Optimizing energy efficiency through smart manufacturing solutions in small-scale metal industries

Authors

  • Madan Mohanrao Jagtap Symbiosis Institute of Operations Management, Nashik Campus, Constituent of Symbiosis International (Deemed University), Nashik 422008, India https://orcid.org/0000-0001-6428-1241
  • Vandana Prashant Sonwaney Symbiosis Institute of Operations Management, Nashik Campus, Constituent of Symbiosis International (Deemed University), Nashik 422008, India https://orcid.org/0000-0002-2131-2041
  • Sagar Ramesh Khiste Symbiosis Institute of Operations Management, Nashik Campus, Constituent of Symbiosis International (Deemed University), Nashik 422008, India; Department of Mechanical Engineering, CSMSS Chh. Shahu College of Engineering, Chhatrapati Sambhajinagar 431011, India https://orcid.org/0009-0002-5429-2101
Article ID: 628
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DOI:

https://doi.org/10.18686/cest628

Keywords:

small-scale metal industry (SMI); small and medium enterprise (SME); Internet of Things (IoT); Digital Life Cycle Management Framework (DLCMF); process mining; FlexSim; sustainable production

Abstract

Small Scale Metal Industries (SMI) require huge amounts of energy to function. Although small-scale industries play a vital role in contributing to the economic development of the nation by way of exporting manufactured goods, the use of outdated systems and equipment results in a loss of energy efficiency and a lack of visibility and transparency of the process flow. As a solution to the problems that arise from poor digital integration in such processes, this paper presents a framework referred to as DLCMF (Digital Life Cycle Management Framework) for energy-intensive small and medium enterprises. In order to monitor the consumption and energy flow in the processes, process mining, real-time analytics, and discrete event simulations have been incorporated into the framework. In the context of this research, a simulation of a Machining industry in the state of Maharashtra was undertaken using the software Flexsim, which has been designed with Industry 4.0 and IoT capabilities. It has proven to be effective in reducing the consumption of energy (by 22%) and the amount of materials used (17%). In addition to this, the model facilitates a seamless integration process with respect to smart sensors, PLCs, and ERP systems, resulting in digital transformation within traditional manufacturing settings. The results resonate well with the Sustainable Development Goals, especially SDG 9, SDG 7, and SDG 13, due to improved energy efficiency, cleaner energy usage, and lower emissions of greenhouse gases. The paper provides good implications for policymakers, SME owners, and researchers who would want to align small-scale industrial practices with global sustainability objectives.

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Published

2026-04-22

How to Cite

Mohanrao Jagtap, M., Prashant Sonwaney, V., & Ramesh Khiste, S. (2026). Optimizing energy efficiency through smart manufacturing solutions in small-scale metal industries. Clean Energy Science and Technology, 4(2). https://doi.org/10.18686/cest628

Issue

Vol. 4 No. 2 (2026)

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Article

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Copyright (c) 2026 Madan Mohanrao Jagtap, Vandana Prashant Sonwaney, Sagar Ramesh Khiste

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

1. Energy system of Germany. Available online: https://www.iea.org/countries/germany (accessed on 11 March 2023).

2. Qin J, Liu Y, Grosvenor R. A Framework of Energy Consumption Modelling for Additive Manufacturing Using Internet of Things. Procedia CIRP. 2017; 63: 307–312. doi: 10.1016/j.procir.2017.02.036 DOI: https://doi.org/10.1016/j.procir.2017.02.036

3. Rajagopal D, Liu B. The United States can generate up to 3.2 EJ of energy annually from waste. Nature Energy. 2020; 5(1): 18–19. doi: 10.1038/s41560-019-0532-x DOI: https://doi.org/10.1038/s41560-019-0532-x

4. Map of Reid Vapor Pressure specifications for summer-grade gasoline by region, April 2026. Available online: https://www.eia.gov (accessed on 11 March 2023).

5. Atmaca AC, Kiray SA, Pehlivan M. Sustainable development from past to present. In: Education Research Highlights in Mathematics, Science and Technology. ISRES Publishing; 2018. pp. 186–214.

6. Sachs JD. From Millennium Development Goals to Sustainable Development Goals. The Lancet. 2012; 379(9832): 2206–2211. doi: 10.1016/S0140-6736(12)60685-0 DOI: https://doi.org/10.1016/S0140-6736(12)60685-0

7. World Commission on Environment and Development. Our Common Future. Oxford University Press; 1987.

8. Safarzadeh S, Rasti-Barzoki M, Hejazi SR. A review of optimal energy policy instruments on industrial energy efficiency programs, rebound effects, and government policies. Energy Policy. 2020; 139: 111342. doi: 10.1016/j.enpol.2020.111342 DOI: https://doi.org/10.1016/j.enpol.2020.111342

9. Thollander P, Danestig M, Rohdin P. Energy policies for increased industrial energy efficiency: Evaluation of a local energy programme for manufacturing SMEs. Energy Policy. 2007; 35(11): 5774–5783. doi: 10.1016/j.enpol.2007.06.013 DOI: https://doi.org/10.1016/j.enpol.2007.06.013

10. Slimani J, Kadrani A, Harraki IE, et al. Renewable Energy Development in Morocco: Reflections on Optimal Choices through Long-term Bottom-up Modeling. In: Proceedings of the 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET); 9 December 2021; Cape Town, South Africa. pp. 1–5. doi: 10.1109/ICECET52533.2021.9698630 DOI: https://doi.org/10.1109/ICECET52533.2021.9698630

11. El Majaty S, Touzani A, Kasseh Y. Results and perspectives of the application of an energy management system based on ISO 50001 in administrative buildings—Case of Morocco. Materials Today: Proceedings. 2023; 72: 3233–3237. doi: 10.1016/j.matpr.2022.07.094 DOI: https://doi.org/10.1016/j.matpr.2022.07.094

12. Rampasso IS, Melo Filho GP, Anholon R, et al. Challenges Presented in the Implementation of Sustainable Energy Management via ISO 50001:2011. Sustainability. 2019; 11(22): 6321. doi: 10.3390/su11226321 DOI: https://doi.org/10.3390/su11226321

13. Carmona-Martínez AA, Fresneda-Cruz A, Rueda A, et al. Renewable Power and Heat for the Decarbonisation of Energy-Intensive Industries. Processes. 2022; 11(1): 18. doi: 10.3390/pr11010018 DOI: https://doi.org/10.3390/pr11010018

14. Kaygusuz K. Energy for Sustainable Development: Key Issues and Challenges. Energy Sources, Part B: Economics, Planning, and Policy. 2007; 2(1): 73–83. doi: 10.1080/15567240500402560 DOI: https://doi.org/10.1080/15567240500402560

15. Bureau of Energy Efficiency. Impact Assessment of Energy Efficiency for Year 2020–21. Government of India; 2024.

16. Kamble SS, Gunasekaran A, Sharma R. Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry. 2018; 101: 107–119. doi: 10.1016/j.compind.2018.06.004 DOI: https://doi.org/10.1016/j.compind.2018.06.004

17. Alshahrani R, Rizwan A, Alomar MA, et al. IoT-Based Sustainable Energy Solutions for Small and Medium Enterprises (SMEs). Energies. 2024; 17(16): 4144. doi: 10.3390/en17164144 DOI: https://doi.org/10.3390/en17164144

18. Ma J, Chen H, Zhang Y, et al. A digital twin-driven production management system for production workshop. The International Journal of Advanced Manufacturing Technology. 2020; 110(5–6): 1385–1397. doi: 10.1007/s00170-020-05977-5 DOI: https://doi.org/10.1007/s00170-020-05977-5

19. De Leoni M, Van Der Aalst WMP, Dees M. A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Information Systems. 2016; 56: 235–257. doi: 10.1016/j.is.2015.07.003 DOI: https://doi.org/10.1016/j.is.2015.07.003

20. Pidd M. Computer Simulation in Management Science, 5th ed. John Wiley & Sons; 2004.

21. [21]3D Simulation Modeling and Analysis Software. Available online: https://www.flexsim.com (accessed on 2 February 2026).

22. Elhusseiny HM, Crispim J. SMEs, Barriers and Opportunities on adopting Industry 4.0: A Review. Procedia Computer Science. 2022; 196: 864–871. doi: 10.1016/j.procs.2021.12.086 DOI: https://doi.org/10.1016/j.procs.2021.12.086

23. Buer SV, Strandhagen JO, Chan FTS. The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. International Journal of Production Research. 2018; 56(8): 2924–2940. doi: 10.1080/00207543.2018.1442945 DOI: https://doi.org/10.1080/00207543.2018.1442945

24. Chowdhury RH. The economic potential of autonomous systems enabled by digital transformation and business analytics. World Journal of Economics and Business Research. 2024; 2(2): 33–42. doi: 10.61784/wjebr3017 DOI: https://doi.org/10.61784/wjebr3017

25. Tiwari D, Miscandlon J, Tiwari A, et al. A Review of Circular Economy Research for Electric Motors and the Role of Industry 4.0 Technologies. Sustainability. 2021; 13(17): 9668. doi: 10.3390/su13179668 DOI: https://doi.org/10.3390/su13179668

26. Ma S, Zhang Y, Liu Y, et al. Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. Journal of Cleaner Production. 2020; 274: 123155. doi: 10.1016/j.jclepro.2020.123155 DOI: https://doi.org/10.1016/j.jclepro.2020.123155

27. Xue X, Wang S, Chun T, et al. An integrated framework for industrial symbiosis performance evaluation in an energy-intensive industrial park in China. Environmental Science and Pollution Research. 2023; 30(14): 42056–42074. doi: 10.1007/s11356-023-25232-0 DOI: https://doi.org/10.1007/s11356-023-25232-0

28. Alex B, Johnson M. A Framework for IoT-Enabled Smart Manufacturing for Energy and Resource Optimization. arXiv preprint. 2025. doi:10.48550/arXiv.2502.03040

29. Schmitt T, Mattsson S, Flores-García E, et al. Achieving energy efficiency in industrial manufacturing. Renewable and Sustainable Energy Reviews. 2025; 216: 115619. doi: 10.1016/j.rser.2025.115619 DOI: https://doi.org/10.1016/j.rser.2025.115619

30. Ghobakhloo M, Iranmanesh M. Digital transformation success under Industry 4.0: A strategic guideline for manufacturing SMEs. Journal of Manufacturing Technology Management. 2021; 32(8): 1533–1556. doi: 10.1108/JMTM-11-2020-0455 DOI: https://doi.org/10.1108/JMTM-11-2020-0455

31. Vu VQ, Tran MQ, Vu LT. Applications of artificial intelligence and IoT technologies in smart manufacturing. Frontiers in Mechanical Engineering. 2023; 9: 1160923. doi: 10.3389/fmech.2023.1160923 DOI: https://doi.org/10.3389/fmech.2023.1160923

32. Pixley JE, Kunrath S, Paz G, et al. Smart Manufacturing in Small and Medium Enterprises in the United States: Implementation, Drivers, Barriers, and Technologies. University of California; 2021. Available online: https://www.calplug.org/wp-content/uploads/2021-Pixley-et-al-SCW-Survey.pdf

33. Ejaz MR. Smart Manufacturing as a Management Strategy to Achieve Sustainable Competitiveness. Journal of the Knowledge Economy. 2024; 15(1): 682–705. doi: 10.1007/s13132-023-01097-z DOI: https://doi.org/10.1007/s13132-023-01097-z

34. Setyadi A, Soekotjo S, Lestari SD, et al. Trends and Opportunities in Sustainable Manufacturing: A Systematic Review of Key Dimensions from 2019 to 2024. Sustainability. 2025; 17(2): 789. doi: 10.3390/su17020789 DOI: https://doi.org/10.3390/su17020789