A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques

Abstract

In supply chain management, selection of suitable suppliers and allocating corresponding orders are two essential strategic decisions. Making these decisions is a complex process due to some uncertain parameters, such as future demand. This study proposes a three-stage solution framework to solve problems with supplier selection & order allocation planning. In Stage 1, a new modified relational deep learning forecasting technique is developed to forecast the demands of products. In this part, the efficiency of this modified technique is compared with two well-known forecasting techniques, namely Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Light-Gradient Boosted Machine (LGBM). In Stage 2, a new hybrid principal component analysis method is used to generate suppliers’ weights. The results from Stages 1 and 2 are used in the multiple objectives optimization model which is developed in Stage 3. The hybrid method is used to derive a set of efficient solutions. The developed framework is discussed using a real dataset from the Canadian meat industry. The results of forecasting models show that the developed deep learning network can reduce the forecasting error by 55.42% when compared to the SARIMA method, and 13.1% when compared to the LGBM method. It is also observed that the consideration of inter-product correlation functions can change the selected suppliers and the corresponding orders.