The key challenge for the retail supply chain is to minimize or eliminate shelf out-of-stock at retail. This implies two things:
- The retail store has the right planogram at the store level with the optimal number of facings of the product and the appropriate backroom inventory to replenish.
- The retailer has the optimal Distribution center inventory so the stores could be replenished periodically to prevent out-of-stocks at the store level.
So this is a large-scale challenge for the retailer to order the correct quantity of the product from the manufacturer at the regional level, then have this get allocated to their distribution centers and stores.
Various forecasting systems attempt to predict store level demand. But this could be a highly inaccurate forecast since demand could be spotty and is also characterized by infrequent demand. The law of small number makes forecast accuracy impossible. The alternative approach is to segment stores into high volume and low-volume stores and establish service levels and inventory levels accordingly.
Once this is done, the aggregate forecast can then be allocated to store level with a confidence level to replenish from. So the demand forecasting at the retail level is an algorithm based on seasonal smoothing complemented with rigorous allocation algorithms.