Fuzzy supplier selection model based on lifetime performance index

Abstract

In view of the impact of global warming, the concept of a circular economy (CE) is receiving much attention from corporations and governments. Many studies have suggested that when products and components are of high quality and long lifetime, they are more easily repaired after breakdown, making it more likely that they will be re-used. Longer product lifetimes also reduce carbon emissions and the cost of maintenance. Therefore, improving the quality and lifetime of products is key to the implementation of CE. However, in supplier selection, product lifespan is often overlooked in favor of product quality and the ability to deliver on time. This study therefore used a lifetime performance index as a tool for supplier selection to ensure the reliability of the final product. Due to the fact that the index contains unknown parameters, we derived the confidence interval of the index and examined the influence of sample size on confidence interval length and statistical inference accuracy. Based on cost and effectiveness considerations, we constructed a fuzzy membership function using the confidence interval of the index to increase testing accuracy and overcome uncertainty in measurement data. We further propose a fuzzy hypothesis testing method to aid in the selection of suppliers with good product lifetime performance. This method is grounded on the confidence interval of the index and can thus lower the chance of erroneous judgment caused by sampling error. At the same time, it increases testing accuracy. We also present a numerical example to demonstrate the efficacy of the proposed method.