this screenshot):
Finally, scrolling even more right, there are 3 additional Hopper-specific fields in the Customers table:
The Facilities table needs to be populated with the location(s) the transportation routes start from and end at; they are the domicile locations for vehicles (assets). The table is otherwise identical to the customers table, where location information can again be used by the geocoding tool to populate the latitude and longitude fields if they are not yet specified. And like other tables, the Status and Notes field are often used to set up scenarios. This screenshot shows the Facilities table populated with 2 depots, 1 current one in Atlanta, GA, and 1 new one in Jacksonville, FL:
Scrolling further right in the Facilities table shows almost all the same fields as those to the right on the Customers table: Operating Schedule, Operating Calendar, and Fixed & Unit Pickup & Delivery Times plus their UOM fields. These all work the same as those on the Customers table, please refer to the descriptions of them in the previous section.
The item(s) that are to be delivered to the customers from the facilities are entered into the Products table. It contains the Product Name, and again a Status and Notes fields for ease of scenario creation. Details around the Volume and Weight of the product are entered here too, which are further explained below this screenshot of the Products table where just one product “PRODUCT” has been specified:
On the Transportation Assets table, the vehicles to be used in the Hopper baseline and any scenario runs are specified. There are a lot of fields around capacities, route and stop details, delivery & pickup times, and driver breaks that can be used on this table, but there is no requirement to use all of them. Use only those that are relevant to your network and the questions you are trying to answer with your model. We will discuss most of them through multiple screenshots. Note that the UOM fields have been omitted in these screenshots. Let’s start with this screenshot showing basic asset details like name, number of units, domicile locations, and rate information:
The following screenshot shows the fields where the operating schedule of the asset, any fixed costs, and capacity of the vehicles can be entered:
Note that if all 3 of these capacities are specified, the most restrictive one will be used. If you for example know that a certain type of vehicle always cubes out, then you could just populate the Volume Capacity and Volume Capacity UOM fields and leave the other capacity fields blank.
If you scroll further right, you will see the following fields that can be used to set limits on route distance and time when using this type of vehicle. Where applicable, you will notice their UOM fields too (omitted in the screenshot):
Limits on the amount of stops per route can be set too:
A tour is defined as all the routes a specific unit of a vehicle is used on during the model horizon. Limits around routes, time, and distance for tours can be added if required:
Scrolling still further right you will see the following fields that can be used to add details around how long pickup and delivery take when using this type of vehicle. These all have their own UOM fields too (omitted in the screenshot):
The next 2 screenshots shows the fields on the Transportation Assets table where rules around driver duty, shift, and break times can be entered. Note that these fields each have a UOM field that is not shown in the screenshot:
Limits around out of route distance can be set too. Plus details regarding the weight of the asset itself and the level of CO2 emissions:
Lastly, a default cost, fixed times for admin, and an operating calendar can be specified for a vehicle in the following fields on the transportation assets table:
As a reference, these are the department of transportation driver regulations in the US and the EU. They have been somewhat simplified from these sources: US DoT Regulations and EU DoT Regulations:
Consider this route that starts from the DC, then goes to CZ1 & CZ2, and then returns to the DC:
The activities on this route can be thought of as follows, where the start of the Rest is the end of Shift 1 and Shift 2 starts at the end of the Rest:
Notes on Driver Breaks:
Except for asset fixed costs, which are set on the Transportation Assets table, and any Direct Costs which are set on the Shipments table, all costs that can be associated with a multi-stop route can be specified in the Transportation Rates table. The following screenshot shows how a transportation rate is set up with a name, a destination name and the first several cost fields. Note that UOM fields have been omitted in this screenshot, but that each cost field has its own UOM field to specify how the costs should be applied:
Scrolling further right in the Transportation Rates table we see the remaining cost fields:
Finally, a minimum charge and fuel surcharge can be specified as part of a transportation rate too:
The amount of product that needs to be delivered from which source facility/supplier to which destination customer or picked up from which customer is specified on the Shipments table. Optionally, details around pickup and delivery times, direct costs, and fixed template routes can be set on this table too. Note that the Shipments table is Transportation Asset agnostic, meaning that the Hopper transportation optimization algorithm will choose the optimal one to use from the vehicles domiciled at the source location. This first screenshot of the Shipments table shows the basic shipment details:
Here is an example of a subset of Shipments for a model that will route both pickups and deliveries:
To the right in the Shipments table we find the fields where details around shipment windows can be entered:
Still further right on the Shipments table are the fields where details around pickup and delivery times can be specified:
Finally, furthest right on the Shipments table are fields where Direct Costs, details around Template Routes and decompositions can be configured:
Note that there are multiple ways to switching between forcing Shipments and the order of stops onto a template route and letting Hopper optimize which shipments will be put on a route together and in which order. Two example approaches are:
The tables and their input fields that can optionally be populated for their inputs to be used by Hopper will now be covered. Where applicable, it will also be mentioned how Hopper will behave when these are not populated.
In the Transit Matrix table, the transport distance and time for any source-destination-asset combination that could be considered as a segment of a route by Hopper can be specified. Note that the UOM fields in this table are omitted in following screenshot:
The transport distances for any source-destination pairs that are not specified in this table will be calculated based on the latitudes and longitudes of the source and destination and the Circuity Factor that is set in the Model Settings table. Transport times for these pairs will be calculated based on the transport distance and the vehicle’s Speed as set on the Transportation Assets table or, if Speed is not defined on the Transportation Assets table, the Average Speed in the Model Settings table.
Costs that need to be applied on a stop basis can be specified in the Transportation Stop Rates table:
If Template Routes are specified on the Shipments table by using the Template Route Name and Template Route Stop Sequence fields, then the Template Routes table can be used to specify if and how insertions of other Shipments can be made into these template routes:
If a template route is set up by using the Template Route Name and Template Route Stop Sequence fields in the Shipments table and this route is not specified in the Template Routes table, it means that no insertions can be made into this template route.
In addition to routing shipments with a fixed amount of product to be delivered to a customer location, Hopper can also solve problems where routes throughout a week need to be designed to balance out weekly demand while achieving the lowest overall routing costs. The Load Balancing Demand and Load Balancing Schedules tables can be used to set this up. If both the Shipments table and the Load Balancing Demand/Schedules tables are populated, by default the Shipments table will be used and the Load Balancing Demand/Schedules tables will be ignored. To switch to using the Load Balancing Demand/Schedules tables (and ignoring the Shipments) table, the Run Load Balancing toggle in the Hopper (Transportation Optimization) Parameters section on the Run screen needs to be switched to on (toggle to the left and grey is off; to the right and blue is on):
The weekly demand, the number of deliveries per week, and, optionally, a balancing schedule can be specified in the Load Balancing Demand table:
To balance demand over a week according to a schedule, these schedules can be specified in the Load Balancing Schedules table:
In the screenshots above, the 3 load balancing schedules that have been set up will spread the demand out as follows:
In the Relationship Constraints table, we can tell Hopper what combinations of entities are not allowed on the same route. For example, in the screenshot below we are saying that customers that make up the Primary Market cannot be served on the same route as customers from the Secondary Market:
A few examples of common Relationship Constraints are shown in the following screenshot where the Notes field explains what the constraint does:
To set the availability of customers, facilities, and assets to certain start and end times by day of the week, the Business Hours table can be used. The Schedule Name specified on this table can then be used in the Operating Schedule fields on the Customers, Facilities and Transportation Assets tables. Note that the Wednesday – Saturday Open Time and Close Time fields are omitted in the following screenshot:
To schedule closure of customers, facilities, and assets on certain days, the Business Calendars table can be used. The Calendar Name specified on this table can then be used in the Operating Calendar fields on the Customers, Facilities and Transportation Assets tables:
Groups are a general Cosmic Frog feature to make modelling quicker and easier. By grouping elements that behave the same together in a group we can reduce the number of records we need to populate in certain tables since we can use the Group names to populate the fields instead of setting up multiple records for each individual element which will all have the same information otherwise. Underneath the hood, when a model that uses Groups is run, these Groups are enumerated into the individual members of the group. We have for example already seen that groups of Type = Customers were used in the Relationship Constraints table in the previous section to prevent customers in the Primary Market being served on the same route as customers in the Secondary Market. Looking in the Groups table we can see which customers are part (‘members’) of each of these groups:
Examples of other Hopper input tables where use of Groups can be convenient are:
Note that in addition to Groups, Named Filters can be used in these instances too. Learn more about Named Filters in this help center article.
The Step Costs table is a general table in Cosmic Frog used by multiple technologies. It is used to specify costs that change based on the throughput level. For Hopper, all cost fields on the Transportation Rates table, the Transportation Stop Rates table, and the Fixed Cost on the Transportation Assets table can be set up to use Step Costs. We will go through an example of how Step Costs are set up, associated with the correct cost field, and how to understand outputs using the following 3 screenshots of the Step Costs table, Transportation Rates table and Transportation Route Summary output table, respectively. The latter will also be discussed in more detail in the next section on Hopper outputs.
In this example, the per unit cost for units 0 through 20 is $1, $0.9 for units 21 through 40, and $0.85 for all units over 40. Had the Step Cost Behavior field been set to All Item, then the per unit cost for all items is $1 if the throughput is between 0 and 20 units, the per unit cost for all items is $0.9 if the throughput is between 21 and 40 units, and the per unit cost for all items is $0.85 if the throughput is over 41 units.
In this screenshot of the Transportation Rates table, it is shown that the Unit Cost field is set to UnitCost_1 which is the stepped cost with 3 throughput levels that we just discussed in the screenshot above:
Lastly, this is a screenshot of the Transportation Route Summary output table where we see that the Delivered Quantity on Route 1 is 78. With the stepped cost structure as explained above for UnitCost_1, the Unit Cost in the output is calculated as follows: 20 * $1 (for units 1-20) + 20 * $0.9 (for units 21-40) + 38 * $0.85 (for units 41-78) = $20 + $18 + $32.30 = $70.30.
When the input tables have been populated and scenarios are created (several resources explaining how to set up and configure scenarios are listed in the “2.4 Status and Notes fields” section further above), one can start a Hopper run by clicking on the Run button at the top right in Cosmic Frog:
The Run screen will come up:
Once a Hopper run is completed, the Hopper output tables will contain the outputs of the run.
As with other Cosmic Frog algorithms, we can look at Hopper outputs in output tables, on maps and analytics dashboards. We will discuss each of these in the next 3 sections. Often scenarios will be compared to each other in the outputs to determine which changes need to be made to the last-mile delivery part of the supply chain.
In the Output Summary Tables section of the Output Tables are 8 Hopper specific tables, they start with “Transportation…”. Plus, there is also the Hopper specific detailed Transportation Activity Report table in the Output Report Tables section:
Switch from viewing Input Tables to Output Tables by clicking on the round grid at the top right of the tables list. The Transportation Summary table gives a high-level summary of each Hopper scenario that has been run and the next 6 Summary output tables contain the detailed outputs at the route, asset, shipment, stop, segment, and tour level. The Transportation Load Balancing Summary output table is populated when a Load Balancing scenario has been run, and summarizes outputs at the daily level. The Transportation Activity Report is especially useful to understand when Rests and Breaks are required on a route. All these output tables will be covered individually in the following sections.
The Transportation Summary table contains outputs for each scenario run that include Hopper run details, cost details, how much product was delivered and how, total distance and time, and how many routes, stops and shipments there were in total.
The Hopper run details that are listed for each scenario include:
The next 2 screenshots show the Hopper cost outputs, summarized by scenario:
Scrolling further right in the Transportation Summary table shows the details around how much product was delivered in each scenario:
For the Quantity UOM that is shown in the farthest right column in this screenshot (eaches here), the Total Delivered Quantity, Total Direct Quantity and Total Undelivered Quantity are listed in these columns. If the Total Direct Quantity is greater than 0, details around which shipments were delivered directly to the customer can be found in the Transportation Shipment Summary output table where the Shipment Status = Direct Shipping. Similarly, if the total undelivered quantity is greater than 0, then more details on which shipments were not delivered and why are detailed in the Unrouted Reason field of the Transportation Shipment Summary output table where the Shipment Status = Unrouted.
The next set of output columns when scrolling further right repeat these delivered, direct and undelivered amounts by scenario, but in terms of volume and weight.
Still further to the right we find the outputs that summarize the total distance and time by scenario:
Lastly, the fields furthest right on the Transportation Summary output table contain details around the number of routes, assets and shipments, and CO2 emissions:
A few columns contained in this table are not shown in any of the above screenshots, these are:
The Transportation Route Summary table contains details for each route in each scenario that include cost, distance & time, number of stops & shipments, and the amount of product delivered on the route.
The costs that together make up the total Route Cost are listed in the next 11 fields shown in the next 2 screenshots:
The next set of output fields show the distance and time for each route:
Finally, the fields furthest right in the Transportation Route Summary table list the amount of product that was delivered on the routes, and the number of stops and delivered shipments on each route.
The Transportation Asset Summary output table contains the details of each type of asset used in each scenario. These details include costs, amount of product delivered, distance & time, and the number of delivered shipments.
The costs that together make up the Total Cost are listed in the next 12 fields:
The next set of fields in the Transportation Asset Summary summarize the distances and times by asset type for the scenario:
Furthest to the right on the Transportation Asset Summary output table we find the outputs that list the total amount of product that was delivered, the number of delivered shipments, and the total CO2 emissions:
The Transportation Shipment Summary output table lists for each included Shipment of the scenario the details of which asset type it is served by, which stop on which route it is, the amount of product delivered, the allocated cost, and its status.
The next set of fields in the Transportation Shipment Summary table list the total amount of product that was delivered to this stop.
The next screenshot of the Transportation Shipment Summary shows the outputs that detail the status of the shipment, costs, and a reason in case the shipment was unrouted.
Lastly, the outputs furthest to the right on the Transportation Shipment Summary output table list the pickup and delivery time and dates, the allocation of CO2 emissions and associated costs, and the Decomposition Name if used:
The Transportation Stop Summary output table lists for each route all the individual stops and their details around amount of product delivered, allocated cost, service time, and stop location information.
This first screenshot shows the basic details of the stops in terms of route name, stop ID, location, stop type, and how much product was delivered:
Somewhat further right on the Transportation Stop Summary table we find the outputs that detail the route cost allocation and the different types of time spent at the stop:
Lastly, farthest right on the Transportation Stop Summary table, arrival, service, and departure dates are listed, along with the stop’s latitude and longitude:
The Transportation Segment Summary output table contains distance, time, and source and destination location details for each segment (or “leg”) of each route.
The basic details of each segment are shown in the following screenshot of the Transportation Segment Summary table:
Further right on the Transportation Segment Summary output table, the time details of each segment are shown:
Next on the Transportation Segment Summary table are the latitudes and longitudes of the segment’s origin and destination locations:
And farthest right on the Transportation Segment Summary output table details around the start and end date and time of the segment are listed, plus CO2 emissions and the associated CO2 cost:
For each Tour (= asset schedule) the Transportation Tour Summary output table summarizes the costs, distances, times, and CO2 details.
The next 3 screenshots show the basic tour details and all costs associated with a tour:
The next screenshot shows the distance outputs available for each tour on the Transportation Tour Summary output table:
Scrolling further right on the Transportation Tour Summary table, the outputs available for tour times are listed:
If a load balancing scenario has been run (see the Load Balancing Demand input table further above for more details on how to run this), then the Transportation Load Balancing Summary output table will be populated too. Details on amount of product delivered, plus the number of routes, assets and delivered shipments by day of the week can be found in this output table; see the following 2 screenshots:
For each route, the Transportation Activity Report details all the activities that happen in chronological order with details around distance and time and it breaks down how far along the duty and drive times are at each point in the route, which is very helpful to understand when rests and short breaks are happening.
This first screenshot of the Transportation Activity Report shows the basic details of the activities:
Next, the distance, time, and delivered amount of product are detailed on the Transportation Activity Report:
Finally, the last several fields on the Transportation Activity Report details cost, and the thus far accumulated duty and drive times:
As with other algorithms within Cosmic Frog, Maps are very helpful in visualizing baseline and scenario outputs. Here, we will do a step by step walk through of setting up a Hopper specific Map and not cover all the ins and outs of maps. If desired, you can review these resources on Maps in general first:
We will first cover the basics of what we need to know to set up a Hopper specific map:
Click on the Map drop-down to view all options in the list:
After adding a new Map or when selecting an existing Map in the Maps list, the following view will be shown on the right-hand side of the map:
After adding a new Layer to a Map or when selecting an existing Layer in a Map, the following view will be shown on the right-hand side of the map:
By default, the Condition Builder view is shown:
There is also a Conditions text field which is not shown in the screenshot as it is covered by the Table Name drop-down. A filter (“condition”) can be typed into the Conditions text field to only show the records of the table that match the filter. For example, typing “CustomerName like ‘%Secondary%’” in the Conditions field, will only show customers where the Customer Name contains the text ‘Secondary’ anywhere in the name. You can learn more about building conditions in this Writing Syntax for Conditions help article.
Switching from Condition Builder to Layer Style shows the following:
Here, following is shown / configurable:
Switching from Layer Style to Layer Labels shows the following:
Using what we have discussed above, we can create the following map quite easily and quickly (the model used here is one from the Resource Library, named Transportation Optimization):
The steps taken to create this map are:
Let’s also cover 2 maps of a model where both pickups and deliveries are being made, from “backhaul” and to “linehaul” customers, respectively. When setting the LIFO (Is Last In First Out) field on the Transportation Assets table to True, this leads to routes that contain both pickup and delivery stops, but all the pickups are made at the end (e.g. modeling backhaul):
Two example routes are being shown in the screenshot above and we can see that all deliveries are first made to the linehaul customers which have blue icons. Then, pickups are made at the backhaul customers which have orange icons. If we want to design interleaved routes where pickups and deliveries can be mixed, we need to set the LIFO field to False. The following screenshot shows 2 of these interleaved routes:
In the Analytics module of Cosmic Frog, dashboards that show graphs of scenario outputs, sliced and diced to the user’s preferences, can quickly be configured. Like Maps, this functionality is not Hopper specific and other Cosmic Frog technologies use these extensively too. We will cover setting up a Hopper specific visualization, but not all the details of configuring dashboards. Please review these resources on Analytics in Cosmic Frog first if you are not yet familiar with these:
We will do a quick step by step walk through of how to set up a visualization of comparing scenario costs by cost type in a new dashboard:
The steps to set this up are detailed here, note that the first 4 bullet points are not shown in the screenshot above:
There are several models in the Resource Library that transportation optimization users may find helpful to review. How to use resources in the Resource Library is described in the help center article “How to Use the Resource Library”.
Teams is an exciting new feature set designed to enhance collaboration within Supply Chain Design, enabling companies to foster a more connected and efficient working environment. With Teams, users can join a shared workspace where all team members have seamless access to collective models and files. This ensures that every piece of work remains synchronized, providing a single source of truth for your data. When one team member updates a file, those changes instantly reflect for all other members, eliminating inconsistencies and ensuring that everyone stays aligned.
Beyond simply improving collaboration, Teams offers a structured and flexible way to organize your projects. Instead of keeping all your files and models confined to a personal account, you can now create distinct teams tailored to different projects, departments, or business functions. This means greater clarity and easier navigation between workspaces, ensuring that the right content is always at your fingertips.
Consider the possibilities:
Teams introduces a more intuitive and structured way to collaborate, organize, and access your work—ensuring that your team members always have the latest updates and a streamlined experience. Get started today and transform the way you work together!
This documentation contains a high-level overview of the Teams feature set, details the steps to get started, gives examples of how Teams can be structured, and covers best practices. More detailed documentation for Organization Administrators and Teams Users is available in the following help center articles:
The diagram below highlights the main building blocks of the Teams feature set:
At a high-level, these are the steps to start using the Teams feature set:
Here follow 5 examples of how teams can be structured, including an example for each and an explanation of why such a setup works well.
Please keep following best practices in mind to ensure optimal use of the Teams feature set:
Once you have set up your teams and added content, you are ready to start collaborating and unlocking the full potential of Teams within Optilogic!
Let us know if you need help along the way—our support team (support@optilogic.com) has your back.
Depending on the type of supply chain one is modelling in Cosmic Frog and the questions being asked of it, it may be necessary to utilize some or all the features that enable detailed production modelling. A few business case examples that will often include some level of detailed production modelling include:
In comparison, modelling a retailer who buys all its products from suppliers as finished goods, does not require any production details to be added to its Cosmic Frog model. Hybrid models are also possible, think for example of a supermarket chain which manufactures its own branded products and buys other brands from its suppliers. Depending on the modelling scope, the production of the own branded products may require using some of the detailed production features.
The following diagram shows a generalized example of production related activities at a manufacturing plant, all of which can be modelled in Cosmic Frog:
In this help article we will cover the inputs & outputs of Cosmic Frog’s production modelling features, while also giving some examples of how to model certain business questions. The model in Optilogic’s Resource Library that is used mainly for the screenshots in this article is the Multi-Year Capacity Planning. There is a 20-minute video available with this model in the Resource Library, which covers the business case that is modelled and some detail of the production setup too.
To not make this document too repetitive we will cover some general Cosmic Frog functionality here that applies to all Cosmic Frog technologies and is used extensively for production modelling in Neo too.
To only show tables and fields in them that can be used by the Neo network optimization algorithm, select Optimization in the Technologies Filter from the toolbar at the top in Cosmic Frog. This will hide any tables and fields that are not used by Neo and therefore simplifies the user interface.
Quite a few Neo related fields in the input and output tables will be discussed in this document. Keep in mind however that a lot of this information can also be found in the tooltips that are shown when you hover over the column name in a table, see following screenshot for an example. The column name, technology/technologies that use this field, a description of how this field is used by those algorithm(s), its default value, and whether it is part of the table’s primary key are listed in the tooltip.
There are a lot of fields with names that end in “…UOM” throughout the input tables. How they work will be explained here so that individual UOM fields across the tables do not need to be explained further in this documentation as they all work similarly. These UOM fields are unit of measure fields and often appear to the immediate right of the field that they apply to, like for example Unit Value and Unit Value UOM in the screenshot above. In these UOM fields you can type the Symbol of a unit of measure that is of the required Type from the ones specified in the Units Of Measure input table. For example, in the screenshot above, the unit of measure Type for the Unit Value UOM field is Quantity. Looking in the Units Of Measure input table, we see there are 2 of these specified: Each and Pallet, with Symbol = EA and PLT, respectively. We can use either of these in this UOM field. If we leave a UOM field blank, then the Primary UOM for that UOM Type specified in the Model Settings input table will be used. For example, for the Unit Value UOM field in the screenshot above the tooltip says Default Value = {Primary Quantity UOM}. Looking this up in the Model Settings table shows us that this is set to EA (= each) in our current model. Let’s illustrate this with the following screenshots of 1) the tooltip for the Unit Value UOM field (located on the Products input table), 2) units of measure of Type = Quantity in the Units Of Measure input table and 3) checking what the Primary Quantity UOM is set to in the Model Settings input table, respectively:
Note that only hours (Symbol = HR) is currently allowed as the Primary Time UOM in the Model Settings table. This means that if another Time UOM, like for example minutes (MIN) or days (DAY), is to be used, the individual UOM fields need to be utilized to set these. Leaving these blank would mean HR is used by default.
With few exceptions, all tables in Cosmic Frog contain both a Status field and a Notes field. These are often used extensively to add elements to a model that are not currently part of the supply chain (commonly referred to as the “Baseline”), but are to be included in scenarios in case they will definitely become part of the future supply chain or to see whether there are benefits to optionally include these going forward. In these cases, the Status in the input table is set to Exclude and the Notes field often contains a description along the lines of ‘New Market’, ‘New Line 2026’, ‘Alternative Recipe Scenario 3, ‘Faster Bottling Plant5 China’, ‘Include S6’, etc. When creating scenario items for setting up scenarios, the table can then be filtered for Notes = ‘New Market’ while setting Status = ‘Include’ for those filtered records. We will not call out these Status and Notes fields in each individual input table in the remainder of this document, but we do encourage users to use these extensively as they make creating scenarios very easy. When exploring any Cosmic Frog models in the Resource Library, you will notice the extensive use of these fields too. The following 2 screenshots illustrate the use of the Status and Notes fields for scenario creation: 1) shows several customers on the Customers table where CZ_Secondary_1 and CZ_Secondary_2 are not currently customers that are being served but we want to explore what it takes to serve them in future. Their Status is set to Exclude and the Notes field contains ‘New Market’; 2) a scenario item called ‘Include New Market’ shows that the Status of Customers where Notes = ‘New Market’ is changed to ‘Include’.
The Status and Notes fields are also often used for the opposite where existing elements of the current supply chain are excluded in scenarios in cases where for example manufacturing locations, products or lines are going to go offline in the future. To learn more about scenario creation, please see this short Scenarios Overview video, this Scenario Creation and Maps and Analytics training session video, this Creating Scenarios in Cosmic Frog help article, and this Writing Scenario Syntax help article.
The model that is mostly used for screenshots throughout this help article is as mentioned above the Multi-Year Capacity Planning model that can be found here in the Resource Library. This model represents a European cheese supply chain which is used to make investment decisions around the growth of a non-mature market in Eastern Europe over a 5-year modelling horizon. New candidate DCs are considered to serve the growing demand in Eastern Europe, the model optimizes which are optimal to open and during which of the 5 years of the modelling horizon. The production setup in the model uses quite a few of the detailed modelling features which will be discussed in detail in this document:
Note that in the screenshots of this model, the columns have been re-ordered sometimes, so you may see a different order in your Cosmic Frog UI when opening the same tables of this model.
The 2 screenshots below show the Products and Facilities input tables of this model in Cosmic Frog:
Note that the naming convention of the products lends itself to easy filtering of the table for the raw materials, bulk materials, and finished goods due to the RAW_, BULK_, and FG_ prefixes. This makes the creation of groups and setting up of scenarios quick and easy.
Note that similar to the naming convention of the products, the facilities are also named with prefixes that facilitate filtering of the facilities so groups and scenarios can quickly be created.
Here is a visual representation of the model with all facilities and customers on the map:
The specific features in Cosmic Frog that allow users to model and optimize production processes of varying levels of complexity while using the network optimization engine (Neo) include the following input tables:
We will cover all these production related input tables to some extent in this article, starting with a short description of each of the basic single-period input tables:
These 4 tables feed into each other as follows:
A couple of notes on how these tables work together:
For all products that are explicitly modelled in a Cosmic Frog model, there needs to be at least 1 policy specified on the Production Policies table or the Supplier Capabilities table so there is at least 1 origin location for each. This applies to for example raw materials, intermediates, bulk materials, and finished goods. The only exception is if by-products are being modelled, these can have Production Policies associated with them, but do not necessarily need to (more on this when discussing Bills of Materials further below). From the 2 screenshots below of the Production Policies table, it becomes clear that depending on the type of product and the level of detail that is needed for the production elements of the supply chain, production policies can be set up quite differently: some use only a few of the fields, while others use more/different fields.
A couple of notes as follows:
Next, we will look at a few other records on the Production Policies input table:
We will take a closer look at the BOMs and Processes specified on these records when discussing the Bills of Materials and Processes tables further below.
Note that the above screenshot was just for PLT_1 and mozzarella, there are similar records in this model for the other 4 cheeses which can also be made at PLT_1, plus similar records for all 5 cheeses at PLT_2, which includes a new potential production line for future expansion too.
Other fields on the Production Policies table that are not shown in the above 2 screenshots are:
The recipes of how materials/products of different stages convert into each other are specified on the Bills of Materials (BOMs) table. Here the BOMs for the blue cheese (_BLU) products are shown:
Note that the above specified BOMs are both location and end-product agnostic. Their names suggest that they are specific for making the BULK_BLU and FG_BLU products, but only associating these BOMs on a Production Policy which has Product Name set to these makes this connection. We can use these BOMs at any location that they apply. Filtering the Production Policies table for the BULK_BLU and FG_BLU products we can see that 1) BOM_BULK_BLU is indeed used to make BULK_BLU and BOM_FG_BLU to make FG_BLU, and 2) the same BOMs are used at PLT_1 and PLT_2:
It is of course possible that the same product uses a different BOM at a different location. In this case, users can set up multiple BOMs for this product on the BOMs table and associate the correct one at the correct location in the Production Policies table. Choosing a naming convention for the BOM Names that includes the location name (or a code to indicate it) is recommended.
The screenshot above of the Bills of Materials table only shows records with Product Type = Component. Components are input into a BOM and are consumed by it when producing the end-product. Besides Component, Product Type can also be set to End Product or Byproduct. We will explain these 2 product types through the examples in this following screenshot:
Notes:
On the Processes table production processes of varying levels of complexity can be set up, from simple 1 step processes without using any work centers, to multi-step ones that specify costs, processing rates, and use different work centers for each step. The processes specified in the Multi-Year Capacity Planning model are relatively straightforward:
Let us also look at an example in a different model which contains somewhat more complex processes for a car manufacturer where the production process can roughly be divided into 3 steps:
Note that, like BOMs, Processes can in theory be both location and end-product agnostic. However:
Other fields on the Processes table that are not shown in the above 2 screenshots are:
If it is important to capture costs and/or capacities of equipment like production lines, tools, machines that are used in the production process, these can be modelled by using work centers to represent the equipment:
In the above screenshot, 2 work centers are set up at each plant: 1 existing work center and 1 new potential work center. The new work centers (PLT_1_NewLine and PLT_2_NewLine) have Work Center Status set to Closed, so they will not be considered for inclusion in the network when running the Baseline scenario. In some of the scenarios in the model, the Work Center Status of these 2 lines is changed to Consider and in these scenarios one of the new lines or both can be opened and used if it is optimal to do so. The scenario item that makes this change looks like this:
Next, we will also look at a few other fields on the Work Centers table that the Multi-Year Capacity Planning model utilizes:
In theory, it can be optimal for a model to open a considered potential work center in one period of the model (say 2024 in this model), close it again in a later period (e.g. 2025), for it then to open it again later (e.g. 2026), etc. In this case Fixed Startup or Fixed Closing Costs would be applied each time the work center was opened or closed, respectively. This type of behavior can be undesirable and is by default prevented by a Neo Run Parameter called “Open Close At Most Once”, as shown in this screenshot:
After clicking on the Run button, the Run screen comes up. The “Open Close At Most Once” parameter can be found in the Neo (Optimization) Parameters section. By default, it is turned on, meaning that a work center or facility is only allowed to change state once during the model’s horizon, i.e. once from closed to open if the Initial State = Potential or once from open to closed if the Initial State = Existing. There may however be situations where opening and/or closing of work centers and facilities multiple times during the model horizon is allowable. In that case, the Open Close At Most Once parameter can be turned off.
Other fields on the Work Centers table that are not shown in the above screenshots are:
Fixed Operating, Fixed Startup, and Fixed Closing Costs can be stepped costs. These can be entered into the fields on the Work Centers input table directly or can be specified on the Step Costs input table and then used on the Work Centers table in those cost fields. An example of stepped costs set up in the Step Costs input table is shown in the screenshot below where the costs are set up to capture the weekly shift cost for 1 person (note that these stepped costs are not in the Multi-Year Capacity Planning model in the Resource Library, they are shown here as an additional example):
To set for example the Fixed Operating Cost to use this stepped cost, type “WC_Shifts” into the Fixed Operating Cost field on the Work Centers input table.
Many of the input tables in Cosmic Frog have a Multi-Time Period equivalent, which can be used in models that have more than 1 period. These tables enable users to make changes that only apply to specific periods of the model. For example, to:
The multi-time period tables are copies of their single-period equivalents, with a few columns added and removed (we will see examples of these in screenshots further below):
Notes on switching status of records through the multi-period tables and updating records partially:
Three of the 4 production specific input tables that have been discussed above have a multi-time period equivalent: Production Policies, Processes, and Work Centers. There is no equivalent for the Bills Of Materials input table, as BOMs are only used if they are associated on records in the Production Policies table. Using different BOMs during different periods can be achieved by associating those BOMs on the Production Policies single-period table and setting the Status of them to Include for those to be used for most of the periods and to Exclude if they are to be included for certain periods / scenarios. Then add those records for which the Status needs to be switched to the Production Policies multi-period input table (we will walk through an example of this using screenshots in the next section).
The 3 production specific multi-time period input tables do have all of the same fields as their single-period equivalents, with the addition of the Period Name field and additional Status field. We will not discuss each multi-time period table and all its fields in detail here, but rather give a few examples of how each can be used.
Note that from this point onwards the Multi-Year Capacity Planning model was modified and added to for purposes of this help article, the version in the Resource Library does not contain the same data in the Multi-Time Period input tables and production specific Constraint tables that is shown in the screenshots below.
This first example on the Production Policies Multi-Time Period input table shows how the production of the cheddar finished good (FG_CHE) is prevented at plant 1 (PLT_1) in years 4 and 5 of the model:
In the following example, an alternative BOM to make feta (FG_FET) is added and set to be used at Plant 2 (PLT_2) during all periods instead of the original BOM. This is set up to be used in a scenario, so the original records need to be kept intact for the Baseline and other scenarios. To set this up, we need to update the Bills Of Materials, Production Policies, and Production Policies Multi-Time Period table, see the following screenshots and explanations:
On the Bills Of Materials input table, all we need to do is add the records for the new BOM that results in FG_FET. It has 2 records, both named ALTBOM_FG_FET, and instead of using only BULK_FET as the component which is what the original BOM uses, it uses a mix of BULK_FET and BULK_BLU as its components.
Next, we first need to associate this new BOM through the Production Policies table:
Lastly, the records that need to be added to the Production Policies Multi-Time Period table are the following 4 which have all the same values for the key columns as the 4 records in the above screenshot of the Production Policies single-period input table, which contain all the possible ways to produce FG_FET at PLT_2:
In the following example, we want to change the unit cost on 2 of the processes: at Plant 1 (PLT_1), the cost on the new potential line needs to be decreased to 0.005 for cheddar cheese (CHE) and increased to 0.015 for Swiss cheese (SWI). This can be done by using the Processes Multi-Time Period input table:
Note that there is also a Work Center Name field on the Processes Multi-Time Period table (not shown in the screenshot). As this is not a key field on the Processes input tables, it can be left blank here on the multi-time period table. This field will not be changed and the value from the single-time period table Work Center Name field will be used for these 2 records.
In the following example, we want to evaluate if upgrading the existing production lines at both plants from the 3rd year of the modelling horizon onwards, so they have a higher throughput capacity at a somewhat higher fixed operating cost, is a good alternative to opening one of the potential new lines at either plant. First, we add a new periods group to the model to set this up:
On the Groups table, we set up a new group named YEARS3-5 (Group Name) that is of Group Type = Periods and has 3 members: YEAR3, YEAR4 and YEAR5 (Member Name).
Cosmic Frog contains multiple tables through which different types of constraints can be added to network optimization (Neo) models. A constraint limits the model in a certain part of the network. These limits can for example be lower or upper limits in terms of the amount of flow between certain locations or certain echelons, the amount of inventory of a certain product or product group at a specific location or network wide, the amount of production of a certain product or product group at a specific location or network wide, etc. In this section the 3 constraints tables that are production specific will be covered: Production Constraints, Production Count Constraints, and Work Center Count Constraints.
A couple of general notes on all constraints tables:
In this example, we want to add constraints to the model that limit the production of all 5 finished goods together to 90,000 units. Both plants have this same upper production limit across the finished goods, and the limit applies to each year of the modelling horizon (5 yearly periods).
Note that there are more fields on the Production Constraints input table which are not shown in the above screenshot. These are:
In this example, we want to limit the number of products that are produced at PLT_1 to a maximum of 3 (out of the 5 finished goods). This limit applies over the whole 5-year modelling period, meaning that in total PLT_1 can produce no more than 3 finished goods:
Again, note there are more fields on the Production Count Constraints input table which are not shown in the above screenshot. These are:
Next, we will show an example of how to open at least 3 work centers, but no more than 5 out of 8 candidate work centers. These limits apply to all 5 yearly periods in the model together and over all facilities present in the model.
Again, there are more fields on the Work Center Count Constraints table that are not shown in the above screenshot:
After running a network optimization using Cosmic Frog’s Neo technology, production specific outputs can be found in several of the more general output tables, like the Optimization Network Summary, and the Optimization Constraints Summary (if any constraints were applied). Outputs more focused on just production can be found in 4 production specific output tables: the Optimization Production Summary, the Optimization Bills Of Material Summary, the Optimization Process Summary, and the Optimization Work Center Summary. We will cover these tables here, starting with the Optimization Network Summary.
The following screenshot shows the production specific outputs that are contained in the Optimization Network Summary output table:
Other production related fields on this table which are not shown in the screenshot above are:
The Optimization Production Summary output table has a record with the production details for each product that was produced as part of the model run:
Other fields on this output table which are not shown in the screenshot are:
The details of how many components were used and how much by-product produced as a result of any bills of materials that were used as part of the production process can be found on the Optimization Bills Of Material Summary output table:
Note that aside from possibly knowing based on the BOM Name, it is not listed in the Bills Of Material Summary output table what the end product is and how much of it is produced as a result of a BOM. Those details are contained in the Optimization Production Summary output table discussed above.
Other fields on this output table which are not shown in the screenshot are:
The details of all the steps of any processes used as part of the production in the Neo network optimization run can be found in the Optimization Process Summary, see these next 2 screenshots:
Other fields on this output table which are not shown in the screenshots are:
For each Work Center that has its Status set to Include or Consider, a record for each period of the model can be found in the Optimization Work Center Summary output table. It summarizes if the Work Center was used during that period, and, if so, how much and at what cost:
The following screenshot shows a few more output fields on the Optimization Work Center Summary output tables that have non-0 values in this model:
Other fields on this output table which are not shown in the screenshots are:
For all constraints in the model, the Optimization Constraint Summary can be a very handy table to check if any constraints are close to their maximum (or minimum, etc.) value to understand where the current and future bottlenecks are and likely will be. The screenshot below shows the outputs on this table for a production constraint that is applied at each of the 3 suppliers, where neither can produce more than 1 million units of RAW_MILK in any 1 year. In the screenshot we specifically look at the Supplier named SUP_3:
Other fields on this output table which are not shown in the screenshots are:
There are a few other output tables of which the main outputs are not related to production, but still contain several fields that result from productions. These are:
In this help article we have covered how to set up alternative Work Centers at existing locations and use the Work Center Status and Initial State fields to evaluate if including these, and from what period onwards if so, will be optimal. We have also covered how Work Center Count Constraints can be used to pick a certain amount of Work Centers to be opened/used from a set of multiple candidates, either at 1 location or multiple. Here we also want to mention that Facility Count Constraints can be used when making decisions at the plant level. Say that based on market growth in a certain region, a manufacturer decides a new plant needs to be built. There are 3 candidate locations for the plant from which the optimal needs to be picked. This can be set up as follows in Cosmic Frog:
A couple of alternative approaches to this are:
As mentioned above in the section on the Bill Of Materials input table, it is possible to set up a model where there is demand for a product that is the By-product resulting from a BOM. This does require some additional set up, and the below walks through this, while also showcasing how the model can be used to determine how much of any flexible demand for this by-product to fulfill. The screenshots show the set-up of a very simple example model built for this specific purpose.
On the Products table, besides the component (for which there also is demand in this model) that goes into any BOM, we also specify:
The demand for the 3 products is set up on the Customer Demand table and we notice that 1) there is demand for the Component, the End Product, and the By-Product, and 2) the Demand Status for ByProduct_1 is set to Consider, which means it does not need to be fulfilled, it will be (partially) fulfilled if it is optimal to do so. (For Component_1 and EndProduct_1 the Demand Status field is left blank, which means the default value of Include will be used.)
The EndProduct_1 is made through a BOM which consumes Component_1 and also make ByProduct_1 as a Byproduct. For this we need to set up a BOM:
Next, on the Production Policies table, we see that Component_1 can be created without a BOM, and:
In reality, these 2 production policies result in the same consumption of Component_1 and same production amounts of EndProduct_1 and ByProduct_1. Both need to be present however in order to be able to also have demand for ByProduct_1 in the model.
Other model elements that need to be set up are:
Three scenarios were run for this simple example model with the only difference between them the Unit Price for ByProduct_1: Baseline (price of ByProduct_1 = 3), PriceByproduct1 (Unit Price of ByProduct_1 = 1), PriceByproduct2 (Unit Price of ByProduct_1 = 2). Let’s review some of the outputs to understand how this Unit Price affects the fulfillment of the flexible demand for ByProduct_1:
The high-level costs, revenues, profit and served/unserved demand outputs by scenario can be found on the Optimization Network Summary output table:
On the Optimization Production Summary output table, we see that all 3 scenarios used BYP_BOM for the production of EndProduct_1 and ByProduct_1, it could have also picked the other BOM (FG_BOM) and the overall results would have been the same.
As the Optimization Production Summary only shows the production of the end products, we will also have a look at the Optimization Bills Of Material Summary output table:
Lastly, we will have a look at the Optimization Inventory Summary output table:
Note that had the demand for Byproduct_1 been set to Include rather than Consider in this example model, all 3 scenarios would have produced 100 units of it to fulfill the demand, and as a result have produced 200 units of EndProduct_1. 100 of those would have been used to fulfill the demand for EndProduct_1 and the other 100 would have stayed in inventory, like we saw in the Baseline scenario above.
Showing supply chains on maps is a great way to visualize them, to understand differences between scenarios, and to show how they evolve over time. Cosmic Frog offers users many configuration options to customize maps to their exact needs. In this documentation we will cover how to create and configure maps in Cosmic Frog.
In Cosmic Frog, a map represents a single geographic visualization composed of different layers. A layer is an individual supply chain element such as a customer, product flow, or facility. To show locations on a map, these need to exist in the master tables (e.g. Customers, Facilities, and Suppliers) and they need to have been geocoded (see also the How to Geocode Locations section in this help center article). Flow based layers are based on output tables, such as the OptimizationFlowSummary or SimulationFlowSummary and to draw these, the model needs to have been run so outputs are present in these output tables.
Maps can be accessed through the Maps module in Cosmic Frog:
The Maps module opens and shows the first map in the Maps list; this will be the default pre-configured “Supply Chain” map for maps the user created and most models copied from the Resource Library:
In addition to what is mentioned under bullet 4 of the screenshot just above, users can also perform following actions on maps:
As we have seen in the screenshot above, the Maps module opens with a list of pre-configured maps and layers on the left-hand side:
The Map menu in the toolbar at the top of the Maps module allows users to perform basic map and layer operations:
These options from the Map menu are also available in the context menu that comes up when right-clicking on a map or layer in the Maps list.
The Map Filters panel can be used to set scenarios for each map individually. If users want to use the same scenario for all maps present in the model, they can use the Global Scenario Filter located in the toolbar at the top of the Maps module:
Now all maps in the model will use the selected scenario, and the option to set the scenario at the map-level is disabled.
When a global scenario has been set, it can be removed using the Global Scenario Filter again:
The zoom level, how the map is centered, and the configuration of maps and their layers persist. After moving between other modules within Cosmic Frog or switching between models, when user comes back to the map(s) in a specific model, the map settings are the same as when last configured.
Now let us look at how users can add new maps, and the map configuration options available to them.
Once done typing the name of the new map, the panel on the right-hand side of the map changes to the Map Filters panel which can be used to select the scenario and products the map will be showing:
Instead of setting which scenario to use for each map individually on the Map Filters panel, user can instead choose to set a global scenario for all maps to use, as discussed above in the Global Scenario Filter section. If a global scenario is set, the Scenario drop-down on the Map Filters panel will be disabled and user cannot open it:
On the Map Information panel, users have a lot of options to configure what the map looks like and what entities (outside of the supply chain ones configured in the layers) are shown on it:
Users can choose to show a legend on the map and configure it on the Map Legend pane:
To start visualizing the supply chain that is being modelled on a map, user needs to add at least 1 layer to a map, which can be done by choosing “New Layer” from the Map-menu:
Once a layer has been added or is selected in the Maps list, the panel on the right-hand side of the map changes to the Condition Builder panel which can be used to select the input or output table and any filters on it to be used to draw the layer:
We will now also look at using the Named Filters option to filter the table used to draw the map layer:
In this walk-through example, user chooses to enable the “DC1 and DC2” named filter:
Lastly on the Named Filters option, users have the option to view a grid preview to ensure the correct filtered records are being drawn on the map:
In the next layer configuration panel, Layer Style, users can choose what the supply chain entities that the layer shows will look like on the map. This panel looks somewhat different for layers that show locations (Type = Point) than for those that show flows (Type = Line). First, we will look at a point type layer (Customers):
Next, we will look at a line type layer, Customer Flows:
At the bottom of the Layer Style pane a Breakpoints toggle is available too (not shown in the screenshots above). To learn more about how these can be used and configured, please see the "Maps - Styling Points & Flows based on Breakpoints" Help Center article.
Labels and tooltips can be added to each layer, so users can more easily see properties of the entities shown in the layer. The Layer Labels configuration panel allows users to choose what to show as labels and tooltips, and configure the style of the labels:
When modelling multiple periods in network optimization (Neo) models, users can see how these evolve over time using the map:
Users can now add Customers, Facilities and Suppliers via the map:
After adding the entity, we see it showing on the map, here as a dark blue circle, which is how the Customers layer is configured on this map:
Looking in the Customers table, we notice that CZ_Philadelphia has been added. Note that while its latitude and longitude fields are set, other fields such as City, Country and Region are not automatically filled out for entities added via the map:
In this final section, we will show a few example maps to give users some ideas of what maps can look like. In this first screenshot, a map for a Transportation Optimization (Hopper engine) model, Transportation Optimization UserDefinedVariables available from Optilogic’s Resource Library (here), is shown:
Some notable features of this map are:
The next screenshot shows a map of a Greenfield (Triad engine) model:
Some notable features of this map are:
This following screenshot shows a subset of the customers in a Network Optimization (Neo engine) model, the Global Sourcing – Cost to Serve model available from Optilogic’s Resource Library (here). These customers are color-coded based on how profitable they are:
Some notable features of this map are:
Lastly, the following screenshot shows a map of the Tariffs example model, a network optimization (Neo engine) model available from Optilogic’s Resource Library (here), where suppliers located in Europe and China supply raw materials to the US and Mexico:
Some notable features of this map are:
We hope users feel empowered to create their own insightful maps. For any questions, please do not hesitate to contact Optilogic support at support@optilogic.com.
Finding problems with any Cosmic Frog model’s data has just become easier with the release of the Integrity Checker. This tool scans all tables or a selected table in a model and flags any records with potential issues. Field level checks to ensure fields contain the right type of data or a valid value from a drop-down list are included, as are referential integrity checks to ensure the consistency and validity of data relationships across the model’s input tables.
In this documentation we will first cover the Integrity Checker tool’s scope, how to run it, and how to review its results. Next, we will compare the Integrity Checker to other Cosmic Frog data validation tools, and we will wrap up with several tips & tricks to help users make optimal use of the tool.
The Integrity Checker extends cell validation and data entry helper capabilities to support users identify a range of issues relating to referential integrity and data types before running a model. The following types of data and referential integrity issues are being checked for when the Integrity Checker is run:
Here, we provide a high-level description for each of these 4 categories; in the appendix at the end of this help center article more details and examples for each type of check are given. From left to right:
The Integrity Checker can be accessed in two ways while in Cosmic Frog’s Data module: from the pane on the right-hand side that also contains Model Assistant and Scenario Errors or from the Grid drop-down menu. The latter is shown in the next screenshot:
*Please note that in this first version of the Integrity Checker, the Inventory Policies and Inventory Policies Multi-Time Period tables are not included in any checks the Integrity Checker performs. All other tables are.
The second way to access the Integrity Checker is, as mentioned above, from the pane on the right-hand side in Cosmic Frog:
If the Integrity Checker has been run previously on a model, opening it again will show the previous results and gives user the option to re-run it by clicking on a “Rerun Check” button which we will see in screenshots further below.
After starting the Integrity Checker in one of the 2 ways described above, a message indicating it is starting will appear in the Integrity Checker pane on the right-hand side:
While the Integrity Checker is running, the status of the run will be continuously updated, while results will be added underneath as checks on individual tables complete. Only tables which have errors in them will be listed in the results.
Once the Integrity Checker run is finished, its status changes to Completed:
Users can see the errors identified by the Integrity Checker by clicking on one of the table cards which will open the table and the Integrity Checker Errors table beneath it:
Clicking on a record in the Integrity Checker Errors table will filter the table above (here the Transportation Policies table) down to the record(s) with that error:
User can go through each record in the Integrity Checker Errors table at the bottom and filter out the associated records with the errors in the table above to review the errors and possibly fix them. In the next screenshot, user has moved onto the second record in the Integrity Checker Errors table:
We will look at one more error, the one that was found on the Products table:
Finally, the following screenshot shows what it looks like when the Integrity Checker was run on an individual table and in the case no errors are found:
There are additional tools in Cosmic Frog which can help with finding problems in the model’s data and overall construction, the table below gives an overview of how these tools compare to each other to help users choose the most suitable one for their situation:
Please take note of the following so you can make optimal use of the Integrity Checker capabilities:
We saw the next diagram further above in the Integrity Checker Scope section. Here we will expand on each of these categories and provide examples.
From left to right:
Note that the numeric and data type checks sound similar, but they are different: a value in a field can pass the data type check (e.g. a double field contains the value -2000), but not the numeric check (a latitude field can only contain values between -90 and 90, so -2000 would be invalid).
We hope you will find the Integrity Checker to be a helpful additional tool to facilitate your model building in Cosmic Frog! For any questions, please contact Optilogic support on support@optilogic.com.
In a supply chain model, sourcing policies describe how network components create and order necessary materials. In Cosmic Frog, sourcing rules & policies appear in two different table categories:
In this section, we will discuss how to use these Sourcing policy tables to incorporate real-world behavior. In the sourcing policy tables we define 4 different types of sourcing relationships:
First we will discuss the options user has for the simulation policy logic used in these 4 tables and the last section covers the other simulation specific fields that can be found on these sourcing policies tables.
Customer fulfillment policies describe which supply chain elements fulfill customer demand. For a Throg (Simulation) run, there are 3 different policy types that we can select in the “Simulation Policy” column:
If “By Preference” is selected, we can provide a ranking describing which sites we want to serve customers for different products. We can describe our preference using the “Simulation Policy Value” column.
In the following example we are describing how to serve customer CZ_CA’s demand. For Product_1, we prefer that demand is fulfilled by DC_AZ. If that is not possible, then we prefer DC_IL to fulfill demand. We can provide rankings for each customer and product combination.
Under this policy, the model will source material from the highest ranked site that can completely fill an order. If no sites can completely fill an order, and if partial fulfillment is allowed, the model will partially fill orders from multiple sources in order of their preference.
If “Single Source” is selected, the customer must receive the given product from 1 specific source, 1 of the 3 DCs in this example.
The “Allocation” policy is similar to the “By Preference” policy, in that it sources from sites in order of a preference ranking. The “Allocation” policy, however, does not look to see whether any sites can completely fill an order before doing partial fulfillment. Instead, it will source as much as possible from source 1, followed by source 2, etc. Note that the “Allocation” and “By Preference” policies will only be distinct if partial fulfillment is allowed for the customer/product combination.
Consider the following example, customer CZ_MA can source the 3 products it puts orders in for from 3 DCs using the By Preference simulation policy. For each product the order of preference is set the same: DC_VA is the top choice, then DC_IL, and DC_AZ is the third (last) choice. Also note that in the Customers table, CZ_MA has been configured so that it is allowed to partially fill orders and line items for this customer.
The first order of the simulation is one that CZ_MA places (screenshot from the Customer Orders table), it orders 20 units of Product_1, 600 units of Product_2, and 160 units of Product_3:
The inventory at the DCs for the products at the time this orders comes in is the same as the initial inventory as this customer order is the first event of the simulation:
When the simulation policy is set to By Preference, we will look to fill the entire order from the highest priority source possible. The first choice is DC_VA, so we check its inventory: it has enough inventory to fill the 20 units of Product_1 (375 units in stock) and the 160 units of Product_3 (500 units in stock), but not enough to fill the 600 units of product_2 (150 units in stock). Since the By Preference policy prefers to single source, it looks at the next priority source, DC_IL. DC_IL does have enough inventory to fulfill the whole order as it has 750 units of Product_1, 1000 units of Product_2, and 300 units of Product_3 in stock.
Now, if we change all the By Preference simulation policies to Allocation via a scenario and run this scenario, the outcomes are different. In this case, as many units as possible are sourced from the first choice DC, DC_VA in this case. This means sourcing 20 units of Product_1, 150 units of Product_2 (all that are in stock), and 160 units of Product_3 from DC_VA. Then next, we look at the second choice source, DC_IL, to see if we can fill the rest of the order that DC_VA cannot fill: the 450 units left of Product_1, which DC_IL does have enough inventory to fill. These differences in sourcing decisions for these 2 scenarios can for example be seen in the Simulation Shipment Report output table:
Replenishment policies describe how internal (i.e. non-customer) supply chain elements source material from other internal sources. For example, they might describe how a distribution center gets material from a manufacturing site. They are analogous to customer fulfillment policies, except instead of requiring a customer name, they require a facility name.
Procurement policies describe how internal (i.e. non-customer) supply chain elements source material from external suppliers. They are analogous to replenishment policies, except instead of using internal sources (e.g. manufacturing sites), they use external suppliers in the Source Name field.
Production policies allow us to describe how material is generated within our supply chain.
There are 4 simulation policies regarding production:
Besides setting the Simulation Policy on each of these Sourcing Policies tables, each has several other fields that the Throg Simulation engine uses as well, if populated. All 4 Sourcing Policies tables contain a Unit Cost and a Lot Size field, plus their UOM fields. The following screenshot shows these fields on the Replenishment Policies table:
The Customer Fulfillment Policies and Replenishment Policies tables both also have an Only Source From Surplus field which can be set to False (default behavior when not set) or True. When set to True, only sources which have available surplus inventory are considered as the source for the customer/facility – product combination. What is considered surplus inventory can be configured using the Surplus fields on the Inventory Policies input table.
Finally, the Production Policies table also has following additional fields: