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The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

The study aims to present the hybrid approach of multiple MCDM techniques with strategic perspective to assist the vendor ranking process.

Multiple MCDM techniques such as fuzzy QFD, mathematical modelling and ANP (analytical network process) are integrated in the model for vendor ranking. Multiple phases in vendor ranking such as pre‐qualification and final selection are dealt with using the above techniques.

Compared to individual approaches, the proposed hybrid model effectively assists the vendor ranking process. The efficacy of the proposed approach is evident from the case study of an automotive components manufacturer involving 20 vendors comprising pre‐qualification by fuzzy QFD and final selection by ANP. This set of potential vendors is evaluated based on three main criteria and eight sub criteria.

Fuzzy QFD is employed for qualifying supplier to form a supplier pool, as it is helpful in converting qualitative information into quantitative parameters. This data is then combined with other quantitative data to form a mathematical model. The mathematical model is solved by the method of integer programming, using TORA. ANP with BOCR (benefits, opportunities, costs, and risks) is proposed for evaluating and selecting appropriate supplier. ANP model is solved using Super Decision package.