首页|Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

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Supplier selection is an important business activity in order to realize the purchasing function in supply chain management.The supplier selection process includes four stages,i.e.,bidding inviting,bidding,group decision-making,and results disclosure,involving the participation of manufacturing service demanders(MSDs),manufacturing service suppliers(MSSs),and decision-makers.Nowadays,all the participants have raised concerns about the increased transparency in supplier selection.Therefore,this study proposes a transparent supplier selection method by considering the engagement of suppliers.In this method,the Bayesian best-worst method(Bayesian BWM)is used to aggregate decision-makers'preferences into the overall optimal weights of the alternative MSSs,and the MSS with the largest weight is considered the suitable MSS for MSDs.Furthermore,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract,where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making process.Finally,a case study of supplier selection in purchasing vibration acceleration sensors is presented.The result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers'preferences,and blockchain can provide credible information about the supplier selection process for MSSs,MSDs,and decision-makers.In this way,the transparency of supplier selection is enhanced.

blockchainsupplier selectionsupply chain transparencyBayesian BWMgroup decision-making

LIU JiaJun、ZHANG Jie、LENG JieWu

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Shanghai Engineering Research Center of Industrial Big Data and Intelligent System,Institute of Artificial Intelligence,Donghua University,Shanghai 201620,China

State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment,Guangdong University of Technology,Guangzhou 510006,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaYoung Elite Scientists Sponsorship Program by the China Association for Science and Technology

52375485522754782021QNRC001

2024

10.1007/s11431-024-2677-0

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(8)