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Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry

Author

Listed:
  • Nitin S. Solke

    (Symbiosis International (Deemed University))

  • Pritesh Shah

    (Symbiosis International (Deemed University))

  • Ravi Sekhar

    (Symbiosis International (Deemed University))

  • T. P. Singh

    (LM Thapar School of Management)

Abstract

The auto industry is critically dependent on lean and flexible manufacturing systems to sustain in today’s dynamic and price sensitive markets. In the current work, a machine learning-based predictive modeling and control strategy is proposed for the attainment of lean manufacturing (LM) through effective management of manufacturing flexibility. Firstly, lean manufacturing models were identified based on machine, labour, volume, routing, product flexibilities and material handling for forty six auto parts manufacturing companies in Pune region (India). As many as twenty three lean manufacturing models were derived based on system identification (control theory) structures: auto regressive with exogenous variables (ARX), auto regressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ) methods. All predictive models were compared for their relative performances based on validation indices such as the FIT%, mean squared error (MSE), final prediction error (FPE) and the number of model parameters. The ARX 750 structure attained the best predictive characteristics for LM (FIT 91.86%). This model was controlled for a set point of 0.8 LM level and corresponding levels of flexibilities were determined. The machine flexibility (MF) was identified to be the most significant contributor to lean manufacturing at a level of 0.7596. Consequently, MF was also modeled based on its seven sub parameters. The ARMAX 2120 structure obtained the best performance characteristics (FIT 99.94%) for MF modeling. Furthermore, this MF model was controlled at a level of 0.7596 (corresponding to 0.8 LM) and the required levels of MF sub parameters were determined. Thus, the current work provides definite guidelines for company managers to target quantitative attainment of specific machine flexibility parameters to ultimately attain an improved lean manufacturing level in the automotive industry.

Suggested Citation

  • Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
    • Handle: RePEc:spr:gjofsm:v:23:y:2022:i:1:d:10.1007_s40171-021-00291-9
    DOI: 10.1007/s40171-021-00291-9
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    1. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    2. Khayyati, Siamak & Tan, Barış, 2020. "Data-driven control of a production system by using marking-dependent threshold policy," International Journal of Production Economics, Elsevier, vol. 226(C).
    3. Tortorella, Guilherme Luz & Narayanamurthy, Gopalakrishnan & Thurer, Matthias, 2021. "Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. C. Vasanthakumar & S. Vinodh & K. Ramesh, 2016. "Application of interpretive structural modelling for analysis of factors influencing lean remanufacturing practices," International Journal of Production Research, Taylor & Francis Journals, vol. 54(24), pages 7439-7452, December.
    5. Elkazaz, Mahmoud & Sumner, Mark & Naghiyev, Eldar & Pholboon, Seksak & Davies, Richard & Thomas, David, 2020. "A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers," Applied Energy, Elsevier, vol. 269(C).
    6. Sabahi, Sima & Parast, Mahour Mellat, 2020. "The impact of entrepreneurship orientation on project performance: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 226(C).
    7. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
    8. Nitin S. Solke & T. P. Singh, 2018. "Analysis of Relationship Between Manufacturing Flexibility and Lean Manufacturing Using Structural Equation Modelling," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(2), pages 139-157, June.
    9. Huang, Yanjun & Fard, Soheil Mohagheghi & Khazraee, Milad & Wang, Hong & Khajepour, Amir, 2017. "An adaptive model predictive controller for a novel battery-powered anti-idling system of service vehicles," Energy, Elsevier, vol. 127(C), pages 318-327.
    10. José M. Merigó & Claudio Muller & Nikunja Mohan Modak & Sigifredo Laengle, 2019. "Research in Production and Operations Management: A University-Based Bibliometric Analysis," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(1), pages 1-29, March.
    11. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    12. Shakun Preet Kaur & Jatinder Kumar & Rakesh Kumar, 2017. "The Relationship Between Flexibility of Manufacturing System Components, Competitiveness of SMEs and Business Performance: A Study of Manufacturing SMEs in Northern India," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(2), pages 123-137, June.
    13. Somen Dey & R. R. K. Sharma & Balbir Kumar Pandey, 2019. "Relationship of Manufacturing Flexibility with Organizational Strategy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 237-256, September.
    14. Mohd. Shaaban Hussain & Mohammed Ali, 2019. "A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(3), pages 267-290, September.
    15. Shiwangi Singh & Sanjay Dhir & Stuart Evans & Sushil, 2021. "The Trajectory of Two Decades of Global Journal of Flexible Systems Management and Flexibility Research: A Bibliometric Analysis," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(4), pages 377-401, December.
    16. Parker, Rodney P. & Wirth, Andrew, 1999. "Manufacturing flexibility: Measures and relationships," European Journal of Operational Research, Elsevier, vol. 118(3), pages 429-449, November.
    17. Stuart Evans & Homa Bahrami, 2020. "Super-Flexibility in Practice: Insights from a Crisis," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(3), pages 207-214, September.
    18. Luis Mendes & José Machado, 2015. "Employees’ skills, manufacturing flexibility and performance: a structural equation modelling applied to the automotive industry," International Journal of Production Research, Taylor & Francis Journals, vol. 53(13), pages 4087-4101, July.
    19. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    20. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    21. Rajdeep Singh & Neeraj Bhanot, 2020. "An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry," International Journal of Production Research, Taylor & Francis Journals, vol. 58(8), pages 2454-2476, April.
    22. Shivam Gupta & Sachin Modgil & Angappa Gunasekaran, 2020. "Big data in lean six sigma: a review and further research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 947-969, February.
    23. Wei, Zelong & Song, Xi & Wang, Donghan, 2017. "Manufacturing flexibility, business model design, and firm performance," International Journal of Production Economics, Elsevier, vol. 193(C), pages 87-97.
    24. Fard, Soheil Mohagheghi & Huang, Yanjun & Khazraee, Milad & Khajepour, Amir, 2017. "A novel anti-idling system for service vehicles," Energy, Elsevier, vol. 127(C), pages 650-659.
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