An integrated decision-making approach for global supplier selection and order allocation to create an environment-friendly supply chain

Due to the fierce market competition, many organizations seek global suppliers because of lower procurement costs and better product quality. However, selecting suitable global suppliers is one of the complicated decision-making tasks for decision-makers due to the involvement of various qualitative and quantitative factors. The primary purpose of this research is to design an integrated approach for global supplier selection and order allocation in the context of developing an environment-friendly supply chain under data uncertainty.

Initially, the fuzzy analytical hierarchy process (FAHP) is used to calculate the selected criteria weights. After that, the weights obtained from FAHP are inserted into the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to examine the performance of selected suppliers and determine their final ranks. Finally, the obtained results from FTOPSIS are incorporated into the multi-choice goal programming (MCGP) model, which involves multi-aspiration levels to allocate the optimal order quantity to the selected global suppliers.

A real-time case study of the automotive industry is presented to demonstrate the efficiency and practicality of the suggested approach. The case study and sensitivity analysis results show that the proposed model effectively tackles suppliers' evaluation and order allocation data uncertainty.

Incorporation of risks, environmental management and economic factors during global supplier selection in the automotive sector has not been given much attention in the past literature. So, this research aims to fulfill the gap by developing an integrated approach that can tackle data uncertainty effectively.