A novel Kano-QFD-DEMATEL approach to optimise the risk resilience solution for sustainable supply chain

Abstract

Sustainable supply chain management plays an important role in company’s sustainability success. As the frequently emerging disruptions may harm supply chain’s sustainable performance, managers need to design an effective sustainable supply chain (SSC) resilience solution to mitigate different risks. In addition, it is critical to integrate customer requirements (CRs) in SSC resilience solution design as SSC’s performance is highly determined by customer satisfaction (CS) in the customer-oriented market. This article proposes an integrative approach to achieve an effective design of SSC resilience solution, taking both CRs and risk factors (RFs) into consideration. In this study, the Kano model, decision-making and trial evaluation laboratory (DEMATEL) are integrated with quality function deployment (QFD) through nonlinear programming to guide companies’ design. Kano model is employed to analyse CRs qualitatively and quantitatively to determine each CR’s priority and CS. DEMATEL is adopted to determine the RFs’ interrelationships. The results obtained with the Kano model and DEMATEL are incorporated into QFD to transform CRs into RFs, and then into resilience measures. Furthermore, a nonlinear optimisation model is formulated to achieve the optimal resilience solution with maximised CS and risk mitigation under minimum cost investment. Finally, a case study is conducted to verify the methodology.

Keywords:

• sustainable supply chain
• risk
• resilience
• Kano
• quality function deployment
• DEMATEL

Acknowledgements

The authors would like to thank the anonymous reviewers for the valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work is supported by the National Natural Science Foundation of China [grant number 51705436], the Fundamental Research Funds for the Central Universities [grant number 2682017CX034], and the National Science and Technology Major Project [grant number 2017-I-0011-0012].