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An Iconic Automobile maker optimizes Inventory for 20 Mio Part – Location combination

The Problem

The Customer, an iconic automobile manufacturer was grappling with Higher Inventories, Lower Service Levels and High Working Capital. It was scouting for a solution to optimize the Inventory Norms for its Commercial Vehicles Spares Division as their existing Inventory Tools / Processes were not delivering the desired results.. The division deals with a very large number of spare parts which are distributed through a wide network of dealers, distributors, and service centres. Optimized Inventory Norms were required at the Factories as well as all the nodes of their distribution network including warehouses, distributors and dealers to ensure that the working capital across the Supply Chain was used for the right parts and not blocked in slow-moving ones.

Key Challenges

For the Commercial Vehicle Spare parts businesses, multi-Echelon Inventory Optimization was required for around 400 customers, dozens of their own upstream warehouses, 4 to 5 factories and hundreds of Vendors. This was a gigantic problem as each of them deals with 40 to 50 thousand parts. In all it was a combination of over 20 million part-locations (50 thousand parts X 400+ customer and own locations).

Key challenges had to be addressed:

  • Accurate relationship between Inventory and Service Level had to be established at around 20 million part-location combination.
  • Optimized Service Targets had to be generated for each product at each location such that an overall ex-customer case-fill target of 97% could be achieved.
  • Multi-echelon Inventory Optimization had to be done to generate an optimal postponement strategy.


rhythm 2.0 Inventory Planner was used to optimize the Safety Stock Norms for all parts at all customer and own locations. rhythm 2.0 is capable of considering the actual secondary sales history for computation of Demand Variability. Likewise, it can consider the actual purchase and receipt history for computing Lead-time, Lead-time variability and Supply Variability.

Using the above Demand, Lead-time and Supply Variabilities, Lead-times and review periods, Production and Distribution lot-sizes etc. the system generated an accurate stock to service curve for each part at each location based on their unique characteristics. This unique relationship helps in determining the inventory level required for achieving a target Service Level for each part at each location. Refer chart below for examples of Stock to Service Curves for a part at a couple of locations. Location 2 has much larger variability compared to Location 1.

An Iconic Automobile maker optimizes Inventory for 20 Mio Part – Location combination

After generating the above unique curves, rhythm 2.0 simultaneously does Multi-Echelon and Multi-part Inventory Optimization to (a) identify the right Inventory deployment strategy (Customer vs Own Warehouse vs Factory or Supplier) and to (b) optimize the individual Service Levels for each part at each location to meet the overall Case Fill Target as shown below. In the following example, parts A, B and C at Locations X, Y and Z have differential Service Levels based on their unique characteristics to meet the overall Case Fill target of 97%.

An Iconic Automobile maker optimizes Inventory for 20 Mio Part – Location combination

Key Results

Existing Customer Norms were used to benchmark against the System generated Norms to ensure that only planned inventory was used for comparison.

Summary of results delivered –

chart2 | Algorhythm Tech


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