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Decision support models for production ramp-up: a systematic literature review

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  • Christoph H. Glock
  • Eric H. Grosse

Abstract

Production ramp-up is a critical step in the life cycle of a new product, and efficiently managing ramp-ups is a key to business success and market leadership. To support the planning of ramp-ups in practice, researchers have developed decision support models in the past that help to solve problems that arise during the ramp-up phase, such as lot sizing, the assignment of workers to workplaces or the determination of the capacity of the production equipment. Decision support models for production ramp-up typically consider the specific characteristics of this phase, such as uncertainty, growth in demand, worker learning or imperfect production processes. The aim of this paper is to provide a comprehensive overview of decision support models for production ramp-up and to identify areas where more research is needed. First, the paper develops a conceptual framework of production ramp-up by categorising typical planning problems and process characteristics of the ramp-up phase. Secondly, a systematic literature review with a focus on mathematical planning models for the ramp-up phase is conducted. The analysis shows that various decision support models that help to realise an efficient production ramp-up exist, but that there are still many opportunities for future research in this area.

Suggested Citation

  • Christoph H. Glock & Eric H. Grosse, 2015. "Decision support models for production ramp-up: a systematic literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 53(21), pages 6637-6651, November.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:21:p:6637-6651
    DOI: 10.1080/00207543.2015.1064185
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    Citations

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    Cited by:

    1. Raisa Akifyeva & Alisa Alieva, 2016. "The Influence of Ethnicity on Teacher Expectations and Teacher Perceptions of Student Warmth and Competence," HSE Working papers WP BRP 65/PSY/2016, National Research University Higher School of Economics.
    2. Patricia Heuser & Peter Letmathe & Matthias Schinner, 2022. "Workforce planning in production with flexible or budgeted employee training and volatile demand," Journal of Business Economics, Springer, vol. 92(7), pages 1093-1124, September.
    3. Annika Becker & Raik Stolletz & Thomas Stäblein, 2017. "Strategic ramp-up planning in automotive production networks," International Journal of Production Research, Taylor & Francis Journals, vol. 55(1), pages 59-78, January.
    4. Glock, Christoph H. & Grosse, Eric H. & Ries, Jörg M., 2017. "Reprint of “Decision support models for supplier development: Systematic literature review and research agenda”," International Journal of Production Economics, Elsevier, vol. 194(C), pages 246-260.
    5. Heuser, Patricia & Tauer, Björn, 2023. "Single-machine scheduling with product category-based learning and forgetting effects," Omega, Elsevier, vol. 115(C).
    6. Christian Weckenborg & Karsten Kieckhäfer & Thomas S. Spengler & Patricia Bernstein, 2020. "The Volkswagen Pre-Production Center Applies Operations Research to Optimize Capacity Scheduling," Interfaces, INFORMS, vol. 50(2), pages 119-136, March.
    7. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    8. Christoph H. Glock & Eric H. Grosse & Ralf M. Elbert & Torsten Franzke, 2017. "Maverick picking: the impact of modifications in work schedules on manual order picking processes," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6344-6360, November.
    9. 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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