Reverse logistics: an optimal return policy based on a simulation approach

Based on increasing legislative constraints and charges, emerging from the undesired environmental effects of waste disposal and from the limited availability of non-renewable natural resources, but also, and not secondly, on the prospect of a potential economic profitability of remanufacturing solutions, a growing interest from businesses for market-returned used products has risen in recent times. In order to attract customers to return their used products, businesses may decide to reward backward flows with a financial incentive (hereafter referred to as “return policy”). However, one complicating factor in such a decision problem is represented by the uncertainty affecting demand and returns. Uncertainties may be caused by both forecasting errors and stochastic behaviour of the underlying customer-related decision processes. The aim of this paper is to analyze the influence of demand and return uncertainties on the optimal return policy a firm should establish in order to minimize the expected total cost. We propose a simulation approach assessing the effect of stochastic variability of product demand, return rate and return delay on the optimal return policy for an elementary Reverse Logistics system. The system considered is made up of two basic supply chain echelons, namely a manufacturer and several customers. The simulation-underlying model is designed as a dynamic framework, which takes into account the evolution in time of variables within a given planning horizon, and hence could be also adapted to the case of product featured by seasonal demand or to short product life cycle environments.