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Supply Chain Efficiency Measurement to Maintain Sustainable Performance in the Automobile Industry

Author

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  • Illi Kim

    (College of Business Administration, Seoul National University, Seoul 08826, Korea)

  • Changhee Kim

    (College of Business Administration, Incheon National University, Incheon 22012, Korea)

Abstract

The automobile industry is set to undergo a structural transformation in the progress toward next-generation industries that involve autonomous vehicles and connected cars. Thus, supply chain management has become increasingly important for corporate competitiveness. This study aims to identify opportunities for improving supply chain performance by quantifying the impact of suppliers on the supply chain. An analysis was conducted in two phases. First, the efficiency of 139 partners that supply automobile components to the Hyundai Motor Company was measured using the Charnes–Cooper–Rhodes model, while the efficiency of Hyundai Motor Company’s 540 supply chains comprising partners, subsidiaries, and parent companies was measured using the network epsilon-based measure model. Second, the relationship between the partner efficiency and the supply chain efficiency was analyzed using the Mann–Whitney U test and the Tobit regression model. The results showed that efficient operation of partners hampers the efficiency of the total supply chain. Thus, there may be several partners that are not committed to quality improvement, while the Hyundai Motor Company seeks to promote quality management through win–win cooperation with partners. Consequently, automakers must review their partner management system, including their performance measurement and incentive systems.

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  • Illi Kim & Changhee Kim, 2018. "Supply Chain Efficiency Measurement to Maintain Sustainable Performance in the Automobile Industry," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2852-:d:163186
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    as
    1. Zhou, Peng & Poh, Kim Leng & Ang, Beng Wah, 2007. "A non-radial DEA approach to measuring environmental performance," European Journal of Operational Research, Elsevier, vol. 178(1), pages 1-9, April.
    2. Alessandro Manello & Giuseppe G. Calabrese & Piercarlo Frigero, 2016. "Technical efficiency and productivity growth along the automotive value chain: evidence from Italy," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 25(2), pages 245-259.
    3. Weber, Charles A. & Current, John R. & Benton, W. C., 1991. "Vendor selection criteria and methods," European Journal of Operational Research, Elsevier, vol. 50(1), pages 2-18, January.
    4. Chen, Ci & Yan, Hong, 2011. "Network DEA model for supply chain performance evaluation," European Journal of Operational Research, Elsevier, vol. 213(1), pages 147-155, August.
    5. Talluri, Srinivas & Narasimhan, Ram & Nair, Anand, 2006. "Vendor performance with supply risk: A chance-constrained DEA approach," International Journal of Production Economics, Elsevier, vol. 100(2), pages 212-222, April.
    6. Wai Peng Wong & Wikrom Jaruphongsa & Loo Hay Lee, 2008. "Supply chain performance measurement system: a Monte Carlo DEA-based approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 162-188.
    7. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    8. Banker, Rajiv D. & Zheng, Zhiqiang (Eric) & Natarajan, Ram, 2010. "DEA-based hypothesis tests for comparing two groups of decision making units," European Journal of Operational Research, Elsevier, vol. 206(1), pages 231-238, October.
    9. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    10. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    11. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    12. Saranga, Haritha, 2009. "The Indian auto component industry - Estimation of operational efficiency and its determinants using DEA," European Journal of Operational Research, Elsevier, vol. 196(2), pages 707-718, July.
    13. Hoff, Ayoe, 2007. "Second stage DEA: Comparison of approaches for modelling the DEA score," European Journal of Operational Research, Elsevier, vol. 181(1), pages 425-435, August.
    14. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    15. Tariq, Yasir Bin & Abbas, Zaheer, 2013. "Compliance and multidimensional firm performance: Evaluating the efficacy of rule-based code of corporate governance," Economic Modelling, Elsevier, vol. 35(C), pages 565-575.
    16. Chia-Nan Wang & Xuan-Tho Nguyen & Yen-Hui Wang, 2016. "Automobile Industry Strategic Alliance Partner Selection: The Application of a Hybrid DEA and Grey Theory Model," Sustainability, MDPI, vol. 8(2), pages 1-18, February.
    17. Patrick L. Brockett & Boaz Golany, 1996. "Using Rank Statistics for Determining Programmatic Efficiency Differences in Data Envelopment Analysis," Management Science, INFORMS, vol. 42(3), pages 466-472, March.
    18. Pradipta Kumar Sahoo & Badri Narayan Rath, 2018. "Productivity growth, efficiency change and source of inefficiency: evidence from the Indian automobile industry," International Journal of Automotive Technology and Management, Inderscience Enterprises Ltd, vol. 18(1), pages 59-74.
    19. Xu, Xin & Cui, Qiang, 2017. "Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure," Energy, Elsevier, vol. 122(C), pages 274-286.
    20. Ramanathan, Usha & Gunasekaran, Angappa, 2014. "Supply chain collaboration: Impact of success in long-term partnerships," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 252-259.
    21. Arshinder & Kanda, Arun & Deshmukh, S.G., 2008. "Supply chain coordination: Perspectives, empirical studies and research directions," International Journal of Production Economics, Elsevier, vol. 115(2), pages 316-335, October.
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