A stochastic model for the implementation of postponement strategies in global distribution networks

When designing global production and distribution systems an important aspect becoming more and more relevant is the question of how to deal with demand uncertainties. Due to the proliferation of product variants that have to be handled in production and distribution, long lead times due to overseas transportation and increasingly volatile, uncertain and market specific demands, an appropriate concept to deal with these problems has to be established. One concept that has been proposed but not yet been fully explored in this context is postponement. In this concept the customization and finalization of a product is procrastinated, i.e., the final products are not completed in factories but in facilities of a distribution network that are located on the network from factories to customers. This may also entail a resequencing of manufacturing steps.Until now, research has mainly focused on general statements regarding advantages and disadvantages of postponement strategies. In contrast, quantitative models that allow for decision making for specific postponement implementations are rare, particularly models that explicitly take into account stochastic demands and long lead times. In order to allow for decision making for specific postponement implementations in uncertain environments, we present a two-stage stochastic mixed integer linear programming model in this paper. We design a case study inspired by decision support issues in the apparel industry. By means of this case study we show that the presented model formulation can support managers to determine an appropriate production and distribution network in uncertain environments. Benefits from the concept of postponement are exemplified using (commercially) available mathematical programming software.