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Process improvement in an Indian automotive part manufacturing company: a case study

Author

Listed:
  • M.L. Meena
  • R. Jain
  • P. Kumar
  • S. Gupta
  • G.S. Dangayach

Abstract

Problem-solving and ongoing procedure enhancements are key elements to obtaining quality improvement in business operations. Many process improvement strategies have been suggested and implemented in organisations, where define, measure, analysis, improve and control (DMAIC) is mostly applied. This study presents a practical application of an improved version of DMAIC, for reducing the defects generated through a process within the auto part manufacturing firm. The paper reviews the most commonly used lean and Six Sigma tools, explicitly, DMAIC, its modifications, and restrictions. Based on this, the study provides define, measure, pre-analysis, experiment, analysis, improve, and control (DMPEAIC) methodology. Finally, DMPEAIC is tested in a case study. The results obtained from the case study shows that DMPEAIC is an efficient approach resulting in the case study company to get a reduction of 76.4% defects in problems related to maintenance methods, and informal issues.

Suggested Citation

  • M.L. Meena & R. Jain & P. Kumar & S. Gupta & G.S. Dangayach, 2018. "Process improvement in an Indian automotive part manufacturing company: a case study," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 23(4), pages 524-551.
    • Handle: RePEc:ids:ijpqma:v:23:y:2018:i:4:p:524-551
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    Cited by:

    1. Santosh B. Rane & Sandesh Wavhal & Prathamesh R. Potdar, 2023. "Integration of Lean Six Sigma with Internet of Things (IoT) for productivity improvement: a case study of contactor manufacturing industry," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1990-2018, October.
    2. Xiaohan Li & Chenwei Ma & Yang Lv, 2022. "Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse," Sustainability, MDPI, vol. 14(18), pages 1-21, September.

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