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Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

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  • Ashfaq Farooqui
  • Kristofer Bengtsson
  • Petter Falkman
  • Martin Fabian

Abstract

The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner – a tool for modelling and analysing production systems – and is currently implemented at an automotive company as a pilot project.

Suggested Citation

  • Ashfaq Farooqui & Kristofer Bengtsson & Petter Falkman & Martin Fabian, 2020. "Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations," International Journal of Production Research, Taylor & Francis Journals, vol. 58(16), pages 4947-4963, July.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:16:p:4947-4963
    DOI: 10.1080/00207543.2020.1735660
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    Cited by:

    1. 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).

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