Supply chain optimization modeling in uncertain environment with prediction mechanism

Abstract

PurposeThe purpose of this paper is to explore new methods to improve supply chain management in uncertain environment, more specifically, to tackle the uncertain demand problem and the inventory optimization problem faced by most supply chain systems.Design/methodology/approachThe paper develops a multi‐objective inventory optimization model, which combines the classic grey prediction GM(1,1) model with the metaheuristic method. The former is applied to achieve the forecasting mechanism in supply chain operations, and the latter is applied to optimize the model solution.FindingsResults show that the grey‐based forecasting mechanism performs better than other prediction methods, such as the double exponential smoothing method used in this paper. The solution of the multi‐objective inventory optimization model is also improved with the integration of grey prediction method. These indicate the importance of a forecasting mechanism in supply chain management.Originality/valueThe paper succeeds in constructing a novel inventory optimization model and in providing a novel supply chain management framework. It shows for the first time that grey prediction method combined with metaheuristic method may be a valid approach to supply chain management under uncertain environment.