制造企业供应链脆弱性影响因素的综合结构模型

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-04-16 DOI:10.1108/jm2-10-2023-0227

Satyendra Kr Sharma, Rajkumar Sharma, Anil Jindal

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引用次数: 0

摘要

目的供应链脆弱性（SCV）分析对全球制造商至关重要，因为它为在不确定环境中建立弹性供应链提供了途径。本研究旨在确定印度制造业供应链脆弱性的驱动因素。设计/方法/途径通过文献综述和专家访谈确定了 16 个驱动因素。研究结果发现，风险并非董事会议程。绩效衡量标准与激励机制不一致以及缺乏风险仪表板是造成 SCV 的原因。供应链安全、集中式生产和分销以及对供应链缺乏信任是驱动因素。该结构模型为供应链可持续发展提供了系统性视角，有助于降低脆弱性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An integrated structural model for supply chain vulnerability influencing factors in manufacturing enterprises

Purpose

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Design/methodology/approach

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

Findings

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

Originality/value

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.