Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement

Abstract

Multi-attribute decision-making (MADM) is the most commonly used methodology for green supplier evaluation and performance improvement. Previous MADM models have mainly relied upon the knowledge and opinions of domain-experts as the starting point for making decisions. However, the results are affected by the subjectivity of these judgments and knowledge limitations. This study develops a data-driven MADM model that utilizes potential rules/patterns derived from a large amount of historical data to help decision-makers objectively select suitable green suppliers and provide systemic improvement strategies to help reach the aspiration level. First, the random forest (RF) algorithm is applied to explore the pairwise influential strength relations among attributes derived from real audit data. The influence matrix derived using the RF algorithm is used as input for decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process analysis which is carried out to obtain the influential strength weights of the attributes. Then, multi-objective optimization on the basis of ratio analysis to the aspiration level (MOORA-AS) is utilized to evaluate the gap between the current and aspiration levels for each green supplier. The developed critical influence strength route (CISR) can help managers derive various strategies for improving green supplier performance. The functioning of the proposed model is illustrated using data obtained from the green supplier management department of a Taiwanese electronics company. The results reveal that the proposed model can effectively help decision-makers to solve the problem of green supplier selection and devise strategies for improvement.