Application of system dynamics modelling for a sustainable manufacturing system of an Indian automotive component manufacturing organisation: a case study

Thirupathi, R.M.; Vinodh, S.; Dhanasekaran, S.

doi:10.1007/s10098-019-01692-2

Cited by 24 publications

(17 citation statements)

References 25 publications

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“…The automotive sector (AS) is a primary manufacturing sector that creates jobs and provides mobility to millions of people worldwide. The AS often has a significant share in countries' gross domestic product and is considered an essential pillar for the economic growth of countries where it operates (Thirupathi, Vinodh, & Dhanasekaran, 2019). At the same time, the Indian AS is one of the most polluting sectors in the country, requiring accelerated efforts toward adopting sustainable and environmentally friendly practices.…”

Section: Introductionmentioning

confidence: 99%

Sustainable manufacturing and green human resources: Critical success factors in the automotive sector

et al. 2020

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This study aims to identify critical success factors (CSFs) for implementing sustainability in organizational human resources management (OHRM) within the automotive sector (AS). A literature review was carried out to identify the OHRM factors that are instrumental for the significant adoption of sustainable practices (SP) in the sector. Based on the outcomes from the literature review, an expert team composed of 17 experts from the AS, academia, and consultancy firms were consulted in order to identify cause–effect relationships among CSFs using a multi‐criteria decision making tool. Data were gathered in India from November 2019 to April 2020. The results revealed that top management support and environmental auditing for suppliers are the two most important CSFs to be prioritized among the factors studied, which implies that by managing these factors first, automotive manufacturers may deal better with the adoption of SP. As a result of prioritizing these two factors, other CSFs related to OHRM (e.g., organizational culture, environmental training, rewards, and incentives) will also positively affect the adoption of SP. The novelty of this study comes through raising the importance of environmental auditing for suppliers as a factor that can facilitate not only the adoption of SP in the AS, but the other OHRM factors as well.

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Section: Introductionmentioning

confidence: 99%

Sustainable manufacturing and green human resources: Critical success factors in the automotive sector

et al. 2020

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“…Thirupathi et al [11] developed a modeling framework based on system dynamics to implement sustainable manufacturing in the context of an automotive manufacturing organisation. They also analysed factors influencing sustainable initiatives using the proposed model.…”

Section: Literature Surveymentioning

confidence: 99%

“…Therefore, sustainable manufacturing practice entails preserving humans by continuously striking the balance between the triple bottom line (i.e., environmental, social and economic) conditions [9]. Although many researchers have outlined how to successfully implement sustainable manufacturing, organisations still face challenges and are consistently facing pressures from stakeholders including customers, governments and society [10,11].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Decision-Making Approach for Cause-And-Effect Analysis of Sustainable Manufacturing Indicators

et al. 2020

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Sustainability is a growing concern for manufacturing companies, as they are major contributors to pollution and consume a substantial portion of the world´s natural resources. Sustainable manufacturing can reduce waste, conserve energy and increase resource efficiency. However, one of the main challenges facing manufacturing organisations to put sustainability into practice is the lack of understanding of the cause-and-effect relationships between critical indicators of sustainable manufacturing. To overcome this challenge, a novel, rigorous and integrated framework, composed of four quantitative methods, is proposed to analyse critical indicators of sustainable manufacturing. The analysis is based on responses from both academic and industry experts. These four methods including DEMATEL (decision-making trial and evaluation laboratory), the MMDE (maximum mean de-entropy) algorithm, ISM (interpretive structural modelling) and SEM (structural equation modelling) are uniquely integrated to present statistically validated relationships between critical indicators using information on varying degrees of relationship between them. The final cause-and-effect models for the respondent groups (i.e., researchers and industry experts) are further validated through gathering the viewpoints of a researcher and an industry practitioner for its robustness. The novelty of our research lies in: (1) proposing a novel and integrated rigorous quantitative framework combined with qualitative research method; (2) applying the proposed framework to analyse contextual relationships between critical indicators of implementing sustainability, in the manufacturing sector as a whole, which to the best of authors' knowledge is the first of its kind; and (3) comparing and contrasting results of researchers and industry practitioners' groups along with a check of their validation and robustness.

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“…Statistics show that the manufacturing industry has high carbon emissions and is also one of the main sources of waste water and exhaust gas [1,2]. The realization of sustainable manufacturing will bring huge potential benefits to business and manufacturing [12][13][14][15], which will bring huge opportunities. Sustainable manufacturing has become a global consensus, as "sustainable manufacturing" has been listed by the U.S. Department of Energy as a strategic framework, with the European Union and China following suit [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Fuzzy MCDM Method for Implementation of Sustainable Precision Manufacturing: Evidence from China

Guan

Zhao

2022

Sustainability

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Despite that products of precision manufacturing are widely used in many fields involving the national economy, precision manufacturing processes are more unfriendly to the environment, resources and social development than general manufacturing. Hence, the implementation of sustainable precision manufacturing (SPM) is of great strategic significance. There is no literature identifying and ranking the drivers of implementation of SPM and the impact on sustainability owing to the application of advanced manufacturing technologies in SPM has not been explored. To resolve these problems, drivers of SPM are identified based on combined support of prior studies and six groups of experts consisting of 71 individuals from six precision manufacturing enterprises. The drivers are calculated and ranked by a two-step fuzzy MCDM method which integrated the fuzzy AHP (fuzzy analytic hierarchy process) and fuzzy TOPSIS (fuzzy technique for order of preference by similarity to ideal solution) algorithms. The evaluation of drivers is based on the basic principles of sustainable development (environmental criterion, social criterion and economic criterion). The paper concludes that technological innovation, government support and current legislation are the most critical drivers during SPM implementation. Additionally, the result of sensitivity verification of the proposed method conducted proves the robustness and correctness of the algorithm and results.

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Application of system dynamics modelling for a sustainable manufacturing system of an Indian automotive component manufacturing organisation: a case study

Cited by 24 publications

References 25 publications

Sustainable manufacturing and green human resources: Critical success factors in the automotive sector

Sustainable manufacturing and green human resources: Critical success factors in the automotive sector

An Integrated Decision-Making Approach for Cause-And-Effect Analysis of Sustainable Manufacturing Indicators

A Two-Step Fuzzy MCDM Method for Implementation of Sustainable Precision Manufacturing: Evidence from China

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