Case Study: Replenishment Planning for a Distributor with a Complex Network

In this case study, we explore a replenishment planning project undertaken for a distributor managing an extensive inventory across a vast network. The distributor in question handled hundreds of thousands of items, distributed through approximately 20 distribution centers (DCs). The scale and complexity of the network presented unique challenges, particularly concerning circular sourcing issues, demand allocation, and the need for real-time optimization.

Key Challenges and Solutions

1. Addressing Circularity Issues: The distributor’s network faced circularity challenges where sourcing was limited to only one level deep. For instance, Site A could source from Site B, and Site B could source from Site A, creating potential loops that could disrupt efficient replenishment. To resolve this, the team introduced virtual locations into the network. By assigning a weighted preference to source items from a primary location first and only turning to secondary locations when necessary, the solution effectively minimized circular sourcing conflicts.
2. Optimizing Demand-Supply Matching: To allocate products efficiently to customers, multiple tiers of demand classification were implemented. The project leveraged a linear programming solver to optimize the demand-supply match across the network. This solver accounted for factors such as inventory levels, transportation costs, and customer demand, ensuring that products were distributed in a way that maximized overall efficiency and minimized costs.
3. Achieving Order-Level Transfer Visibility: Given the complexity of the network, achieving order-level transfer visibility was crucial. The solution needed to handle cases where transfers between distribution centers were often cross-docked, meaning they were not taken off the truck but directly transferred to fulfill orders. This level of visibility allowed the distributor to manage inventory more effectively, reducing delays and ensuring timely deliveries.
4. Real-Time Optimization and Execution: One of the most significant challenges was the need to run the optimization in near real-time. Due to a tight order cutoff window, the team needed to ensure that cuts and orders reached the warehouse within an hour. This requirement necessitated multiple optimization runs per day to accommodate different time zones, a sharp departure from the traditional overnight batch processing typically used in replenishment planning. This new approach aligned closely with the operational needs of many consumer packaged goods (CPG) companies and distributors, who require a more responsive and dynamic planning process.
5. Project Duration and Impact: The entire project, from initiation to completion, took 25 weeks. This relatively short timeframe was essential given the project’s complexity and the need for a rapid transformation of the distributor’s replenishment planning capabilities. By the end of the project, the distributor had implemented a more agile and efficient replenishment process, allowing them to respond more effectively to changes in demand and supply conditions.

Conclusion

This case study demonstrates the transformative impact of a carefully designed replenishment planning solution tailored to a distributor’s specific needs. By addressing circularity issues, optimizing demand-supply matching, achieving order-level visibility, and implementing real-time optimization, the project enabled the distributor to enhance operational efficiency, reduce costs, and improve service levels across its network. This approach represents a forward-thinking model for other distributors and CPG companies looking to modernize their replenishment planning strategies.