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Investing in Excess Capacity: Combining Real Options and Fuzzy Approaches in a Co-Opetitive Network

Investing in Excess Capacity: Combining Real Options and Fuzzy Approaches in a Co-Opetitive Network

Pierluigi Argoneto, Paolo Renna
Copyright: © 2011 |Pages: 22
ISBN13: 9781615206339|ISBN10: 1615206337|EISBN13: 9781615206346
DOI: 10.4018/978-1-61520-633-9.ch003
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MLA

Argoneto, Pierluigi, and Paolo Renna. "Investing in Excess Capacity: Combining Real Options and Fuzzy Approaches in a Co-Opetitive Network." Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods, edited by Ioannis Minis, et al., IGI Global, 2011, pp. 51-72. https://doi.org/10.4018/978-1-61520-633-9.ch003

APA

Argoneto, P. & Renna, P. (2011). Investing in Excess Capacity: Combining Real Options and Fuzzy Approaches in a Co-Opetitive Network. In I. Minis, V. Zeimpekis, G. Dounias, & N. Ampazis (Eds.), Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods (pp. 51-72). IGI Global. https://doi.org/10.4018/978-1-61520-633-9.ch003

Chicago

Argoneto, Pierluigi, and Paolo Renna. "Investing in Excess Capacity: Combining Real Options and Fuzzy Approaches in a Co-Opetitive Network." In Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods, edited by Ioannis Minis, et al., 51-72. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-633-9.ch003

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Abstract

The cooperation among firms allows them to focus on their core products, improving efficiency and competiveness. The emerging paradigm of co-opetition, considering at the same time cooperative and competitive aspects, seems to be the most promising approach both in traditional and electronic network. This chapter investigates the excess capacity issue for independent plants operating in a co-opetitive network. Two models have been proposed: the first without any information exchange, based on classical real options approach, and the second characterized by a certain degree of information sharing: in here the real options methodology is combined with a fuzzy engine. A simulation environment based on Multi Agent Architecture has been developed in order to test the proposed models. The simulation results show that the innovative combined approach drastically reduces the investment, maintaining a high level of profit.

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