Investigating the hidden losses caused by out-of-shelf events: A multi-agent-based simulation

Abstract

Out-of-shelf events refer to periods of time in which items of a certain product are not available to customers. It is clear that incidents of this nature result in economic loss, but their side effects are much more profound: since there is no record of missed sales opportunities, the estimated demand curve tends to be inaccurate. As a result, order placement strategies employed by retailers are based on imprecise forecast models, so further out-of-shelf events are very likely to occur: a vicious cycle, hence, arises. In this work, we propose a multi-agent-based simulation to evaluate the impact of out-of-shelf events that considers the reactions of customers towards these incidents and retailers' ordering strategies. Our results show that these events have a significant effect on demand estimation and that multi-agent-based simulations may provide interesting insights and support for the development of more accurate forecast models in retail.