Evaluating complex decision and predictive environments: the case of green supply chain flexibility
Abstract
Supply chain flexibility is an important operations strategy dimension for organizations to achieve and maintain competitive advantage. With rising greener customer expectations and increasingly stringent environmental regulations, green supply chains are now viewed as another competitive weapon. Green supply chains are characterized by higher complexity and turbulence. Green supply chain flexibility can aid organizations function in this complex and uncertain environment, yet investigation into this area is very limited. This paper aims contribute to this field by investigating green supply chain flexibility achievement through information systems. This paper introduces a green supply chain flexibility matrix framework. Given the large data needs, as described in the matrix, a novel probability evaluation methodology that can help predict rankings of projects and programs is introduced. The methodology extends a TOPSIS based three-parameter interval grey number (TpGN) approach by incorporating neighborhood rough set theory (RST) to evaluate IS programs’ green flexibility support capability. The results of this methodology are more objective and effective for two reasons. (1) The results are predictive rankings based on probability degree instead of the fixed deterministic ranks. (2) Neighborhood rough set theory used in this study can limit loss of information when compared to rough set theory, yet still simplify extensive data sets. This paper also identifies study limitations and future research directions for green supply chain flexibility.
Keyword : flexibility, green supply chain, information systems, probability evaluation methodology, rough set, TOPSIS, grey number
How to Cite
Bai, C., & Sarkis, J. (2018). Evaluating complex decision and predictive environments: the case of green supply chain flexibility. Technological and Economic Development of Economy, 24(4), 1630-1658. https://doi.org/10.3846/20294913.2018.1483977
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chuu, S. J. (2011). Interactive group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a supply chain. European Journal of Operational Research, 213(1), 279-289. https://doi.org/10.1016/j.ejor.2011.03.022
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Garavelli, A. C. (2003). Flexibility configurations for the supply chain management. International Journal of Production Economics, 85(2), 141-153. https://doi.org/10.1016/S0925-5273(03)00106-3
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Golden, W., & Powell, P. (1999). Exploring inter-organisational systems and flexibility in Ireland: a case of two value chains. International Journal of Agile Management Systems, 1(3), 169-176. https://doi.org/10.1108/14654659910296544
Gong, M., Tan, K. H., Pawar, K., Wong, W. P., & Tseng, M. L. (2015). Information communication technology and sustainable food supply chain: a resource-based analysis. International Journal of Business Performance and Supply Chain Modelling, 7(3), 233-255. https://doi.org/10.1504/IJBPSCM.2015.071611
Gosain, S., Malhotra, A., & El Sawy, O. A. (2004). Coordinating for flexibility in e-business supply chains. Journal of Management Information Systems, 21(3), 7-45. https://doi.org/10.1080/07421222.2004.11045816
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Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An International Journal, 12(4), 330-353. https://doi.org/10.1108/14635770510609015
Hu, Q., Yu, D., & Xie, Z. (2008b). Neighborhood classifiers. Expert Systems with Applications, 34(2), 866-876. https://doi.org/10.1016/j.eswa.2006.10.043
Hu, Q., Yu, D., Liu, J., & Wu, C. (2008a). Neighborhood rough set based heterogeneous feature subset selection. Information Sciences, 178(18), 3577-3594. https://doi.org/10.1016/j.ins.2008.05.024
Jin, Y., Vonderembse, M., Ragu-Nathan, T. S., & Smith, J. T. (2014). Exploring relationships among IT-enabled sharing capability, supply chain flexibility, and competitive performance. International Journal of Production Economics, 153, 24-34. https://doi.org/10.1016/j.ijpe.2014.03.016
Karsak, E. E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research, 40(13), 3167-3181. https://doi.org/10.1080/00207540210146062
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Kumar, V., Fantazy, K. A., Kumar, U., & Boyle, T. A. (2006). Implementation and management framework for supply chain flexibility. Journal of Enterprise Information Management, 19(3), 303-319. https://doi.org/10.1108/17410390610658487
Liang, J., Shi, Z., Li, D., & Wierman, M. J. (2006). Information entropy, rough entropy and knowledge granulation in incomplete information systems. International Journal of General Systems, 35(6), 641-654. https://doi.org/10.1080/03081070600687668
Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision Support Systems, 54(3), 1452-1462. https://doi.org/10.1016/j.dss.2012.12.016
Luo, D., & Wang, X. (2012). The multi-attribute grey target decision method for attribute value within three-parameter interval grey number. Applied Mathematical Modelling, 36(5), 1957-1963. https://doi.org/10.1016/j.apm.2011.07.074
Luo, D., Wang, X., & Song, B. (2013). Multi-attribute decision-making methods with three-parameter interval grey number. Grey Systems: Theory and Application, 3(3), 305-315. https://doi.org/10.1108/GS-08-2013-0021
Malhotra, M. K., & Mackelprang, A. W. (2012). Are internal manufacturing and external supply chain flexibilities complementary capabilities?. Journal of Operations Management, 30(3), 180-200. https://doi.org/10.1016/j.jom.2012.01.004
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