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A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation

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  • Kamble, Sachin S.
  • Gunasekaran, Angappa
  • Ghadge, Abhijeet
  • Raut, Rakesh

Abstract

The smart manufacturing systems (SMS) offer several advantages compared to the traditional manufacturing systems and are increasingly being adopted by manufacturing organizations as a strategy to improve their performance. Developing an SMS is expensive and complicated, integrating together various technologies such as automation, data exchanges, cyber-physical systems (CPS), artificial intelligence, internet of things (IoT), and semi-autonomous industrial systems. The Small, Medium and Micro Enterprises (SMMEs) have limited resources and therefore, would like to see the benefits from investments before allowing adopting SMS. This study uses a combination of exploratory and empirical research design to identify and validate the performance measures relevant to the evaluation of SMS investments in auto-component manufacturing SMMEs based in India. The study found that an Industry 4.0 enabled SMS offer more competitive benefits compared to a traditional manufacturing system. The planned investments in SMS can be evaluated on ten performance dimensions namely, cost, quality, flexibility, time, integration, optimized productivity, real-time diagnosis & prognosis, computing, social and ecological sustainability. Proposed novel Smart Manufacturing Performance Measurement System (SMPMS) framework is expected to guide the practitioners in SMMEs to evaluate their SMS investments.

Suggested Citation

  • Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
  • Handle: RePEc:eee:proeco:v:229:y:2020:i:c:s0925527320302176
    DOI: 10.1016/j.ijpe.2020.107853
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    References listed on IDEAS

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    1. Sachin Kamble & Angappa Gunasekaran & Himanshu Arha, 2019. "Understanding the Blockchain technology adoption in supply chains-Indian context," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2009-2033, April.
    2. Christoph Müller & Martin Grunewald & Thomas Stefan Spengler, 2018. "Redundant configuration of robotic assembly lines with stochastic failures," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3662-3682, May.
    3. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1941-1952, December.
    4. Sachin S. Kamble & Angappa Gunasekaran, 2020. "Big data-driven supply chain performance measurement system: a review and framework for implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(1), pages 65-86, January.
    5. Pedro J. Rivera Torres & Eileen I. Serrano Mercado & Orestes Llanes Santiago & Luis Anido Rifón, 2018. "Erratum to: Modeling preventive maintenance of manufacturing processes with probabilistic Boolean networks with interventions," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1953-1953, December.
    6. Alqahtani, Ammar Y. & Gupta, Surendra M. & Nakashima, Kenichi, 2019. "Warranty and maintenance analysis of sensor embedded products using internet of things in industry 4.0," International Journal of Production Economics, Elsevier, vol. 208(C), pages 483-499.
    7. Zhen-Yao Chen & R. J. Kuo, 2019. "Combining SOM and evolutionary computation algorithms for RBF neural network training," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1137-1154, March.
    8. Kamble, Sachin S. & Gunasekaran, Angappa & Parekh, Harsh & Joshi, Sudhanshu, 2019. "Modeling the internet of things adoption barriers in food retail supply chains," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 154-168.
    9. Yulin Wang & Yongping Zhang & Fei Tao & Tingyu Chen & Ying Cheng & Shunkun Yang, 2019. "Logistics-aware manufacturing service collaboration optimisation towards industrial internet platform," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 4007-4026, June.
    10. Silviu Raileanu & Florin Anton & Alexandru Iatan & Theodor Borangiu & Silvia Anton & Octavian Morariu, 2017. "Resource scheduling based on energy consumption for sustainable manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1519-1530, October.
    11. Tortorella, Guilherme Luz & Cawley Vergara, Alejandro Mac & Garza-Reyes, Jose Arturo & Sawhney, Rapinder, 2020. "Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers," International Journal of Production Economics, Elsevier, vol. 219(C), pages 284-294.
    12. Gregory W. Vogl & Brian A. Weiss & Moneer Helu, 2019. "A review of diagnostic and prognostic capabilities and best practices for manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 79-95, January.
    13. Shradha A. Gawankar & Angappa Gunasekaran & Sachin Kamble, 2020. "A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1574-1593, March.
    14. Daniel Kiel & Julian M. Müller & Christian Arnold & Kai-Ingo Voigt, 2017. "Sustainable Industrial Value Creation: Benefits And Challenges Of Industry 4.0," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(08), pages 1-34, December.
    15. Randolph B. Cooper & Robert W. Zmud, 1990. "Information Technology Implementation Research: A Technological Diffusion Approach," Management Science, INFORMS, vol. 36(2), pages 123-139, February.
    16. Stoyan Stoyanov & Mominul Ahsan & Chris Bailey & Tracy Wotherspoon & Craig Hunt, 2019. "Predictive analytics methodology for smart qualification testing of electronic components," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1497-1514, March.
    17. Guilherme Francisco Frederico & Jose Arturo Garza-Reyes & Anil Kumar & Vikas Kumar, 2020. "Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 70(4), pages 789-807, May.
    18. Yang Fu & Yun Zhang & Huang Gao & Ting Mao & Huamin Zhou & Ronglei Sun & Dequn Li, 2019. "Automatic feature constructing from vibration signals for machining state monitoring," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 995-1008, March.
    19. Xiaoming Qian & Jiachen Tu & Peihuang Lou, 2019. "A general architecture of a 3D visualization system for shop floor management," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1531-1545, April.
    20. Kiel, Daniel & Arnold, Christian & Voigt, Kai-Ingo, 2017. "The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective," Technovation, Elsevier, vol. 68(C), pages 4-19.
    21. Ullah Saif & Zailin Guan & Chuangjian Wang & Cong He & Lei Yue & Jahanzaib Mirza, 2019. "Drum buffer rope-based heuristic for multi-level rolling horizon planning in mixed model production," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3864-3891, June.
    22. Kamble, Sachin S. & Gunasekaran, Angappa & Gawankar, Shradha A., 2020. "Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications," International Journal of Production Economics, Elsevier, vol. 219(C), pages 179-194.
    23. Sachin Kamble & Angappa Gunasekaran & Neelkanth C. Dhone, 2020. "Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1319-1337, March.
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