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Modelling ramp-up curves to reflect learning: improving capacity planning in secondary pharmaceutical production

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  • Klaus R.N. Hansen
  • Martin Grunow

Abstract

The experience gained during production ramp-up leads to an increase of the effective production capacity over time. However, full utilisation of production capacity is not always possible during ramp-up. In such cases, the experience gained and hence the available effective capacity are overestimated. We develop a new method, which captures ramp-up as a function of the cumulative production volume to better reflect the experience gained while producing the new product. The use of the more accurate and computationally effective approach is demonstrated for the case of secondary pharmaceutical production. Due to its regulatory framework, this industry cannot fully exploit available capacities during ramp-up. We develop a capacity planning model for a new pharmaceutical drug, which determines the number and location of new production lines and the build-up of inventory such that product availability at market launch is ensured. Our MILP model is applied to a real industry case study using three empirically observed ramp-up curves to demonstrate its value as decision support tool. We demonstrate the superiority of our volume-dependent method over the traditional time-dependent ramp-up functions and derive managerial insights into the selection of ramp-up function and the value of shortening ramp-ups.

Suggested Citation

  • Klaus R.N. Hansen & Martin Grunow, 2015. "Modelling ramp-up curves to reflect learning: improving capacity planning in secondary pharmaceutical production," International Journal of Production Research, Taylor & Francis Journals, vol. 53(18), pages 5399-5417, September.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:18:p:5399-5417
    DOI: 10.1080/00207543.2014.998788
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    Cited by:

    1. 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.
    2. Ana Carolina Silva & Catarina Moreira Marques & Jorge Pinho de Sousa, 2023. "A Simulation Approach for the Design of More Sustainable and Resilient Supply Chains in the Pharmaceutical Industry," Sustainability, MDPI, vol. 15(9), pages 1-23, April.
    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. Hansen, Klaus Reinholdt Nyhuus & Grunow, Martin, 2015. "Planning operations before market launch for balancing time-to-market and risks in pharmaceutical supply chains," International Journal of Production Economics, Elsevier, vol. 161(C), pages 129-139.
    5. Heuser, Patricia & Tauer, Björn, 2023. "Single-machine scheduling with product category-based learning and forgetting effects," Omega, Elsevier, vol. 115(C).

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