Exploring the Challenges of Integrating Lean Green Practices in Industry 4.0 Manufacturing Frameworks: An Empirical Study

Kumar, Rajesh; Kumar, Rajender; Kumar, Ashwini

doi:10.1007/978-3-031-68271-1_12

Springer Series in Advanced Manufacturing

2024

DOI: 10.1007/978-3-031-68271-1_12

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Exploring the Challenges of Integrating Lean Green Practices in Industry 4.0 Manufacturing Frameworks: An Empirical Study

Rajesh Kumar,

Rajender Kumar,

Ashwini Kumar

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2024

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Cited by 2 publications

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A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Ojha,

Goyal,

Chand

et al. 2024

Concurrent Engineering

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Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.

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A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Ojha,

Goyal,

Chand

et al. 2024

Concurrent Engineering

View full text Add to dashboard Cite

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Impact of Lean Manufacturing Strategies and Success Factors on Performance Improvement—A Case Study

Kumar,

Kumar

2024

SAE Int. J. Mater. Manf.

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<div>LM (Lean manufacturing) is the manufacturing strategy focused on continuous improvement of manufacturing operations. This study has been carried out in manufacturing industry of northern India to assess important success factors, LM strategies applied, and important benefits of both LM strategies and approach. Questionnaire survey has been performed to achieve the desired objectives. Results indicated that manufacturing organizations have great affinity for LM strategies viz. small incremental improvements (kaizen) for strategic success. Production rates are highly improved after implementing LM approach. Mediating role of every success factor have been measured using regression analysis and structural equation modeling. Moreover, correlation shows the highly significant relations between LM strategies and benefits of the LM approach.</div>

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Exploring the Challenges of Integrating Lean Green Practices in Industry 4.0 Manufacturing Frameworks: An Empirical Study

Cited by 2 publications

References 35 publications

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Impact of Lean Manufacturing Strategies and Success Factors on Performance Improvement—A Case Study

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