Mathematical modeling and Bayesian estimation for error-prone retail shelf audits

a r t i c l e i n f o Prevalent execution errors such as out-of-stock, inventory record inaccuracy, and product misplacement jeopardize retail performance by causing low on-shelf availability, which discourages not only retailers who have lost sales but also manufacturers who have worked hard to deliver goods into retail stores. Thus, external service companies are hired by manufacturers to conduct manual inspection regularly. Motivated by the practical need of shelf audit service providers, we use a general cost structure to develop a decision support model for periodic inspection. Some qualitative insights about the intricate relationships among inspection efficacy, cost factors, failure rate of shelf inventory integrity, and optimal decisions are derived from analytics assuming risk-neutrality. From simulation experiments we also find that managers' risk preferences have non-trivial impacts on optimal decisions. Based on a total cost standpoint high-quality inspection is predominantly preferred regardless of the level of risk aversion. Finally, we propose a Bayesian statistical model and a Markov chain Monte Carlo approach to estimate model parameters such that managers can make empirically informed decisions. Our major contribution lies in developing a mathematical model that is practically applicable and proposing a Bayesian estimation approach to rationalize unobservable model parameters, which are influential to optimal decisions but often arbitrarily assumed by decision makers. Retail operations is composed of various tasks pertaining to assortment planning, product pricing, inventory optimization, and store execution [20]. Among those tasks, store execution is highly labor-extensive and complicated because it involves people, processes, and technology. Thus, execution errors such as shelf out-of-stock (OOS), inventory record inaccuracy (IRI), and product misplacement have become norms rather than anomalies even at financially successful retailers [47]. Store execution errors jeopardize retail performance by resulting in low on-shelf availability, which discourages not only retailers who have lost sales but also other supply chain members who have worked hard to deliver goods into the retail outlet. Being well-known for its operational excellence, Walmart recently admitted to a low on-shelf availability issue and predicted a $3 billion opportunity in filling in empty shelves [13]. Facing prevalent issues pertaining to on-shelf availability, retailers have gradually seen the need of allocating extra labor capacity to carry out shelf audits in order to reach higher service levels [17]. However, hiring more employees who are able to execute prescribed tasks and fix shelf errors goes against the common practice in retailing to minimize labor cost …