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Feb 20, 2024

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Demand Forecasting and Sourcing When Wal-Mart China tasked the Senior VP of Supply Chain Management with expanding their perishable DC operation and reducing costs, she anticipated encountering various challenges. Initially, she needed to guarantee that it became a top-tier perishable distribution operation, setting a benchmark for efficient DC operations across the industry. Additionally, she aimed to ensure it could facilitate profitable growth and, most crucially, to make it customer centric. This involved minimizing costs to provide customers with the freshest products at consistently low prices. "Wal-Mart found itself in a distinctive position when embarking on this project," as noted by Johnson in 2015. The challenges faced were not confined to the supply chain but encompassed broader enterprise issues, necessitating business transformation where the supply chain played a pivotal role. One significant inefficiency arose from the decentralized regional purchasing structure, with 29 autonomous buying offices in China serving 5 DCs and over 125 stores before centralization. This fragmentation hindered bulk buying leverage, forcing Wal-Mart to deal with over 20,000 suppliers, primarily distributors, leading to issues like unmet order minimums and increased carrying costs. Centralizing purchasing aimed to enhance efficiency, combining cross-dock flow and staple stock flow, enabling consistent service and product fill- rates for all stores, regardless of their distance from suppliers, resulting in heightened in-stock positions and increased sales while reducing supply chain costs. Demand forecasting plays a crucial role in enabling companies to comprehend their supply chain dynamics and meet orders promptly. Wal-Mart China encounters various constraints in demand forecasting daily, such as supplier limitations involving order minimums

or maximums, as well as manufacturing constraints preventing the production of sufficient products to meet demand. Logistical constraints, including vehicle and route limitations, transport calendars, and load route consolidation rules, further impede accurate demand forecasting for Wal-Mart. Additionally, the strategic placement of the Distribution Center (DC) in southern China strategically positions it for optimal accessibility to all 128 stores, ensuring a weighted average distance to stores of approximately 170 kilometers. This strategic placement aids in standardizing costs across the distribution network and reduces storage time by swiftly moving products to stores instead of storing them in the DCs. The existing multitude of suppliers for Wal-Mart China is suboptimal for efficiently managing all 128 stores and 5 DCs. The majority being listed as distributors leads to confusion and prolonged processes in the supply chain. To enhance supplier management, it is crucial to identify strategic suppliers and centralize purchasing to capitalize on buying power. Furthermore, reducing high carrying costs involves employing different flow models for DC inventory maintenance. For staple stock items, carrying them in inventory helps minimize stockouts and maintain stocked shelves, facilitated by easier demand forecasting due to reliable sales history and reports. Conversely, for items with less historical data, the cross-dock model proves more suitable. In this model, Wal-Mart relies on suppliers to fulfill orders by shipping full truckloads directly to stores, streamlining the process of adding SKUs to the distribution list while posing challenges for route additions due to the swift turnaround time required for order fulfillment. I propose implementing sourcing optimization software, such as Arriba, to enhance the organization of suppliers, items, costs, lead times, and crucial fulfillment information. Automating these processes with software can eradicate human errors and ensure that all necessary information is readily accessible when needed. This sourcing optimization software

facilitates effective management of purchasing, spend, contracts, e-catalogs, and other supplier- related data. Additionally, I recommend streamlining the purchasing process by eliminating decentralized purchasing. Centralizing purchasing through a large network ensures the fulfillment of all order minimums and maximums, aligns pricing with volume, and ensures suppliers have a demand forecast in harmony with the entire organization's operations rather than specific groups of 6-10 stores. This centralized approach is anticipated to enhance the capabilities of the Distribution Centers (DCs) and contribute to increased sales. References: 1. WALMART CHINA — SUPPLY CHAIN TRANSFORMATION. (2015). Johnson. https://services.hbsp.harvard.edu/lti/links/content-launch 2. Canitz, H. (2024, January 27). Constraints, Constraints, Constraints: Building the Optimal Supply Chain. Logility. https://www.logility.com/blog/constraints-constraints-constraints- building-the-optimal-supply-chain/

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