Optimizing Inventory Management
Mu Sigma collaborated with a leading home improvement retailer to refine inventory management for 30 million store-SKU combinations, integrating a simulation-based solution into the client's IT systems, optimizing safety stock, re-order policies, and minimizing lost sales and inventory excess.
Mounting Lost Sales
Due to flawed demand planning and an outdated inventory system, the retailer grappled with mounting lost sales and spiralling inventory holding costs. Legacy systems operated on assumptions unsuited for specialty retailers with predominantly slow-moving items catering to diverse customer segments.
Mu Sigma’s In-depth Analysis:
Analysis revealed that the inventory inefficiencies stemmed from the retailer’s unique demand patterns with their slow-moving items. Key discoveries included pronounced seasonality variations across product categories, distinct customer behaviors on weekdays versus weekends, erratic supplier lead times, and stark demand differences between regular shoppers and bulk-buying contractors.
First Principles Thinking:
Based on these insights, we collaborated with the business to design a module that enhanced inventory planning for select products across 3,000 stores. By emphasizing first principle thinking, we constructed a tailored solution. We identified item-store combinations using demand and supply metrics, then applied discrete event simulation models to craft new inventory policies and safety stock levels. After validating the models using an order simulator, preliminary store tests showed marked reductions in lost sales and optimized in-stock levels. This refined algorithm, fully compatible with the client’s existing system, was scaled to streamline over 30 million item-store combinations.
Increase in Sales
Reduction in inventory
Reduction in manual inventory management
The firm's is derived from the statistical terms "Mu" and "Sigma," which symbolize a
probability distribution's mean and standard deviation, respectively.