๐๐ข๐ง-๐๐๐ฑ ๐ฏ๐ฌ ๐๐๐ฆ๐๐ง๐-๐๐๐ฌ๐๐ ๐๐ญ๐จ๐ซ๐ ๐๐๐ฉ๐ฅ๐๐ง๐ข๐ฌ๐ก๐ฆ๐๐ง๐ญ: ๐๐ก๐๐ญ ๐๐๐ญ๐ฎ๐๐ฅ๐ฅ๐ฒ ๐๐จ๐ซ๐ค๐ฌ?
In retail, few decisions are as quietly powerful as store replenishment logic. While it may sit within supply chain or inventory management teams, the ripple effect of replenishment reaches far beyond warehouses and stockrooms. It influences sell-through rates, stock availability, markdown pressure, working capital deployment, and ultimately the customer’s experience in-store and across channels. Yet, many retailers inherit their replenishment model rather than strategically choosing it. Min-Max logic has long been the default approach because it is simple, structured, and predictable. On the other hand, demand-based replenishment promises dynamic responsiveness, using historical sales data and forecasting to adjust stock levels in real time. As retail networks grow and omnichannel complexity increases, this shift toward more data-driven models has accelerated. But the real conversation is not about replacing one method with another. It is about alignment. Min-Max can perform exc...