Operations and supply chain optimization

The car replacement part industry is prone to sudden shifts in demand, driven by car park developments, seasonality, promotions and substitution effects. There are hundreds of car types on the road, with varying and unknown number of cars per type, and a long tail of small volume and/or low frequency product sales. All of which makes it difficult to accurately predict future demand, and thus manage inventory. But holding excess inventory is an expensive drain on resource. The challenge was to meet customer demand for high levels of product availability while optimizing cash flow and working capital.

We embarked on an inventory optimization journey to improve forecast accuracy and streamline inventory management across the client’s portfolio of brands and car types. The approach was three-fold: first, predict future demand distribution across brands, customer segments and car types, by constructing a robust and reliable data model and advanced AI techniques. Second, calculate optimal stock levels to keep service levels high and inventory costs low, by optimizing over 100s of demand scenarios, per product. And third, empower purchasing managers to make smart decisions and set up a reliable governance model, supported by AI-powered automation and machine learning.

In the first year of operation, we exceeded business case expectations on all three measures. We achieved a 31% reduction in working capital tied up in inventory, automation of 30% of decision-making processes and 4x higher ROI.