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

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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 develo** 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.

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Notes

  1. http://www.sqlite.org/index.html

  2. The problem seems to be suited for application spatial regression techniques. However, this approach requires the availability of independent variables over the whole search space. In our experiment setup we only record sensor readings at the edges of the mold. Furthermore, Tobler’s law of spatial auto-correlation (Tobler 1970) is violated in the experimental data: In each run there is a single leak and thus a leak at a given point has no direct effect on the probability of a leak occurring in neighboring positions. Hence, classic spatial regression approaches such as Kriging or geographically weighted regression can not be applied for the problem at hand.

  3. The alternative formulation of maximizing the likelihood of detection is not applicable in our manufacturing scenario.

  4. Another difference to this canonical formulation is the absence of step-level probabilities due to our point forecasts.

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Authors and Affiliations

  1. Faculty of Business Management and Economics, Julius-Maximilians-Universität Würzburg, Sanderring 2, 97070, Würzburg, Germany

    Nikolai Stein, Jan Meller & Christoph M. Flath

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  1. Nikolai Stein
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  2. Jan Meller
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  3. Christoph M. Flath
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Correspondence to Christoph M. Flath.

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Stein, N., Meller, J. & Flath, C.M. Big data on the shop-floor: sensor-based decision-support for manual processes. J Bus Econ 88, 593–616 (2018). https://doi.org/10.1007/s11573-017-0890-4

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  • DOI: https://doi.org/10.1007/s11573-017-0890-4

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