The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
Barriers and overcoming strategies to supply chain sustainability innovation
Open accessAbstract
Abstract This study identifies a list of barriers that hinders adoption, implementation and upscaling of sustainable supply chain innovation in the manufacturing industry. It further proposes overcoming strategies that seek to aid management decision to dealing with these barriers systematically. A multi-criteria decision analysis method, the Best-Worst Method (BWM), is adopted to aid in the evaluation and prioritization of the barriers and their overcoming strategies within the Indian manufacturing industry, an emerging economy. The results depict that, “lack of technical expertise and training”, “lack of R&D and innovation capabilities”, “popularity of traditional technology”, “high initial investment in latest technology” and “fear of extra workload and loss of flexibility” are the top five barriers that confronts the Indian manufacturing companies in their quest for adopting and implementing sustainable supply chain innovation practices. In addition, the overcoming strategic pathway for dealing with these barriers are provided. The findings provide managerial and policy insights for guiding the formation of strategic operations framework and resource allocation if these Indian manufacturing firms seeks to build sustainability into their supply chain innovations.
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Cites
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Cited by
Ali, S. S., Kaur, R., & Khan, S. (2023) · Annals of Operations Research · doi:10.1007/s10479-021-04454-w · Cited by 154
Low-carbon warehousing practices and challenges: insights from emerging country
Ibrahim, A., Fernando, Y., Tseng, M.-L., & Lim, M. K. (2024) · International Journal of Logistics Research and Applications · doi:10.1080/13675567.2022.2145276 · Cited by 23
Metadata
- Authors
- Gupta, H., Kusi-Sarpong, S., & Rezaei, J.
- Venue
- Resources, Conservation and Recycling
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 161
- Pages
- 104819
- ISSN
- 0921-3449 (ISSN-L)
- OpenAlex
- W3034625217
- Cited by
- 304
- References
- 102
- FWCI
- 40.79
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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