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Resources value mapping: A method to assess the resource efficiency of manufacturing systems

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  • Papetti, Alessandra
  • Menghi, Roberto
  • Di Domizio, Giulia
  • Germani, Michele
  • Marconi, Marco

Abstract

The assessment and monitoring of energy and resource efficiency is an essential activity toward the implementation of sustainable manufacturing practices. Existing energy/resource assessment methods and tools are not based on a comprehensive approach, lack on the use of specific key performance indicators, are dedicated to expert stakeholders and do not provide useful suggestions for improving production systems. This paper proposes an innovative method, called Resources Value Mapping that aims to map and classify activities and related energy/resource consumptions according to lean philosophy principles (value-added, non value-added, waste). A user-friendly map and two efficiency indicators (Cost Index and Muda Index) are proposed to quantitatively support the identification of criticalities related to activities, processes, lines, plants, etc., and to successively guide the decision-making process during the improvement strategies implementation. The method has been used to analyze a manufacturing plant that produces cooking appliances. The case study demonstrated the applicability of the method in real industrial contexts and its effectiveness in identifying the energy/resource flows (electricity and compressed air), departments (sheet department) and lines (mechanical and hydraulic presses) for which the waste and non value-added consumptions are prominent. The analysis highlighted that less of 20% of the resources consumed during the process creates value, offering wide margins for improvement. Finally, it aided the definition of an action plan leading to relevant reduction of resource consumptions, economic savings and environmental benefits.

Suggested Citation

  • Papetti, Alessandra & Menghi, Roberto & Di Domizio, Giulia & Germani, Michele & Marconi, Marco, 2019. "Resources value mapping: A method to assess the resource efficiency of manufacturing systems," Applied Energy, Elsevier, vol. 249(C), pages 326-342.
    • Handle: RePEc:eee:appene:v:249:y:2019:i:c:p:326-342
    DOI: 10.1016/j.apenergy.2019.04.158
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    References listed on IDEAS

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    Cited by:

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    4. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    5. Yan, Xiaopeng & Chen, Baijin, 2021. "Analysis of a novel energy-efficient system with 3-D vertical structure for hydraulic press," Energy, Elsevier, vol. 218(C).
    6. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
    7. Sun, Jingchao & Na, Hongming & Yan, Tianyi & Che, Zichang & Qiu, Ziyang & Yuan, Yuxing & Li, Yingnan & Du, Tao & Song, Yanli & Fang, Xin, 2022. "Cost-benefit assessment of manufacturing system using comprehensive value flow analysis," Applied Energy, Elsevier, vol. 310(C).
    8. Wen, Xuanhao & Cao, Huajun & Hon, Bernard & Chen, Erheng & Li, Hongcheng, 2021. "Energy value mapping: A novel lean method to integrate energy efficiency into production management," Energy, Elsevier, vol. 217(C).
    9. Liu, Weipeng & Peng, Tao & Kishita, Yusuke & Umeda, Yasushi & Tang, Renzhong & Tang, Wangchujun & Hu, Luoke, 2021. "Critical life cycle inventory for aluminum die casting: A lightweight-vehicle manufacturing enabling technology," Applied Energy, Elsevier, vol. 304(C).
    10. Frank Bertagnolli & Kerstin Herrmann & Isabel Rittmann & Tobias Viere, 2021. "The Application of Lean Methods in Corporate Sustainability—A Systematic Literature Review," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
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