A diagnostic analysis of retail out-of-stocks

Abstract

In the highly competitive retail industry, merchandise out-of-stock (OOS) is a significant and pertinent problem. This thesis performs a diagnostic analysis on retail out-of-stocks using empirical data from a major retailer. In this thesis, we establish the empirical relationship of OOS rate with the amount of safety stock carried, the time between orders and the forecast error, providing insights to the effects of these three factors on the probability of OOS occurrences. The root causes of OOS are also examined in the thesis. We find that up to 34% of OOS can be attributed to forecast error while up to 22% can be attributed to delay in order replenishment. For the OOSs that were associated with order delay, we can trace 60% of these to out-of-stock at the store's distribution center (DC). The thesis also examines a peculiarity in the occurrence of OOSs. We found that the OOS rate of Class C items is significantly higher in stores with higher sales volume. We can attribute much of this phenomenon to three factors: stores with higher sales volume hold less safety stock for Class C items, have a shorter time between orders and have relatively larger forecast errors.