Fostering supply chain agility by prominent enablers’ identification and developing conceptual modeling based on the ISM-MICMAC approach

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-11-01 DOI:10.1108/jm2-01-2023-0002
Yesim Can Saglam
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Abstract

Purpose Today’s marketplace has witnessed intense competitive pressures and high levels of uncertainty and disruption. Therefore, supply chains require agility to obtain a sustainable competitive advantage and cope with uncertainties as well as disruptions. Although a wide range of studies exists on supply chain agility (SCA) from the perspective of antecedents or consequences, there is little research on the investigation of enablers of SCA and their relations among them. Furthermore, the literature has investigated proactive and reactive enablers for enhancing SCA, but most studies have not sufficiently framed their analysis of both aspects synchronically. This paper aims to find out the interrelationships among the proactive and reactive enablers for enhancing SCA. Design/methodology/approach An extensive literature review has been conducted to identify SCA enablers and a Delphi study has been performed to elucidate SCA enablers in the manufacturing industry in Turkey. Interpretive structural modeling (ISM) has been used to identify the contextual relationship among the SCA enablers, and the model has been validated based on Matriced Impact Croises Multiplication Appliquee a un Classement (MICMAC) analysis. Findings On theoretical and practical levels, the proposed ISM model in this study can help organizations analyze and interpret interrelationships among enablers of SCA. For managers, it can provide better insights and understanding of the facilitators of SCA to enhance the effectiveness of the supply chain and cope with uncertainties and turbulence. According to results, enhancing “supply and demand side competency”, “delivery speed” and “strategic sourcing” are the most significant enablers of SCA. Originality/value The study extends the existing literature related to the enablers of SCA by modeling the proactive and reactive enablers of SCA based on the Al Humdan et al. (2020) classification. Arranging the enablers of SCA in a hierarchy and classifying the enablers into different levels with the help of the ISM-MICMAC approach is an exclusive effort to achieve successful management of the supply chain.
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通过突出的推动者识别和基于ISM-MICMAC方法开发概念建模来促进供应链敏捷性
今天的市场见证了激烈的竞争压力和高度的不确定性和破坏。因此,供应链需要敏捷性,以获得可持续的竞争优势,并应对不确定性和中断。尽管从前因或后果的角度对供应链敏捷性(SCA)进行了广泛的研究,但对SCA的促成因素及其相互关系的研究却很少。此外,文献已经研究了增强SCA的主动和被动使能因素,但大多数研究没有充分地同时分析这两个方面。本文旨在找出增强SCA的主动促成因素和被动促成因素之间的相互关系。设计/方法/方法进行了广泛的文献综述,以确定SCA促进因素,并进行了德尔福研究,以阐明土耳其制造业中的SCA促进因素。解释结构建模(ISM)已被用于识别SCA驱动程序之间的上下文关系,并且该模型已基于矩阵影响循环乘法应用类(MICMAC)分析进行了验证。在理论和实践层面,本研究提出的ISM模型可以帮助组织分析和解释SCA促成因素之间的相互关系。对于管理者来说,它可以更好地洞察和理解SCA的促进因素,以提高供应链的有效性,应对不确定性和动荡。结果表明,提高“供需侧能力”、“交付速度”和“战略采购”是SCA最重要的推动因素。原创性/价值本研究基于Al Humdan等人(2020)的分类,通过对SCA的主动和被动促成因素进行建模,扩展了与SCA促成因素相关的现有文献。在ISM-MICMAC方法的帮助下,在层次结构中安排SCA的促成因素并将促成因素分类到不同的级别,这是实现供应链成功管理的唯一努力。
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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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
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.
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