A holistic framework for assessing risks in sustainable supply chain innovation in the garment, textile, and leather industry
This study develops a holistic framework for assessing risks in sustainable supply chain innovation (SSCI) within Jordan's garment, textile, and leather (GTL) industry, addressing critical challenges posed by demand volatility, customer concentration, and price competition. Using multi-criteria...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
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| Series: | Sustainable Technology and Entrepreneurship |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773032825000069 |
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| Summary: | This study develops a holistic framework for assessing risks in sustainable supply chain innovation (SSCI) within Jordan's garment, textile, and leather (GTL) industry, addressing critical challenges posed by demand volatility, customer concentration, and price competition. Using multi-criteria decision-making tools such as AHP and sensitivity analysis, the research prioritizes risks into high, moderate, and low sensitivity categories. Highly sensitive risks, including demand-related challenges, require dynamic and adaptive strategies, while moderately sensitive risks, like supplier mismatches, benefit from enhanced visibility and collaboration. Low-sensitivity risks, such as cultural resistance and energy consumption, are better managed through long-term sustainability initiatives.The study's methodology involved input from six experts and systematic analyses to ensure robust prioritization of SSCI risks. Key findings highlight the necessity of tailoring risk mitigation approaches to specific risk sensitivities, offering actionable insights for supply chain managers. The framework is distinctive in integrating sustainability into risk prioritization, providing a structured approach adaptable across similar industries.This research contributes to SSCI literature by advancing decision theory for risk evaluation in evolving scenarios. It underscores the importance of dynamic strategies for high-sensitivity risks and phased approaches for addressing lower-priority risks. Future research could explore the applicability of this framework in global supply chain networks or extend its use through advanced technologies like artificial intelligence for enhanced risk forecasting and management. |
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| ISSN: | 2773-0328 |