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Big data in lean six sigma: a review and further research directions

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  • Shivam Gupta
  • Sachin Modgil
  • Angappa Gunasekaran

Abstract

Manufacturing and service organisations improve their processes on a continuous basis to have better operational performance. They use lean six sigma (LSS) projects for process improvement. Therefore, this study aims to investigate the existing literature in LSS and the application of big data analytics (BDA) to have more confident and predictable decisions in each phase of LSS. Fifty-two articles have been identified after a careful and vigilant screening of closely related themes. Future research directions in the big data and LSS have been highlighted on the basis of organisational theories. Review presents an investigation framework consisting of BDA techniques applicable to each phase of LSS in all the dimensions such as volume, variety, velocity and veracity of big data. Review highlights the concerns of big data in LSS such as system design and integration, system performance, security and reliability of data, sustaining the control and conducting the experiments, distributed material and information flow. The review unveils the application of 8 modern organisational theories to big data in LSS with 21 key aspects of related theories and 19 distinct research gaps as opportunities for future research.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:3:p:947-969
    DOI: 10.1080/00207543.2019.1598599
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    Cited by:

    1. Diego Tlapa & Ignacio Franco-Alucano & Jorge Limon-Romero & Yolanda Baez-Lopez & Guilherme Tortorella, 2022. "Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    2. Koc, Kerim & Ekmekcioğlu, Ömer & Işık, Zeynep, 2023. "Developing a probabilistic decision-making model for reinforced sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 259(C).
    3. Shivam Gupta & Sachin Modgil & Piera Centobelli & Roberto Cerchione & Serena Strazzullo, 2022. "Additive Manufacturing and Green Information Systems as Technological Capabilities for Firm Performance," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(4), pages 515-534, December.
    4. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    5. Lima Rui M. & Souza Ingrid & Pereira Eduarda & Belém Ana Cristina & Pinto Cátia Marlene & Lazzaris Joana & Fonseca Pedro, 2023. "Characterising project management of lean initiatives in industrial companies — crossing perspectives based on case studies," Engineering Management in Production and Services, Sciendo, vol. 15(1), pages 57-72, March.
    6. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    7. Sachin Modgil & Rohit Kumar Singh & Cyril Foropon, 2022. "Quality management in humanitarian operations and disaster relief management: a review and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 1045-1098, December.
    8. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    9. Eva Nedeliakova & Maria Hudakova & Matej Masar & Lenka Lizbetinova & Renata Stasiak-Betlejewska & Peter Šulko, 2020. "Sustainability of Railway Undertaking Services with Lean Philosophy in Risk Management—Case Study," Sustainability, MDPI, vol. 12(13), pages 1-28, June.
    10. Diego Tlapa & Guilherme Tortorella & Flavio Fogliatto & Maneesh Kumar & Alejandro Mac Cawley & Roberto Vassolo & Luis Enberg & Yolanda Baez-Lopez, 2022. "Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review," IJERPH, MDPI, vol. 19(15), pages 1-23, July.
    11. Shang Shanshan & Lyv Wenfei & Luo Lijuan, 2022. "Applying lean six sigma incorporated with big data analysis to curriculum system improvement in higher education institutions," 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. 13(2), pages 641-656, April.
    12. J. Vicente Tébar-Rubio & F. Javier Ramírez & M. José Ruiz-Ortega, 2023. "Conducting Action Research to Improve Operational Efficiency in Manufacturing: The Case of a First-Tier Automotive Supplier," Systemic Practice and Action Research, Springer, vol. 36(3), pages 427-459, June.
    13. 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.
    14. Margherita Bernabei & Marco Eugeni & Paolo Gaudenzi & Francesco Costantino, 2023. "Assessment of Smart Transformation in the Manufacturing Process of Aerospace Components Through a Data-Driven Approach," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 67-86, March.

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