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Quality 4.0: a review of big data challenges in manufacturing

Author

Listed:
  • Carlos A. Escobar

    (General Motors)

  • Megan E. McGovern

    (General Motors)

  • Ruben Morales-Menendez

    (Tecnológico de Monterrey)

Abstract

Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart manufacturing. Although these driving technologies have the capacity to advance the state of the art in manufacturing, it is not trivial to do so. Current benchmarks of quality, conformance, productivity, and innovation in industrial manufacturing have set a very high bar for machine learning algorithms. A new concept has recently appeared to address this challenge: Quality 4.0. This name was derived from the pursuit of performance excellence during these times of potentially disruptive digital transformation. The hype surrounding artificial intelligence has influenced many quality leaders take an interest in deploying a Quality 4.0 initiative. According to recent surveys, however, 80–87% of the big data projects never generate a sustainable solution. Moreover, surveys have indicated that most quality leaders do not have a clear vision about how to create value of out these technologies. In this manuscript, the process monitoring for quality initiative, Quality 4.0, is reviewed. Then four relevant issues are identified (paradigm, project selection, process redesign and relearning problems) that must be understood and addressed for successful implementation. Based on this study, a novel 7-step problem solving strategy is introduced. The proposed strategy increases the likelihood of successfully deploying this Quality 4.0 initiative.

Suggested Citation

  • Carlos A. Escobar & Megan E. McGovern & Ruben Morales-Menendez, 2021. "Quality 4.0: a review of big data challenges in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2319-2334, December.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-021-01765-4
    DOI: 10.1007/s10845-021-01765-4
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    References listed on IDEAS

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    1. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
    2. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    3. Pedro Malaca & Luis F. Rocha & D. Gomes & João Silva & Germano Veiga, 2019. "Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 351-361, January.
    4. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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