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An approach to the valuation and decision of ERP investment projects based on real options

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  • F. Wu
  • H. Li
  • L. Chu
  • D. Sculli
  • K. Gao

Abstract

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach. Copyright Springer Science+Business Media, LLC 2009

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  • F. Wu & H. Li & L. Chu & D. Sculli & K. Gao, 2009. "An approach to the valuation and decision of ERP investment projects based on real options," Annals of Operations Research, Springer, vol. 168(1), pages 181-203, April.
    • Handle: RePEc:spr:annopr:v:168:y:2009:i:1:p:181-203:10.1007/s10479-008-0365-7
    DOI: 10.1007/s10479-008-0365-7
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    Cited by:

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    2. D. Laurie Hughes & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "Elucidation of IS project success factors: an interpretive structural modelling approach," Annals of Operations Research, Springer, vol. 285(1), pages 35-66, February.
    3. Zugang Liu & Anna Nagurney, 2013. "Supply chain networks with global outsourcing and quick-response production under demand and cost uncertainty," Annals of Operations Research, Springer, vol. 208(1), pages 251-289, September.
    4. Bettina Freitag & Lukas Häfner & Verena Pfeuffer & Jochen Übelhör, 2020. "Evaluating investments in flexible on-demand production capacity: a real options approach," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 133-161, April.
    5. Christian Ullrich, 2013. "Valuation of IT Investments Using Real Options Theory," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(5), pages 331-341, October.
    6. Robert Keller & Lukas Häfner & Thomas Sachs & Gilbert Fridgen, 2020. "Scheduling Flexible Demand in Cloud Computing Spot Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(1), pages 25-39, February.
    7. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    8. Marcel Philipp Müller & Sebastian Stöckl & Steffen Zimmermann & Bernd Heinrich, 2016. "Decision Support for IT Investment Projects," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 58(6), pages 381-396, December.

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