Skip to main content

A statistical investigation of inventory shrinkage in a large retail chain

Buy Article:

$55.00 plus tax (Refund Policy)


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.

Keywords: Shrinkage; regression modelling; retail crime; theft

Document Type: Research Article


Affiliations: Manchester Business School, University of Manchester, UK

Publication date: 2007-05-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more