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A framework for performance measurement during production ramp-up of assembly stations

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  • Doltsinis, Stefanos C.
  • Ratchev, Svetan
  • Lohse, Niels

Abstract

Production ramp-up is an important phase in the lifecycle of a manufacturing system which still has significant potential for improvement and thereby reducing the time-to-market of new and updated products. Production systems today are mostly one-of-a-kind complex, engineered-to-order systems. Their ramp-up is a complex order of physical and logical adjustments which are characterised by try and error decision making resulting in frequent reiterations and unnecessary repetitions. Studies have shown that clear goal setting and feedback can significantly improve the effectiveness of decision-making in predominantly human decision processes such as ramp-up. However, few measurement-driven decision aides have been reported which focus on ramp-up improvement and no systematic approach for ramp-up time reduction has yet been defined. In this paper, a framework for measuring the performance during ramp-up is proposed in order to support decision making by providing clear metrics based on the measurable and observable status of the technical system. This work proposes a systematic framework for data preparation, ramp-up formalisation, and performance measurement. A model for defining the ramp-up state of a system has been developed in order to formalise and capture its condition. Functionality, quality and performance based metrics have been identified to formalise a clear ramp-up index as a measurement to guide and support the human decision making. For the validation of the proposed framework, two ramp-up processes of an assembly station were emulated and their comparison was used to evaluate this work.

Suggested Citation

  • Doltsinis, Stefanos C. & Ratchev, Svetan & Lohse, Niels, 2013. "A framework for performance measurement during production ramp-up of assembly stations," European Journal of Operational Research, Elsevier, vol. 229(1), pages 85-94.
  • Handle: RePEc:eee:ejores:v:229:y:2013:i:1:p:85-94
    DOI: 10.1016/j.ejor.2013.02.051
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    References listed on IDEAS

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    1. Carrillo, Janice E. & Franza, Richard M., 2006. "Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time," European Journal of Operational Research, Elsevier, vol. 171(2), pages 536-556, June.
    2. Lohman, Clemens & Fortuin, Leonard & Wouters, Marc, 2004. "Designing a performance measurement system: A case study," European Journal of Operational Research, Elsevier, vol. 156(2), pages 267-286, July.
    3. Terwiesch, Christian & E. Bohn, Roger, 2001. "Learning and process improvement during production ramp-up," International Journal of Production Economics, Elsevier, vol. 70(1), pages 1-19, March.
    4. de Ron, Ad J., 1995. "Measure of manufacturing performance in advanced manufacturing systems," International Journal of Production Economics, Elsevier, vol. 41(1-3), pages 147-160, October.
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    Cited by:

    1. Weckenborg, Christian & Schumacher, Patrick & Thies, Christian & Spengler, Thomas S., 2024. "Flexibility in manufacturing system design: A review of recent approaches from Operations Research," European Journal of Operational Research, Elsevier, vol. 315(2), pages 413-441.
    2. Filla, Patrick & Klingebiel, Katja, 2014. "A Risk Management Approach for the Pre-Series Logistics in Production Ramp-Up," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Next Generation Supply Chains: Trends and Opportunities. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 18, volume 18, pages 407-422, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Ranasinghe, Thilini & Senanayake, Chanaka D. & Grosse, Eric H., 2024. "Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system," International Journal of Production Economics, Elsevier, vol. 267(C).

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