Case Study: Near Realtime Replenishment Planning

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 handles 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. The company wished to replace a hybrid ERP plus manual process that consumed considerable time from employees at the cutoff window every day. By implementing the new process, the company was able to free time from employees saving hundreds of thousands a year and get a more consistent approach to allocating inventory to customer orders.

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. Planning Optimization Process: The replenishment planning run was designed to operate seamlessly, much like an Azure or Google function. The company would send items, networks, sourcing, customer orders, and other data to the optimization system, which would process the information and, within about an hour, return an optimized plan ready for execution. This plan could then be directly integrated into the company’s ERP and warehouse management systems, allowing for immediate action. This quick turnaround enabled the distributor to maintain agility and responsiveness in their supply chain operations, critical for meeting tight order deadlines and managing complex inventory needs.

6. 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.

At K3 Group, we specialize in solving complex replenishment optimization challenges. If you’re looking to enhance your supply chain efficiency and streamline your operations, we’re here to help.

About K3 Group

At K3 Group, we specialize in implementing tailored systems that drive efficiency and streamline operations. Our expertise covers a wide range of solutions, including supply chain inventory optimization and inventory planning software to ensure your business is always prepared for demand fluctuations. We support BlueYonder Fulfillment (formerly JDA), Kinaxis RapidResponse, and o9 MEIO systems, integrating them into your overall replenishment planning process to improve accuracy, reduce costs, and enhance service levels across your supply chain. Let us help you optimize your fulfillment and inventory strategies for maximum results.

• Category: Replenishment