Analyzing the impact factors of remanufacturing industry on the Indian economy: a step towards circular economy

IF 1.8 Q3 MANAGEMENT
Vaishali Sharma, Rajesh Katiyar, Ruchi Mishra
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引用次数: 0

Abstract

Purpose

The purpose of this article is to investigate and analyze the interactions between economic and sustainable development elements in the context of remanufacturing in India.

Design/methodology/approach

To comprehend the hierarchical and contextual link among factors impacting remanufacturing in India, the study used interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) methodologies.

Findings

The integrated ISM-DEMATEL approach identifies optimal utilization of the resources as the most crucial factor influencing remanufacturing in India, followed by reducing landfills, conserving energy and low cost. The study also reveals that optimal utilization of resources, reduction of landfills, conservation of energy and incorporated advanced technology impacts most of the factors but get affected by a few factors.

Practical implications

Industry practitioners and policymakers should consider the remanufacturing process to achieve sustainable and economic development. The government and other stakeholders can use the ISM framework and cause-and-effect diagram to classify the impact factors and their impact on the Indian economy and environment.

Social implications

This study supports the process to save the landfills and curbing pollution, conserve energy and optimize utilization of the resources, generate employment and supporting the development of the economy. Remanufacturing will undoubtedly contribute to the development of an environment and economy in India that benefits both.

Originality/value

ISM and DEMATELs strategy offers a tiered model and a cause-and-effect relationship between the variables affecting remanufacturing in India.

分析再制造产业对印度经济的影响因素:迈向循环经济的一步
为了理解影响印度再制造的各种因素之间的层次和背景联系,本研究采用了解释性结构建模(ISM)和决策试验与评估实验室(DEMATEL)方法。研究结果ISM-DEMATEL 综合方法认为,资源的优化利用是影响印度再制造的最关键因素,其次是减少填埋、节约能源和低成本。研究还显示,资源优化利用、减少垃圾填埋、节约能源和采用先进技术对大多数因素都有影响,但也会受到少数因素的影响。政府和其他利益相关者可以使用 ISM 框架和因果关系图对影响因素及其对印度经济和环境的影响进行分类。社会意义本研究支持节约垃圾填埋场和遏制污染、节约能源和优化资源利用、创造就业和支持经济发展的进程。再制造无疑将促进印度环境和经济的发展,使二者受益。原创性/价值ISM 和 DEMATELs 战略提供了一个分层模型以及影响印度再制造的变量之间的因果关系。
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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