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A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0

Author

Listed:
  • Varun Tripathi

    (Department of Mechanical Engineering, Accurate Institute of Management & Technology, Greater Noida 201306, India)

  • Somnath Chattopadhyaya

    (Department of Mechanical Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, India)

  • Alok Kumar Mukhopadhyay

    (Department of Mining Machinery Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, India)

  • Shubham Sharma

    (Department of Mechanical Engineering, IK Gujral Punjab Technical University, Main Campus-Kapurthala, Kapurthala 144603, India
    Department of Mechanical Engineering, University Centre for Research and Development, Chandigarh University, Mohali 140413, India)

  • Changhe Li

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Gianpaolo Di Bona

    (Department of Civil and Industrial Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy)

Abstract

The production management system in Industry 4.0 is emphasizes the improvement of productivity within limited constraints by sustainable production planning models. To accomplish this, several approaches are used which include lean manufacturing, kaizen, smart manufacturing, flexible manufacturing systems, cyber–physical systems, artificial intelligence, and the industrial Internet of Things in the present scenario. These approaches are used for operations management in industries, and specifically productivity maximization with cleaner shop floor environmental management, and issues such as worker safety and product quality. The present research aimed to develop a methodology for cleaner production management using lean and smart manufacturing in industry 4.0. The developed methodology would able to enhance productivity within restricted resources in the production system. The developed methodology was validated by production enhancement achieved in two case study investigations within the automobile manufacturing industry and a mining machinery assembly unit. The results reveal that the developed methodology could provide a sustainable production system and problem-solving that are key to controlling production shop floor management in the context of industry 4.0. It is also capable of enhancing the productivity level within limited constraints. The novelty of the present research lies in the fact that this type of methodology, which has been developed for the first time, helps the industry individual to enhance production in Industry 4.0 within confined assets by the elimination of several problems encountered in shop floor management. Therefore, the authors of the present study strongly believe that the developed methodology would be beneficial for industry individuals to enhance shop floor management within constraints in industry 4.0.

Suggested Citation

  • Varun Tripathi & Somnath Chattopadhyaya & Alok Kumar Mukhopadhyay & Shubham Sharma & Changhe Li & Gianpaolo Di Bona, 2022. "A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0," Mathematics, MDPI, vol. 10(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:347-:d:731919
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    References listed on IDEAS

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    1. Tyagi, Satish & Choudhary, Alok & Cai, Xianming & Yang, Kai, 2015. "Value stream mapping to reduce the lead-time of a product development process," International Journal of Production Economics, Elsevier, vol. 160(C), pages 202-212.
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    Cited by:

    1. Valentina De Simone & Valentina Di Pasquale & Maria Elena Nenni & Salvatore Miranda, 2023. "Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    2. Lin, Weiwen & Qin, Shan & Zhou, Xinzhu & Guan, Xin & Zeng, Yanzhao & Wang, Zeyu & Shen, Yaohan, 2024. "Three-dimensional quantitative mineral prediction from convolutional neural network model in developing intelligent cleaning technology," Resources Policy, Elsevier, vol. 88(C).
    3. Hao Li & Xiaocong Wang & Yan Liu & Gen Liu & Zhongshang Zhai & Xinyu Yan & Haoqi Wang & Yuyan Zhang, 2023. "A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    4. Xiaoyu Wen & Qingbo Song & Yunjie Qian & Dongping Qiao & Haoqi Wang & Yuyan Zhang & Hao Li, 2023. "Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
    5. Varun Tripathi & Somnath Chattopadhyaya & Alok Kumar Mukhopadhyay & Shubham Sharma & Changhe Li & Sunpreet Singh & Waqas Ul Hussan & Bashir Salah & Waqas Saleem & Abdullah Mohamed, 2022. "A Sustainable Productive Method for Enhancing Operational Excellence in Shop Floor Management for Industry 4.0 Using Hybrid Integration of Lean and Smart Manufacturing: An Ingenious Case Study," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    6. Âli Yurdun Orbak & Metin Küçük & Mehmet Akansel & Shubham Sharma & Changhe Li & Raman Kumar & Sunpreet Singh & Gianpaolo Di Bona, 2023. "Mathematical Model Assisted Six-Sigma Approach for Reducing the Logistics Costs of a Pipe Manufacturing Company: A Novel Experimental Approach," Mathematics, MDPI, vol. 11(3), pages 1-18, January.

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