Production quantity allocation for order fulfilment in the supply chain: a neural network based approach

Abstract

In the current global business environment, it is very important to know how to allocate products from the producer to buyers (or distributors). If products are not appropriately distributed due to absence of an effective allocation policy, the producer and buyers cannot expect to increase customer satisfaction and financial profit. Sometimes some buyers can order more than the actual demand due to inappropriately forecasting customer orders. This is the big obstacle to the effective allocation of products. If the producer can become aware of buyers actual demands, it is possible to realise high-level order fulfilment through the effective allocation of products. In this study, new allocation policies are proposed considering buyers demands. The back propagation algorithm, one of the learning algorithms in neural network theory, is used to recognise actual demands from the previous buyers orders. After excluding surplus demands included in buyers demands, products are allocated to buyers according to one of the existing allocation policies depending on the companys decision. In the numerical examples, new allocation policies reducing buyers surplus demands outperform previous allocation policies with respect to average amount of backorder.