Supplier selection and order allocation planning using predictive analytics and multi-objective programming

Abstract

Supplier Selection and Order Allocation (SSOA) are two critical strategic decisions in supply chain management. It is challenging to make these decisions when the demand is unknown. Prediction of demand is a complex problem as it highly depends on some parameters such as product cost. In this research, a three-stage framework is proposed to tackle the hurdles of SSOA planning problem. In the first stage, a hybrid deep learning technique based on multistep Long-Short Term Memory (LSTM) network is developed to determine the future product demands. The efficiency of the developed model is evaluated using two standard error measuring techniques. Then, the results are compared with two other forecasting models to have accurate forecast. One of them is Seasonal Auto-Regressive Integrated Moving Average (SARIMA), and the other one is a deep learning model named Multilayer Perceptron (MLP). In the second stage, a fuzzy supplier evaluation model based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) model is developed to consider qualitative criteria. The third stage fetches the results from the forecasting model in Stage 1, and the results of the fuzzy model in the second stage. A unique multi-objective programming model is developed to select the best suppliers and to determine the allocated orders to them. To derive the efficient solutions, the weighted-sum method is used. The application of the proposed framework is discussed using a real dataset from the Canadian Juice industries. The results of the performance comparison among the considered forecasting models show that the developed LSTM model can lead to less forecasting errors compared to the SARIMA and MLP models.