A statistical investigation of inventory shrinkage in a large retail chain

In a large retail chain, we used statistical regression to relate shrinkage to explanatory variables such as staffing, security, store layout and catchment-area demographics. There were large measurement errors in the retailer's estimates of shrinkage, together with high correlations among the potential causal variables. These effects caused our models to give poor predictions of shrinkage for individual stores, but the models were highly statistically significant, which means that they can accurately forecast the average (and hence the aggregate) effects of policy changes affecting hundreds of stores. Factors associated with lower shrinkage included high turnover of stock, and high densities on the sales floor of staff, pay-points and customers. Our results suggest that crowding among staff and customers may be a more effective inhibitor of shrinkage than many traditional formal security precautions, such as CCTV and store detectives.