Design Your Supply Chain with the Most Powerful and Intelligent Optimization Engine
What Does Cosmic Frog Optimization Do?
Optimization is the trusted mathematical modeling approach used in most supply chain design business use cases. It can not only handle complex supply chain challenges, but it is also prescriptive and provides recommended actions. Mixed integer optimization seeks to either maximize or minimize the defined objective function by changing variables while adhering to defined constraints.
When there are myriad possibilities to consider, Cosmic Frog supply chain optimization identifies the best possible configuration(s) to meet your business KPIs.
Supply Chain Optimization Provides Answers to Tough Business Questions
Use Cosmic Frog optimization for supply chain design use cases such as network strategy, greenfield analysis, mode selection, CAPEX planning, merger and acquisition analysis, and optimizing product flow or the balance of supply and demand.

Created by the Most Experienced Team
Cosmic Frog was built by industry experts, each with decades of experience modeling, optimizing, simulating, and scaling supply chain designs. The Applied Research team behind our powerful optimization engine consists of PhDs and operations research professionals who are experts in building complex algorithms and supply chain design.

Models Run Faster in Cosmic Frog
Optilogic has teamed up with Gurobi, the fastest mathematical optimization solver on the market, to offer a next generation supply chain design solution that reduces time to value. Modelers are more efficient when they can run more scenarios—and run them faster—with Cosmic Frog hyperscaling combined with Gurobi’s rapid solve times.
Optimization + Simulation + Risk
Combine prescriptive analytics with predictive analytics to not only find the optimal solution but gain visibility into how any given scenario would perform under realistic conditions. The Cosmic Frog platform combines optimization and simulation in a single, flexible platform, sharing the same data schema. This architecture provides a workflow for users to utilize both technologies for a more comprehensive, insightful, and accurate analysis, leading to improved decision-making in supply chain design. Plus, every design includes a supply chain risk rating.
