Wal-Mart needs superb supply chain, logistics, forecasting, store ordering, replenishing, planograming, and labor and task management capabilities to make its new small footprint stores profitable. Taking a clue from apparel logistics, it's a good bet that Wal-Mart could look at case-pack optimization to improve distribution center flow-through with cases sized for the sell-through and shelf-capacity profiles of these stores. Shelf-sets and department layouts in its urban stores will vary much more widely than those of its bread-and-butter supercenter stores. This presents a host of new challenges at the nexus of at least space planning, economic order quantity calculations, and inbounds transportation routing.
Fresh Item Management
Carrying fresh produce on a cost basis that saves consumers $1 billion annually represents another challenge where IT will be critical to Wal-Mart's success. To level set, however, while a $1 billion claim can catch headlines, it equates to less than a 1% reduction in retail food prices at Wal-Mart. Unless there's a dramatic shift from long-term deflationary trends in food costs, Wal-Mart will hit this target with ease. Nevertheless the profitable management of perishables requires specialized capabilities in short-term forecasting, order, specifications, PLU, sourcing, and inbound logistics management.
Building out an urban fleet of small footprint stores is another area where predictive analytics will play a key role, in this case three tiers of capabilities in location intelligence. From top to bottom, market analysis which markets to enter and in what order; store network design how many stores will each market support and in a rough-cut approach, where should they be located to efficiently serve different trade areas; and site selectionat a micro-level, on which parcels or at which intersections should each store be placed? This kind of location intelligence requires gravity modeling to measure the relative attraction of each store or store location option in view of the drawing power of nearby complementary attractions and the strength of competitors' stores, the impedance caused by distances and ease of travel to and from daytime and nighttime population centers, and the demographic characteristics and shopping behaviors of those populations.