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Published on
June 8, 2026
Daikin Industries is one of the world’s largest air conditioning companies, with a history spanning more than 100 years, a consolidated workforce of over 104,000 employees across 324 group companies, and fiscal year 2025 consolidated revenue exceeding 5 trillion yen. With operations in 170+ countries and regions, and international sales now representing 84% of total revenue, Daikin operates a supply chain of enormous scale and complexity.
Air conditioning represents 92% of Daikin’s revenue, spanning residential, commercial, and industrial product lines across every major global market.
The supply chain work described here was led by Daikin’s Data Utilization Promotion Group, part of the Technology Innovation Center (TIC), working in close collaboration with the relevant business divisions. Established in April 2020, the group’s mission is to use data and digital technology to address operational challenges across Daikin’s business.
Daikin’s supply chain operates across multiple independent functions — procurement, production, logistics, and sales — each operating with distinct KPIs and information sets. This creates a complex ecosystem of distributed decision-makers whose locally rational choices do not always produce globally optimal outcomes. This initiative focused on addressing a specific supply chain challenge.
Air conditioners are products that require high service levels. Unlike cars, when a unit fails, consumers often purchase what is available at that time rather than wait for a specific brand. Stockouts translate directly to lost sales, making high service levels a commercial requirement, not a preference.
Daikin’s domestic supply methods and inventory parameters have largely been carried over from past operating practices. Frontline staff manually adjusted inventory based on short-term sales data; however, in a changing business environment, there was a need to assess whether current operations remained optimal and to validate their cost-effectiveness.
To reduce reliance on external consultants and strengthen internal capabilities, Daikin’s Data Utilization Promotion Group worked closely with the relevant business divisions to develop an in-house supply chain simulation capability. The team applied simulation to the challenge, with the aim of generating evidence-based insights and options for management review.
Team leader Shota Tanaka framed the philosophy clearly: the goal of simulation is not to derive a single correct answer and control everything from the top. It is to ensure that each distributed decision-maker can understand the impact of their choices on the whole system — so they can consistently make better decisions. He also noted that an overly precise model becomes rigid and brittle in the face of change. An appropriately abstracted model is more durable and more likely to be used.
Working closely with the relevant business divisions, Tanaka’s team built a simulation model covering high-volume residential room air conditioner models across a network of three factory locations and six distribution centers, using one year of historical data. Cosmic Frog was selected for its ability to replicate Daikin’s proprietary, non-standard supply logic — something other tools could not accommodate without significant compromise. Cosmic Frog combines optimization, simulation, and agentic AI in a single cloud-native platform, giving Daikin’s team the flexibility to model complex, non-standard supply chain logic without IT overhead. They validated the model against actual historical results, then designed scenarios varying production logic, replenishment logic (push vs. pull), inventory parameters, and shipping rules.
The project followed a common implementation structure: data collection and cleaning, current-state model construction, validation against historical results, scenario design, and simulation and optimization runs. Cosmic Frog’s ability to handle both inventory policy simulation and logistics network optimization within a single platform meant the team worked from a consistent analytical foundation, reducing onboarding time and enabling direct knowledge transfer.
The project was ongoing at the time of presentation. Full quantified findings will be finalized following scenario completion and internal review, expected mid-2026.
The design intent was not to solve an isolated problem — it was to build a replicable internal methodology. The domestic project was a model for that approach, with tools, processes, team structure, and data approaches drawn directly from earlier work. Looking ahead, the intent is for this methodology to extend to other global regions and broaden in scope to incorporate procurement alongside logistics and inventory.
The relationship with the Optilogic team was described by Daikin as a genuine collaboration — not a vendor transaction. Hands-on support for model construction, input parameters, and replication of non-standard supply logic, combined with Japanese-language technical assistance, was identified as a key factor in the project maintaining its current pace of progress.
Decision quality. Supply chain decisions will be supported by quantified scenario analysis alongside inherited experience and expert judgment.
Speed of response. By building an internal simulation capability, Daikin is now better positioned to quickly rerun analyses as conditions change and to evaluate future scenarios in a timely manner.
Global scalability. A standardized simulation methodology — common tools, analytical frameworks, and shared know-how — is intended to create the foundation for more consistent supply chain decision-making as the methodology matures across the organization.
For the domestic project, the next step is completing scenario simulations and presenting optimal supply method and inventory parameter recommendations to senior management. Beyond this project, Daikin’s Data Utilization Promotion Group is looking to extend this methodology to other global regions and broaden the scope to include procurement.
About Optilogic Partner BigM
BigM is a Japan-based supply chain consulting firm focused on solving complex supply chain challenges through digital technology, with a team consisting entirely of supply chain specialists with deep expertise in optimization, decision-making, and supply chain design. With a mission to enhance sustainability, efficiency, and optimization through advanced technology and global networks, BigM works with enterprises across Japan and beyond — helping them move from raw data to real insight, faster. The firm uses Optilogic's platform, including DataStar and Cosmic Frog, to help clients build repeatable, scalable analytics workflows that connect data directly to supply chain decision-making.
Daikin Industries is one of the world’s largest air conditioning companies, with a history spanning more than 100 years, a consolidated workforce of over 104,000 employees across 324 group companies, and fiscal year 2025 consolidated revenue exceeding 5 trillion yen. With operations in 170+ countries and regions, and international sales now representing 84% of total revenue, Daikin operates a supply chain of enormous scale and complexity.
Air conditioning represents 92% of Daikin’s revenue, spanning residential, commercial, and industrial product lines across every major global market.
The supply chain work described here was led by Daikin’s Data Utilization Promotion Group, part of the Technology Innovation Center (TIC), working in close collaboration with the relevant business divisions. Established in April 2020, the group’s mission is to use data and digital technology to address operational challenges across Daikin’s business.
Daikin’s supply chain operates across multiple independent functions — procurement, production, logistics, and sales — each operating with distinct KPIs and information sets. This creates a complex ecosystem of distributed decision-makers whose locally rational choices do not always produce globally optimal outcomes. This initiative focused on addressing a specific supply chain challenge.
Air conditioners are products that require high service levels. Unlike cars, when a unit fails, consumers often purchase what is available at that time rather than wait for a specific brand. Stockouts translate directly to lost sales, making high service levels a commercial requirement, not a preference.
Daikin’s domestic supply methods and inventory parameters have largely been carried over from past operating practices. Frontline staff manually adjusted inventory based on short-term sales data; however, in a changing business environment, there was a need to assess whether current operations remained optimal and to validate their cost-effectiveness.
To reduce reliance on external consultants and strengthen internal capabilities, Daikin’s Data Utilization Promotion Group worked closely with the relevant business divisions to develop an in-house supply chain simulation capability. The team applied simulation to the challenge, with the aim of generating evidence-based insights and options for management review.
Team leader Shota Tanaka framed the philosophy clearly: the goal of simulation is not to derive a single correct answer and control everything from the top. It is to ensure that each distributed decision-maker can understand the impact of their choices on the whole system — so they can consistently make better decisions. He also noted that an overly precise model becomes rigid and brittle in the face of change. An appropriately abstracted model is more durable and more likely to be used.
Working closely with the relevant business divisions, Tanaka’s team built a simulation model covering high-volume residential room air conditioner models across a network of three factory locations and six distribution centers, using one year of historical data. Cosmic Frog was selected for its ability to replicate Daikin’s proprietary, non-standard supply logic — something other tools could not accommodate without significant compromise. Cosmic Frog combines optimization, simulation, and agentic AI in a single cloud-native platform, giving Daikin’s team the flexibility to model complex, non-standard supply chain logic without IT overhead. They validated the model against actual historical results, then designed scenarios varying production logic, replenishment logic (push vs. pull), inventory parameters, and shipping rules.
The project followed a common implementation structure: data collection and cleaning, current-state model construction, validation against historical results, scenario design, and simulation and optimization runs. Cosmic Frog’s ability to handle both inventory policy simulation and logistics network optimization within a single platform meant the team worked from a consistent analytical foundation, reducing onboarding time and enabling direct knowledge transfer.
The project was ongoing at the time of presentation. Full quantified findings will be finalized following scenario completion and internal review, expected mid-2026.
The design intent was not to solve an isolated problem — it was to build a replicable internal methodology. The domestic project was a model for that approach, with tools, processes, team structure, and data approaches drawn directly from earlier work. Looking ahead, the intent is for this methodology to extend to other global regions and broaden in scope to incorporate procurement alongside logistics and inventory.
The relationship with the Optilogic team was described by Daikin as a genuine collaboration — not a vendor transaction. Hands-on support for model construction, input parameters, and replication of non-standard supply logic, combined with Japanese-language technical assistance, was identified as a key factor in the project maintaining its current pace of progress.
Decision quality. Supply chain decisions will be supported by quantified scenario analysis alongside inherited experience and expert judgment.
Speed of response. By building an internal simulation capability, Daikin is now better positioned to quickly rerun analyses as conditions change and to evaluate future scenarios in a timely manner.
Global scalability. A standardized simulation methodology — common tools, analytical frameworks, and shared know-how — is intended to create the foundation for more consistent supply chain decision-making as the methodology matures across the organization.
For the domestic project, the next step is completing scenario simulations and presenting optimal supply method and inventory parameter recommendations to senior management. Beyond this project, Daikin’s Data Utilization Promotion Group is looking to extend this methodology to other global regions and broaden the scope to include procurement.
About Optilogic Partner BigM
BigM is a Japan-based supply chain consulting firm focused on solving complex supply chain challenges through digital technology, with a team consisting entirely of supply chain specialists with deep expertise in optimization, decision-making, and supply chain design. With a mission to enhance sustainability, efficiency, and optimization through advanced technology and global networks, BigM works with enterprises across Japan and beyond — helping them move from raw data to real insight, faster. The firm uses Optilogic's platform, including DataStar and Cosmic Frog, to help clients build repeatable, scalable analytics workflows that connect data directly to supply chain decision-making.
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