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Sustainable supply chain decision-making in the automotive industry: A data-driven approach

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  • Zareian Beinabadi, Hanieh
  • Baradaran, Vahid
  • Rashidi Komijan, Alireza

Abstract

The auto parts manufacturing sector faces multifaceted challenges ranging from production planning to sustainability imperatives, necessitating innovative solutions. This study presents an integrated data-driven approach tailored to address these challenges. Leveraging advanced AI techniques, including Convolutional and Recurrent Neural Networks optimized with the Moth-flame Optimization Algorithm (MFO), we accurately predict demand quantities for automotive components. Through empirical validation with Iranian auto parts manufacturers, our model achieves an impressive accuracy rate of over 90 %. Subsequently, Data Envelopment Analysis (DEA) evaluates suppliers not only based on demand quantities but also on their social, economic, and environmental impacts, with a resulting average efficiency score of 0.75. The Best-Worst Method (BWM) further refines supplier selection, leading to the identification of top-performing suppliers with an average score of 0.8. This comprehensive approach enables auto parts manufacturers to optimize production planning processes while aligning with sustainable development goals. The successful application of our model underscores the transformative potential of integrating business analytics and AI in the automotive industry towards sustainability.

Suggested Citation

  • Zareian Beinabadi, Hanieh & Baradaran, Vahid & Rashidi Komijan, Alireza, 2024. "Sustainable supply chain decision-making in the automotive industry: A data-driven approach," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:soceps:v:95:y:2024:i:c:s0038012124001071
    DOI: 10.1016/j.seps.2024.101908
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    References listed on IDEAS

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    1. Ajit Sharma & Zhibo Zhang & Rahul Rai, 2021. "The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4960-4994, August.
    2. Vincent Charles & Ali Emrouznejad & Tatiana Gherman, 2023. "A critical analysis of the integration of blockchain and artificial intelligence for supply chain," Annals of Operations Research, Springer, vol. 327(1), pages 7-47, August.
    3. Sueyoshi, Toshiyuki & Goto, Mika, 2011. "DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation," Energy Economics, Elsevier, vol. 33(2), pages 292-303, March.
    4. Lixiong Gong & Canlin Wang, 2019. "Model of Automobile Parts Sale Prediction Based on Nonlinear Periodic Gray GM(1,1) and Empirical Research," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, August.
    5. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    6. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    7. Calabrese, G.G. & Falavigna, G. & Ippoliti, R., 2024. "Innovation policy and corporate finance: The Italian automotive supply chain and its transition to Industry 4.0," Journal of Policy Modeling, Elsevier, vol. 46(2), pages 336-353.
    8. Senna, Pedro P. & Bonnin Roca, Jaime & Barros, Ana C., 2023. "Overcoming barriers to manufacturing digitalization: Policies across EU countries," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    9. Mukherjee, Krishnendu, 2014. "Supplier selection criteria and methods: past, present and future," MPRA Paper 60079, University Library of Munich, Germany.
    10. Teixeira, Josélia Elvira & Tavares-Lehmann, Ana Teresa C.P., 2022. "Industry 4.0 in the European union: Policies and national strategies," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    11. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
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