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Big data on the shop-floor: sensor-based decision-support for manual processes

Author

Listed:
  • Nikolai Stein

    (Julius-Maximilians-Universität Würzburg)

  • Jan Meller

    (Julius-Maximilians-Universität Würzburg)

  • Christoph M. Flath

    (Julius-Maximilians-Universität Würzburg)

Abstract

Analytics applications are becoming indispensable in today’s business landscape. Greater data availability from self-monitoring production equipment allows firms to empower individual workers on the shop-floor with powerful decision support solutions. To explore the potential of such solutions, we replicate an important manual leak detection process from high-tech composite manufacturing and augment the system with highly sensitive sensors. Based on this setup we illustrate the main steps and major challenges in developing and instantiating a predictive decision support system. By establishing a scalable and generic feature generation approach as well as leveraging techniques from statistical learning, we are able to improve the forecasts of the leak position by almost 90%. Recognizing that mere forecast information cannot be evaluated with respect to business value, we subsequently embed the problem in an analysis of the underlying searcher path problem. We compare predictive and prescriptive search policies against simple benchmark rules. The data-supported policies dramatically reduce the median as well as the variability of the search time. Based on these findings we posit that prescriptive analytics can and should play a greater role in assisting manual labor in manufacturing environments.

Suggested Citation

  • Nikolai Stein & Jan Meller & Christoph M. Flath, 2018. "Big data on the shop-floor: sensor-based decision-support for manual processes," Journal of Business Economics, Springer, vol. 88(5), pages 593-616, July.
    • Handle: RePEc:spr:jbecon:v:88:y:2018:i:5:d:10.1007_s11573-017-0890-4
    DOI: 10.1007/s11573-017-0890-4
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    Cited by:

    1. 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.
    2. Hauser, Matthias & Flath, Christoph M. & Thiesse, Frédéric, 2021. "Catch me if you scan: Data-driven prescriptive modeling for smart store environments," European Journal of Operational Research, Elsevier, vol. 294(3), pages 860-873.
    3. Julian Weller & Nico Migenda & Yash Naik & Tim Heuwinkel & Arno Kühn & Martin Kohlhase & Wolfram Schenck & Roman Dumitrescu, 2024. "Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories," Mathematics, MDPI, vol. 12(17), pages 1-36, August.
    4. Martin Schymanietz & Julia M. Jonas & Kathrin M. Möslein, 2022. "Exploring data-driven service innovation—aligning perspectives in research and practice," Journal of Business Economics, Springer, vol. 92(7), pages 1167-1205, September.

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    More about this item

    Keywords

    Prescriptive analytics; Data science; Manufacturing; Internet of things; Optimal search;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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