Evaluating complex decision and predictive environments: the case of green supply chain flexibility
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. Gr...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2018-08-01
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| Series: | Technological and Economic Development of Economy |
| Subjects: | |
| Online Access: | http://journals.vgtu.lt/index.php/TEDE/article/view/4528 |
| _version_ | 1818913951842304000 |
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| author | Chunguang Bai Joseph Sarkis |
| author_facet | Chunguang Bai Joseph Sarkis |
| author_sort | Chunguang Bai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T23:38:39Z |
| format | Article |
| id | doaj.art-8aa0475b134d4d4483a7673247684ac4 |
| institution | Directory Open Access Journal |
| issn | 2029-4913 2029-4921 |
| language | English |
| last_indexed | 2024-12-19T23:38:39Z |
| publishDate | 2018-08-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Technological and Economic Development of Economy |
| spelling | doaj.art-8aa0475b134d4d4483a7673247684ac42022-12-21T20:01:31ZengVilnius Gediminas Technical UniversityTechnological and Economic Development of Economy2029-49132029-49212018-08-0124410.3846/20294913.2018.1483977Evaluating complex decision and predictive environments: the case of green supply chain flexibilityChunguang Bai0Joseph Sarkis1School of Management and Economics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, P.R. ChinaSchool of Business, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280, USASupply 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.http://journals.vgtu.lt/index.php/TEDE/article/view/4528flexibilitygreen supply chaininformation systemsprobability evaluation methodologyrough setTOPSIS |
| spellingShingle | Chunguang Bai Joseph Sarkis Evaluating complex decision and predictive environments: the case of green supply chain flexibility Technological and Economic Development of Economy flexibility green supply chain information systems probability evaluation methodology rough set TOPSIS |
| title | Evaluating complex decision and predictive environments: the case of green supply chain flexibility |
| title_full | Evaluating complex decision and predictive environments: the case of green supply chain flexibility |
| title_fullStr | Evaluating complex decision and predictive environments: the case of green supply chain flexibility |
| title_full_unstemmed | Evaluating complex decision and predictive environments: the case of green supply chain flexibility |
| title_short | Evaluating complex decision and predictive environments: the case of green supply chain flexibility |
| title_sort | evaluating complex decision and predictive environments the case of green supply chain flexibility |
| topic | flexibility green supply chain information systems probability evaluation methodology rough set TOPSIS |
| url | http://journals.vgtu.lt/index.php/TEDE/article/view/4528 |
| work_keys_str_mv | AT chunguangbai evaluatingcomplexdecisionandpredictiveenvironmentsthecaseofgreensupplychainflexibility AT josephsarkis evaluatingcomplexdecisionandpredictiveenvironmentsthecaseofgreensupplychainflexibility |