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Managing business dynamics with adaptive supply chain portfolios

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  • Seifert, Ralf W.
  • Langenberg, Kerstin U.

Abstract

Practitioners and scholars readily agree that firms need to frequently adapt their supply chain portfolios to respond to today's rapidly evolving business dynamics. Adapting a well established supply chain portfolio, however, may involve high costs and expose a firm to unforeseeable risks. In this paper we address this issue. We differentiate business dynamics into product portfolio dynamics and global business dynamics and classify supply chain adaptation into high, medium and low. Building on these classifications we develop mathematical models to analyze how much supply chain adaptation a firm actually requires to respond to the business dynamics it faces. Our results indicate that supply chain adaptation may indeed be crucial for a firm to retain its competitiveness. The need for it, however, differs widely across firms. For example, a firm faced with product portfolio commoditization may be required to adapt its entire manufacturing footprint, while a firm with a high product turnover rate may not need to adapt its supply chain portfolio at all. Furthermore, the need for supply chain adaptation is not only determined by the business context a firm operates in but can be manipulated by the firm's product portfolio decisions. Finally, we also argue that, to exhaust the attainable benefits, a firm should carefully align its supply chain portfolio with the employed supply chain adaptation strategy.

Suggested Citation

  • Seifert, Ralf W. & Langenberg, Kerstin U., 2011. "Managing business dynamics with adaptive supply chain portfolios," European Journal of Operational Research, Elsevier, vol. 215(3), pages 551-562, December.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:3:p:551-562
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    3. Lorentz, Harri & Kittipanya-ngam, Pichawadee & Singh Srai, Jagjit, 2013. "Emerging market characteristics and supply network adjustments in internationalising food supply chains," International Journal of Production Economics, Elsevier, vol. 145(1), pages 220-232.
    4. Ourania Markaki & Dimitris Askounis, 2021. "Assessing the operational and economic efficiency benefits of dynamic manufacturing networks through fuzzy cognitive maps: a case study," Operational Research, Springer, vol. 21(2), pages 925-950, June.
    5. Martí, Joana M. Comas & Tancrez, Jean-Sébastien & Seifert, Ralf W., 2015. "Carbon footprint and responsiveness trade-offs in supply chain network design," International Journal of Production Economics, Elsevier, vol. 166(C), pages 129-142.
    6. Wu, Kan & Yuan, Xue-Ming, 2016. "Optimal production-inventory policy for an integrated multi-stage supply chain with time-varying demandAuthor-Name: Zhao, Shi Tao," European Journal of Operational Research, Elsevier, vol. 255(2), pages 364-379.
    7. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.

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