Case Study

Using Supply Chain Design to Right-Size Inventory

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Using Supply Chain Design to Right-Size Inventory

Challenge

A global healthcare and consumer goods company needed to better understand their customer demand patterns, improve customer service and identify more effective postponement and risk-pooling strategies to enable overall reductions in working capital. The company selected LLamasoft technology to deliver optimized safety stock and cycle stock targets for their healthcare unit by modeling the complete production and distribution supply chain.

Solution

The LLamasoft team, working alongside the customer, followed a three-step process including demand analysis, cycle stock optimization and safety stock optimization. SAP sales history and sales forecasts were entered into LLamasoft® Supply Chain Guru®, generating a clear picture of demand product classes and associated customer/product demand patterns. Multi-echelon safety stock targets for finished goods, WIP and raw materials were generated providing actionable data well beyond their current safety stock tool which generates only single echelon finished good safety stock targets.
health-goods-graph

Simulation was used to validate inventory levels recommended by Inventory Guru (blue line) compared to the company’s existing safety stock method (purple dashed). Green line represents the actual simulated safety stock results (using Supply Chain Guru simulation). The existing method significantly overstocks, while the Inventory Guru levels more closely match actual test results (green).
health-goods-graph-2

Results

Through new LLamasoft demand analysis and classification technology combined with inventory optimization, the company realized that their current safety stock methods had been incorrectly classifying the demand for their healthcare products, assuming all products followed a “normal” distribution demand pattern. Supply Chain Guru’s automated classification showed only five percent of their products fit a normal distribution, with the remaining 95 percent spread amongst four additional demand classes. The “normal” assumption resulted in significant excess inventory or missed service metrics for most products. Furthermore, the Supply Chain Guru model was able to show the network-wide view of cycle stock, work-in-progress (WIP), in-transit and safety stock inventories. The result was a $2 million potential opportunity for inventory reduction—about 25 percent of total inventory cost.

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