Exploring the significant factors of reconfigurable manufacturing system adoption in manufacturing industries

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-07-21 DOI:10.1108/jm2-12-2022-0286

Rajesh B. Pansare, M. Nagare, V. Narwane

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Abstract

Purpose A reconfigurable manufacturing system (RMS) can provide manufacturing flexibility, meet changing market demands and deliver high performance, among other benefits. However, adoption and performance improvement are critical activities in it. The current study aims to identify the important factors influencing RMS adoption and validate a conceptual model as well as develop a structural model for the identified factors. Design/methodology/approach An extensive review of RMS articles was conducted to identify the eight factors and 47 sub-factors that are relevant to RMS adoption and performance improvement. For these factors, a conceptual framework was developed as well as research hypotheses were framed. A questionnaire was developed, and 117 responses from national and international domain experts were collected. To validate the developed framework and test the research hypothesis, structural equation modeling was used, with software tools SPSS and AMOS. Findings The findings support six hypotheses: “advanced technologies,” “quality and safety practice,” “strategy and policy practice,” “organizational practices,” “process management practices,” and “soft computing practices.” All of the supported hypotheses have a positive impact on RMS adoption. However, the two more positive hypotheses, namely, “sustainability practices” and “human resource policies,” were not supported in the analysis, highlighting the need for greater awareness of them in the manufacturing community. Research limitations/implications The current study is limited to the 47 identified factors; however, these factors can be further explored and more sub-factors identified, which are not taken into account in this study. Practical implications Managers and practitioners can use the current work’s findings to develop effective RMS implementation strategies. The results can also be used to improve the manufacturing system’s performance and identify the source of poor performance. Originality/value This paper identifies critical RMS adoption factors and demonstrates an effective structural-based modeling method. This can be used in a variety of fields to assist policymakers and practitioners in selecting and implementing the best manufacturing system. Graphical abstract

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制造业采用可重构制造系统的重要因素探讨

目的可重构制造系统（RMS）可以提供制造灵活性，满足不断变化的市场需求，并提供高性能等优点。然而，采用和绩效改进是其中的关键活动。当前的研究旨在确定影响RMS采用的重要因素，验证概念模型，并为已确定的因素开发结构模型。设计/方法/方法对RMS文章进行了广泛的审查，以确定与RMS采用和性能改进相关的8个因素和47个子因素。针对这些因素，制定了一个概念框架，并提出了研究假设。编制了一份调查表，收集了来自国内和国际领域专家的117份答复。为了验证所开发的框架并检验研究假设，使用SPSS和AMOS软件工具进行了结构方程建模。发现支持六个假设：“先进技术”、“质量和安全实践”、“战略和政策实践”、组织实践、“过程管理实践”和“软计算实践”。“所有支持的假设都对RMS的采用产生了积极影响。然而，“可持续性实践”和“人力资源政策”这两个更积极的假设在分析中没有得到支持，这突出表明制造业需要提高对它们的认识。研究局限性/含义目前的研究仅限于47个已确定的因素；然而，这些因素可以进一步探索，并确定更多的子因素，这些因素在本研究中没有被考虑在内。实际含义管理者和从业者可以利用当前工作的结果来制定有效的RMS实施策略。研究结果还可用于提高制造系统的性能，并确定性能差的原因。独创性/价值本文确定了关键的RMS采用因素，并展示了一种有效的基于结构的建模方法。这可以用于各种领域，以帮助决策者和从业者选择和实施最佳制造系统。图形摘要

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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.

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