Abstract
Capacity planning is a crucial part of global manufacturing strategies in the automotive industry, especially in the presence of volatile markets with high demand uncertainty. Capacity adjustments in machining intensive areas, e.g. body shop, paint shop, or aggregate machining face lead times exceeding a year, making an elaborated decision support indispensable. In this regard, two-stage stochastic programming is a frequently used framework to support capacity and flexibility decisions under uncertainty. However, it does not anticipate future capacity adjustment opportunities in response to market demand developments. Motivated by empirical findings from the automotive industry, we develop a multi-stage stochastic dynamic programming approach where the evolution of demand is represented by a Markov demand model. An efficient multi-stage solution algorithm is proposed and the benefits compared to a rolling horizon application of a two-stage approach are illustrated for different generic manufacturing networks. Especially network structures with limited flexibility might significantly benefit from applying a multi-stage framework.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmed S, Garcia R (2002) Dynamic capacity acquisition and assignment under uncertainty. Ann Oper Res 124(1–4):267–283
Ahmed S, King A, Parija G (2003) A multi-stage stochastic integer programming approach for capacity expansion under uncertainty. J Glob Optim 26(1):3–24
Alonso-Ayuso A, Escuerdo L, Garn A, Ortuño M, Prez G (2003) An approach for strategic supply chain planning under uncertainty based on stochastic 0–1 programming. J Glob Optim 26(1):97–124
Bertrand J (2003) Supply chain design: flexibility considerations. In: de Kok A, Graves S (eds) Supply chain management: design, coordination and operation. Handbooks in Operations Research and Management Science, vol. 11. Elsevier, Amsterdam, pp 133–198
Bihlmaier R, Koberstein A, Obst R (2009) Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty. OR Spectrum 31(2):311–336
Chandra C, Everson M, Grabis J (2005) Evaluation of enterprise-level benefits of manufacturing flexibility. Omega 33(1):17–31
Chen ZL, Li S, Tirupati D (2002) A scenario-based stochastic programming approach for technology and capacity planning. Comput Oper Res 29(7):781–806
Eppen GD, Martin RK, Schrage L (1989) A scenario approach to capacity planning. Oper Res 37(4):517–527
Fine CH, Freund RM (1990) Optimal investment in product-flexible manufacturing capacity. Manage Sci 36(4):449–466
Fleischmann B, Ferber S, Henrich P (2006) Strategic planning of BMW’s global production network. Interfaces 36(3):194–208
Francas D, Kremer M, Minner S, Friese M (2009) Strategic process flexibility under lifecycle demand. Int J Product Econ 121(2):427–440
Harrison JM, Van Mieghem JA (1999) Multi-resource investment strategies: operational hedging under demand uncertainty. Eur J Oper Res 113(1):17–29
Huang K, Ahmed S (2009) The value of multi-stage stochastic programming in capacity planning under uncertainty. Oper Res 57(4):893–904
Jordan W, Graves S (1995) Principles on the benefits of manufacturing process flexibility. Manage Sci 41(4):577–594
Julka N, Baines T, Tjahjono B, Lendermann P, Vitanov V (2007) A review of multi-factor capacity expansion models for manufacturing plants: searching for a holistic decision aid. Int J Product Econ 106(2):607–621
Kall P, Wallace SW (1994) Stochastic programming. Wiley, NewYork
Kauder S, Meyr H (2009) Strategic network planning for an international automotive manufacturer. OR Spectrum 31(3):507–532
Luss H (1982) Operations research and capacity expansion problems: a survey. Oper Res 30(5):907–947
Mak HY, Shen ZJM (2009) Stochastic programming approach to process flexibility design. Flexible Services Manuf J 21(3-4):75–91
Massmann M (2006) Kapazitierte stochastisch-dynamische Facility-Location-Planung-Modellierung und Lösung eines strategischen Standortentscheidungsproblems bei unsicherer Nachfrage. Deutscher Universitäts-Verlag, Wiesbaden
Pflug G, Römisch W (2007) Modeling, measuring and managing risk. World Scientific, Singapore
Powell WB (2007) Approximate dynamic programming. Wiley, Hoboken
Riis M, Andersen K (2004) Multiperiod capacity expansion of a telecommunications connection with uncertain demand. Comput Oper Res 31(9):1427–1436
Santoso T, Ahmed S, Goetschalckx M, Shapiro A (2005) A stochastic programming approach for supply chain network design under uncertainty. Eur J Oper Res 167(1):96–115
Storey J (2008) The world’s car manufacturers-a strategic review of finance and operations. Automotive World Ltd
Van Mieghem JA (1998) Investment strategies for flexible resources. Manage Sci 44(8):1071–1078
Van Mieghem JA (2003) Commissioned paper: capacity management, investment, and hedging: review and recent developments. Manuf Service Oper Manage 5(4):269–302
Yaffee RA, McGee M (2000) Introduction to time series analysis and forecasting: with applications of SAS and SPSS. Academic Press, San Diego
Acknowledgements
The authors gratefully acknowledge the comments of two anonymous referees that helped to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Stephan, H.A., Gschwind, T. & Minner, S. Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand. Flex Serv Manuf J 22, 143–162 (2010). https://doi.org/10.1007/s10696-010-9071-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10696-010-9071-2