基于模糊混合方法的印度可持续供应链风险管理分析

Venkateswarlu Nalluri, Ching-Torng Lin, Long-Sheng Chen
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引用次数: 0

摘要

由于风险因素的复杂性,在可持续供应链管理中可能会出现不同来源的风险因素。电信服务公司无法以有限的资源同时实施多个改进实践来克服风险因素。行业应评估风险因素之间的关系,并探索改善措施的决定因素。本研究旨在分析和确定关键风险因素(CRFs),以使用混合方法加强印度电信行业的可持续供应链管理实践。采用模糊解释结构模型(FISM)和模糊决策试验与评价实验室(FDEMATEL)方法分析了各指标间的关系。本研究的共同结果是,政府政策(法律法规)风险(R13)对电信业务可持续供应链的风险影响最大。此外,非法活动(如2g诈骗)(R3)、环境污染(R18)等风险因素也间接受到高驱动功率crf的影响。根据研究结果,政府可以建立公正、公平、公开的法律和确定性,以防止电信供应链中的风险;服务提供商可以监控快速发展的技术。本研究的贡献在于使用混合方法建立了一个层次结构模型,以有效地理解CRFs关系并探索决定性的风险因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk Management Analysis of the Sustainable Supply Chain Using a Fuzzy Hybrid Approach in India
Different sources of risk factors can happen in sustainable supply chain management due to their complex nature. The telecommunication service firm cannot implement multiple improvement practices altogether to overcome the risk factors with limited resources. The industries should evaluate the relationship between risk factors and explore the determinants of improvement measures. The present study aims to analyses and identifies critical risk factors (CRFs) for enhancing sustainable supply chain management practices in the Indian telecommunication industry using the hybrid approach. The relationship among these CRFs has been analyzed by using fuzzy interpretive structural modelling (FISM) and Fuzzy decision-making trial and evaluation laboratory (FDEMATEL) methods to explore the relationships between them. The common result of the present study is that the risks government policies (laws and regulations) (R13) are the most affecting CRFs of the sustainable supply chain in telecom service. In addition, the risk factors illegal activities (e.g.2G scams) (R3), environmental pollution(R18) are indirectly affected by high driving power CRFs. Based on the results, the government could build justice, fairness, open laws, and certainties to prevent risk in the telecoms supply chain; service providers could monitor the rapidly evolving technologies. The contribution of this study is using a hybrid approach to establish a hierarchical structural model for an effective understanding of CRFs relationships and to explore decisive risk factors.
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