Optimizing Inventory Flexibility for 18,000 SKUs Through Simulation and Algorithmic Modeling
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1. A large technology company engaged a consulting firm to develop a flexible inventory model for about 18,000 SKUs with highly volatile demand for new technologies. 2. The goal was to create an algorithm that simulates demand and assigns optimal buffer inventory levels for each SKU, while ensuring the inventory budget is not exceeded. 3. The algorithm clusters SKUs, estimates demand distributions, and uses nonlinear programming to optimize flexible inventory levels to minimize total costs while meeting service level targets.
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Optimizing Inventory Flexibility for 18,000 SKUs Through Simulation and Algorithmic Modeling
- 1. A Case Study in Inventory Flexibility Modeling A Fortune 100 Technology Company Quick Context Objective • >90% adoption amongst the Demand Planning community • 22% of Demand peaks were addressed by the Flexible Inventory in 2014 – lower Lost Sales Impact • BRIDGEi2i appreciates the fact that Inventory is an asset as well as a liability • Our frameworks for Inventory Calibration allows for strategy dynamics in an organization Key Success Elements Our Approach 6 Months 3 Years Client Project length Length of relationship with client • All data was securely accessed within Client environment • Historical and Future forecasts • Monthly Actual Bookings from Teradata • Standard Costs and ASP • Holding cost and lost sales based on assumptions and business justifications • Forecast Bias removal, Outlier Treatment for data treatment • All SKUs were clustered and a Best- Fitting Error Distribution was estimated • Lost sales cost, inventory cost and service levels were found out based on the samples drawn • Non linear programing used to optimize the flexible inventory for all products • Total operation cost minimized such that certain service level are met • A front end tool help demand planners (DP) to see the optimum flexibility % • Tool allows for what-if scenario analysis • Product grouping & product family level analysis • Budgeting & cost analysis Data Management Algorithmic Play Operationalization a. ~18,000 SKUs have highly volatile demand and are new technology b. Client has an Inventory budget that cannot be exceeded but is not optimally distributed across the portfolio a. To develop an algorithm that simulates demand signals and associates a buffer inventory need for every SKU b. To build a tracking mechanism that ensures optimality is maintained