Cyclo is Optilogic’s new Multi Echelon Inventory Optimization (MEIO) engine within Cosmic Frog. It helps supply chain teams determine where safety stock should be held across a network, how much is needed at each stage, and how service levels impact total safety stock cost and responsiveness.
If you just want to get going with Cyclo as quick as possible, follow these steps:
Multi Echelon Inventory Optimization (MEIO) is a planning approach used to optimize safety stock across an entire supply chain network.
Cyclo, the MEIO engine, is designed to optimize safety stock placement across multi-stage supply chains that may include suppliers, manufacturing plants, distribution centers, and customer-facing locations. Instead of optimizing each node independently, Cyclo evaluates the entire network simultaneously so organizations can reduce total safety stock while maintaining desired service levels.
Cyclo uses a Guaranteed Service Model (GSM) approach to optimize service-time relationships between facilities and derive recommended safety stock levels.
Cyclo helps organizations answer key supply chain questions such as:
By optimizing safety stock placement across the entire network, Cyclo can help organizations:
Cyclo is especially valuable for:
Both Cyclo and Dendro support inventory optimization workflows in Cosmic Frog, but they are designed for different planning problems.
In practice both can be used together:
Cyclo uses a Guaranteed Service Model (GSM) approach. Rather than directly optimizing safety stock quantities, Cyclo optimizes service-time commitments between facilities. Those service-time decisions are then translated into safety stock requirements.
Represents your risk tolerance – balancing the cost of holding extra buffer inventory against the risk and cost of lost sales. This is a user input. Two risk measures are available:
Service Type 1 is a stricter measure than Type 2 and will in most cases lead to more safety stock.
Time a facility expects upstream suppliers to deliver material. This is a decision variable in the optimization
Time a facility needs to replenish – typically transport time from the upstream location to the facility and/or production/processing time at the facility. These are model inputs.
Time a facility promises to deliver to downstream customers. This is a decision variable in the optimization.
The effective time window over which demand uncertainty accumulates.
In a Guaranteed Service Model (GSM), each facility commits to serving downstream nodes within a defined service time. The effective exposure to uncertainty is the Net Replenishment Time (NRT):
NRT = Incoming Service Time + Fixed Lead Times − Outgoing Service Time
As NRT increases, more uncertainty accumulates and more safety stock is typically required.
Cyclo evaluates many combinations of incoming and outgoing service times across the network to find the lowest total safety stock holding cost, while reaching the target service level. The optimization is not changing any fixed lead times. Instead, it is strategically deciding where responsiveness should exist in the network.
Consider a product with the following flow path:
Manufacturer → Distribution Center → Customer
Assume the following fixed lead times:
The total physical replenishment lead time across the network is therefore 5 days.
These lead times are inputs to the model and are not optimized. What Cyclo optimizes are the service-time commitments between stages. Specifically:
These service times are optimized with the goal to minimize total safety stock holding cost across the network.
In the next example scenarios:
Manufacturer ----> DC (5 days safety stock) ----> Customer
Interpretation:
This approach is common in highly responsive distribution networks.
Manufacturer (2 days safety stock) --> DC (3 days safety stock) --> Customer
Interpretation:
Manufacturer (4 days safety stock) --> DC (1 day safety stock) --> Customer
Interpretation:
The total physical replenishment exposure is driven by the same 5-day total network lead time in all 3 scenarios.
What changes is:
Cyclo evaluates many combinations of:
to determine the optimal inventory strategy across the network.
Without MEIO, organizations often duplicate safety stock across multiple locations and optimize inventory independently at each node. Cyclo instead evaluates the network holistically and strategically concentrates inventory where it is most cost-effective, while achieving the required service level.
The following diagram summarizes the inputs and outputs of the Cyclo engine; they will be covered in more detail in the Cyclo in Cosmic Frog section that follows.
The following workflow provides a step-by-step approach for configuring and running Cyclo.
The following table provides an overview of the input tables used by Cyclo, whether they are required, and their purpose. Further below, several screenshots show examples of some of the main inputs in Cosmic Frog.
The following screenshots show several input tables with key Cyclo fields.
Demand can be specified in either of the Customer Orders and Customer Order Profiles tables. If the Customer Orders table is populated it will be used and the Customer Order Profiles table will be skipped in that case. If the Customer Orders table is blank, the Customer Order Profiles table will be used.
Before running Cyclo, verify that the supply chain network is fully configured. Recommended validation checks:
You can also use Cosmic Frog’s Integrity Checker and filter the results where the Relevant Technology field contains Cyclo.
Once the model has been built, you can optionally configure additional scenarios to run. Here 1 additional scenario is added besides the Baseline:
After inputs are validated and scenarios set up, users can kick off their Cyclo optimization run by clicking on the green Run button at the top right in Cosmic Frog, which brings up the Run Settings modal:
During execution, Cyclo processes:
The optimization engine evaluates inventory decisions holistically across the network rather than independently by node.
After the optimization is completed, review the generated outputs.
The Cyclo outputs are in 2 tables, Inventory Network Summary and Inventory Safety Stock Summary, and include:
Cyclo outputs help users understand recommended inventory placement, service-time commitments, and total network inventory cost tradeoffs.
The Inventory Network Summary summarizes results by scenario:
This helps users:
The Inventory Safety Stock Summary shows detailed results at the product x location level, by scenario:
The 4 screenshots in the next sub-sections are of additional fields on the same table and do not always show the fields of the screenshot above again.
The recommended safety stock reflects:
Note that another field not shown in the screenshot, Holding Cost, is available in this table too. Its value is the holding cost for 1 unit of product at that location for the length of the model run. The Holding Cost Contribution is calculated as this Holding Cost value multiplied with the Safety Stock value.
These values represent:
Cyclo can recommend inventory policies and their parameters:
These policies help operationalize inventory decisions.
When reviewing Cyclo outputs, focus on patterns across the network rather than individual locations.
Questions to ask include:
Safety stock optimization quality depends heavily on the quality of the data.
Recommended practices:
Cyclo is especially valuable for scenario analysis.
Examples include:
Scenario comparisons help quantify operational tradeoffs.
MEIO is fundamentally a system-wide optimization problem; avoid evaluating locations independently. The best global solution may intentionally increase inventory at one node in order to reduce much larger inventory requirements elsewhere.
Cyclo outputs are most valuable when reviewed collaboratively by:
Why does Cyclo place more inventory at upstream locations?
In many networks, upstream buffering can reduce downstream safety stock due to variability evening out when aggregating demand from multiple downstream locations (pooling effect). This lowers the total inventory holding cost. Cyclo evaluates these trade-offs automatically.
Does higher service always mean more inventory?
Generally, yes. Higher service-level targets reduce allowable stockout risk, which usually increases safety stock requirements.
Why are service times optimized instead of inventory directly?
The Guaranteed Service Model simplifies the optimization problem and provides a scalable framework for network-wide inventory positioning. Safety stock is derived from optimized service-time relationships.
Cyclo brings advanced Multi Echelon Inventory Optimization capabilities into Cosmic Frog.
By optimizing service-time commitments and safety stock placement across the entire supply chain network, Cyclo helps organizations:
Cyclo is especially valuable for organizations operating complex, multi-stage supply chains where local safety stock decisions can create unintended network-wide impacts.
Please do not hesitate to contact our support team on Support@optilogic.com in case of any questions of feedback.
Cyclo is Optilogic’s new Multi Echelon Inventory Optimization (MEIO) engine within Cosmic Frog. It helps supply chain teams determine where safety stock should be held across a network, how much is needed at each stage, and how service levels impact total safety stock cost and responsiveness.
If you just want to get going with Cyclo as quick as possible, follow these steps:
Multi Echelon Inventory Optimization (MEIO) is a planning approach used to optimize safety stock across an entire supply chain network.
Cyclo, the MEIO engine, is designed to optimize safety stock placement across multi-stage supply chains that may include suppliers, manufacturing plants, distribution centers, and customer-facing locations. Instead of optimizing each node independently, Cyclo evaluates the entire network simultaneously so organizations can reduce total safety stock while maintaining desired service levels.
Cyclo uses a Guaranteed Service Model (GSM) approach to optimize service-time relationships between facilities and derive recommended safety stock levels.
Cyclo helps organizations answer key supply chain questions such as:
By optimizing safety stock placement across the entire network, Cyclo can help organizations:
Cyclo is especially valuable for:
Both Cyclo and Dendro support inventory optimization workflows in Cosmic Frog, but they are designed for different planning problems.
In practice both can be used together:
Cyclo uses a Guaranteed Service Model (GSM) approach. Rather than directly optimizing safety stock quantities, Cyclo optimizes service-time commitments between facilities. Those service-time decisions are then translated into safety stock requirements.
Represents your risk tolerance – balancing the cost of holding extra buffer inventory against the risk and cost of lost sales. This is a user input. Two risk measures are available:
Service Type 1 is a stricter measure than Type 2 and will in most cases lead to more safety stock.
Time a facility expects upstream suppliers to deliver material. This is a decision variable in the optimization
Time a facility needs to replenish – typically transport time from the upstream location to the facility and/or production/processing time at the facility. These are model inputs.
Time a facility promises to deliver to downstream customers. This is a decision variable in the optimization.
The effective time window over which demand uncertainty accumulates.
In a Guaranteed Service Model (GSM), each facility commits to serving downstream nodes within a defined service time. The effective exposure to uncertainty is the Net Replenishment Time (NRT):
NRT = Incoming Service Time + Fixed Lead Times − Outgoing Service Time
As NRT increases, more uncertainty accumulates and more safety stock is typically required.
Cyclo evaluates many combinations of incoming and outgoing service times across the network to find the lowest total safety stock holding cost, while reaching the target service level. The optimization is not changing any fixed lead times. Instead, it is strategically deciding where responsiveness should exist in the network.
Consider a product with the following flow path:
Manufacturer → Distribution Center → Customer
Assume the following fixed lead times:
The total physical replenishment lead time across the network is therefore 5 days.
These lead times are inputs to the model and are not optimized. What Cyclo optimizes are the service-time commitments between stages. Specifically:
These service times are optimized with the goal to minimize total safety stock holding cost across the network.
In the next example scenarios:
Manufacturer ----> DC (5 days safety stock) ----> Customer
Interpretation:
This approach is common in highly responsive distribution networks.
Manufacturer (2 days safety stock) --> DC (3 days safety stock) --> Customer
Interpretation:
Manufacturer (4 days safety stock) --> DC (1 day safety stock) --> Customer
Interpretation:
The total physical replenishment exposure is driven by the same 5-day total network lead time in all 3 scenarios.
What changes is:
Cyclo evaluates many combinations of:
to determine the optimal inventory strategy across the network.
Without MEIO, organizations often duplicate safety stock across multiple locations and optimize inventory independently at each node. Cyclo instead evaluates the network holistically and strategically concentrates inventory where it is most cost-effective, while achieving the required service level.
The following diagram summarizes the inputs and outputs of the Cyclo engine; they will be covered in more detail in the Cyclo in Cosmic Frog section that follows.
The following workflow provides a step-by-step approach for configuring and running Cyclo.
The following table provides an overview of the input tables used by Cyclo, whether they are required, and their purpose. Further below, several screenshots show examples of some of the main inputs in Cosmic Frog.
The following screenshots show several input tables with key Cyclo fields.
Demand can be specified in either of the Customer Orders and Customer Order Profiles tables. If the Customer Orders table is populated it will be used and the Customer Order Profiles table will be skipped in that case. If the Customer Orders table is blank, the Customer Order Profiles table will be used.
Before running Cyclo, verify that the supply chain network is fully configured. Recommended validation checks:
You can also use Cosmic Frog’s Integrity Checker and filter the results where the Relevant Technology field contains Cyclo.
Once the model has been built, you can optionally configure additional scenarios to run. Here 1 additional scenario is added besides the Baseline:
After inputs are validated and scenarios set up, users can kick off their Cyclo optimization run by clicking on the green Run button at the top right in Cosmic Frog, which brings up the Run Settings modal:
During execution, Cyclo processes:
The optimization engine evaluates inventory decisions holistically across the network rather than independently by node.
After the optimization is completed, review the generated outputs.
The Cyclo outputs are in 2 tables, Inventory Network Summary and Inventory Safety Stock Summary, and include:
Cyclo outputs help users understand recommended inventory placement, service-time commitments, and total network inventory cost tradeoffs.
The Inventory Network Summary summarizes results by scenario:
This helps users:
The Inventory Safety Stock Summary shows detailed results at the product x location level, by scenario:
The 4 screenshots in the next sub-sections are of additional fields on the same table and do not always show the fields of the screenshot above again.
The recommended safety stock reflects:
Note that another field not shown in the screenshot, Holding Cost, is available in this table too. Its value is the holding cost for 1 unit of product at that location for the length of the model run. The Holding Cost Contribution is calculated as this Holding Cost value multiplied with the Safety Stock value.
These values represent:
Cyclo can recommend inventory policies and their parameters:
These policies help operationalize inventory decisions.
When reviewing Cyclo outputs, focus on patterns across the network rather than individual locations.
Questions to ask include:
Safety stock optimization quality depends heavily on the quality of the data.
Recommended practices:
Cyclo is especially valuable for scenario analysis.
Examples include:
Scenario comparisons help quantify operational tradeoffs.
MEIO is fundamentally a system-wide optimization problem; avoid evaluating locations independently. The best global solution may intentionally increase inventory at one node in order to reduce much larger inventory requirements elsewhere.
Cyclo outputs are most valuable when reviewed collaboratively by:
Why does Cyclo place more inventory at upstream locations?
In many networks, upstream buffering can reduce downstream safety stock due to variability evening out when aggregating demand from multiple downstream locations (pooling effect). This lowers the total inventory holding cost. Cyclo evaluates these trade-offs automatically.
Does higher service always mean more inventory?
Generally, yes. Higher service-level targets reduce allowable stockout risk, which usually increases safety stock requirements.
Why are service times optimized instead of inventory directly?
The Guaranteed Service Model simplifies the optimization problem and provides a scalable framework for network-wide inventory positioning. Safety stock is derived from optimized service-time relationships.
Cyclo brings advanced Multi Echelon Inventory Optimization capabilities into Cosmic Frog.
By optimizing service-time commitments and safety stock placement across the entire supply chain network, Cyclo helps organizations:
Cyclo is especially valuable for organizations operating complex, multi-stage supply chains where local safety stock decisions can create unintended network-wide impacts.
Please do not hesitate to contact our support team on Support@optilogic.com in case of any questions of feedback.
