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.
Highlights
Chains and supply chain management have added flexibility as a main operations strategy dimension due to increasingly uncertain, complex, and dynamic business environments (Wu, Tseng, Chiu, & Lim, 2016; Blome, Schoenherr, & Rexhausen, 2013; Moon, Yi, & Ngai, 2012)
Decision tools are needed to help deal with big and complex data sets (Bai, Sarkis, Wei, & Koh, 2012). For this reason we introduce a novel multiple criteria decision making (MCDM) methodology for identification and selection of green information systems (IS) to improve green supply chain flexibility, an to provide a decision making model for converting complex flexibility data into simple and effective metrics for decision making (Bai & Sarkis, 2013a)
This paper offers a framework of green supply chain flexibility “close the loop”, making reverse logistics and its flexibility an important part of green supply chains
Summary
Chains and supply chain management have added flexibility as a main operations strategy dimension due to increasingly uncertain, complex, and dynamic business environments (Wu, Tseng, Chiu, & Lim, 2016; Blome, Schoenherr, & Rexhausen, 2013; Moon, Yi, & Ngai, 2012). Using the broader definition of supply chain flexibility (Kumar, V., Fantazy, Kumar, U., & Boyle, 2006), green supply chain flexibility can be defined as the ability of supply chains to rapidly address customers’ dynamic green requirements and increasingly stringent environmental regulations without incurring significant performance degradation. These green supply chain flexibility concerns can be relatively more difficult to manage due to immaturity of most organizations in managing green supply chain functions (Bai & Sarkis 2017, 2013a). A contribution, of this paper is to further study green supply chain flexibility as a response to additional complexities when greening supply chains
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