Data-driven supplier evaluation in complex product industry value chain for brand value co-creation

Abstract

In the context of big data, it is an excellent challenge to evaluate suppliers under a brand value (BV) co-creation scenario due to the complexity of the BV co-creation process, as well as the nature of the multilayer complex network (MCN) relationships among suppliers in a complex product industrial value chain (CPIVC). To improve the BV satisfaction and accuracy of supplier evaluation results and to offer manufacturers effective decision-support in selecting suppliers that create BV, a data-driven supplier decision-making method comprising a two-stage model is proposed under BV orientation. In the first stage, the supplier relationship model is developed to elicit the autocorrelation importance based on MCN theory and supplier data. In the second stage, the supplier autocorrelation importance and industrial value chain data are integrated into the Quality Function Deployment (QFD) process to transform BV demands into value activities, and further into suppliers, with the purpose of calculating the importance of each supplier. Finally, the applicability and superiority of this method are illustrated by carrying out a case study using the real data collected from the air-conditioning industrial value chain. The proposed method takes into account the essence of MCN relationships among suppliers, making assessment results more accurate and realistic. It not only provides manufacturers with an effective decision-making tool to select suppliers for co-creating BV, and but also contributes to an innovative method for data-driven supplier evaluation of CPIVC.