A multi-criteria decision support model for evaluating the performance of partnerships |
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| Affiliation: | 1. Computer and Network Center, National Cheng Kung University, Tainan 701, Taiwan;2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan;1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;2. Shanghai Jiao Tong University, Shanghai, China;3. College of Information and Computer Engineering, Northeast Forestry University, Harbin, China;4. Harbin Vicog Intelligent Systems Co., Ltd, Harbin, China;1. Institute of Humanities, Arts and Sciences, Federal University of Southern Bahia, BR-367, Km 10, CEP: 45810-000, Porto Seguro, Bahia, Brazil;2. Department of Computer Science, Institute of Mathematics and Computer Science, University of São Paulo, Av. Trabalhador São-carlense, 400, Caixa Postal: 668, CEP: 13560-970, São Carlos, São Paulo, Brazil;3. Department of Computation and Mathematics, School of Philosophy, Science and Literature in Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, CEP: 14090-901, Ribeirão Preto, São Paulo, Brazil;1. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin 34185-1416, Iran;2. Singapore Institute of Technology, Singapore 13868, Singapore;3. School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;4. Singapore Institute of Manufacturing Technology, Singapore 638075, Singapore |
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| Abstract: | Partnership is one of the strategies that could help companies increase their competiveness in a global market. Previous studies reported that a high percentage of partnerships fail to achieve their drivers of entering into partnership. The lack of a comprehensive partnership evaluation has been identified as one of the main reasons for partnership failure. In this paper, a multi-criteria decision support model is developed to evaluate the performance of an ongoing partnership in different periods based on the measures associated with the drivers for entering into the partnership. Interpretive Structural Modeling (ISM), Analytical Network Process (ANP) and Fuzzy Logic (FL) are used in order to address the interdependency, the importance of, and the uncertainty in performance measures, respectively. The outputs of the model are the importance of each performance measure and a single number for the overall partnership performance in each period, named as Partnership Performance Index (PPI) here. PPI is different from either mere financial or operational performance measures. PPI is a multi-dimensional measure which includes multiple performance measures associated with the partnership drivers and accounts for their importance and interdependencies. The model is applied to a partnership between a logging company and a sawmill in British Columbia, Canada. PPI is used to evaluate this partnership in three different periods. PPI values are compared to conventional measures for partnership evaluation and the managers confirmed that PPI values better represent the performance of their partnership. The sensitivity of the PPIs is investigated based on the changes in the importance as well as the value of the measures. The rankings from the model are compared to the ones estimated by the managers, and the results showed that the rankings are compatible. This model contributes to the literature by developing an index for partnership performance which captures partnership drivers and performance measures as well as their importance and interdependencies. |
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