Enhancing Industry 4.0 Energy Efficiency: A Data-Driven Dynamic Control for Pull-Flow Lines
Source abstract
Abstract
This paper investigates the effectiveness of dynamic switch-off policies in flow line production systems, aiming to balance energy efficiency and operational performance. A three-machine simulation model is developed and tested under steady-state and fluctuating processing conditions. The proposed policy, based on adaptive thresholds and Statistical Process Control (SPC) logic, is compared against two benchmarks: the traditional always-on model and a fixed switch-off policy. Simulation results demonstrate that the dynamic policy reduces customer-related performance measures—specifically queue lengths and waiting times—by approximately 50–56% compared to fixed policies. Crucially, this improvement is achieved while maintaining energy savings (~11%) and work-in-process reduction (~38%) comparable to the static approach. These benefits remain consistent even under high-variability scenarios, confirming the robustness of the proposed control architecture for Industry 4.0 sustainable manufacturing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Advanced Queuing Theory Analysis · Digital Transformation in Industry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Engineering, University of Basilicata, 85100 Potenza, Italy
References (41)
Show source-provided references
1. U.S. Energy Information Administration (2025, August 21). International Energy Outlook 2016, Available online: http://www.eia.gov/outlooks/ieo/pdf/0484(2016).pdf.
2. CECIMO (2009). Concept Description for CECIMO’s Self-Regulatory Initiative (SRI) for the Sector Specific Implementation of the Directive 2005/32/EC, CECIMO. Available online: http://www.ecodesign-info.eu/documents/Machine_tools_VA_20Oct09.pdf.
3. Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies10.1016/j.jmsy.2019.05.002 ↗
4. Dahmus, J.B., and Gutowski, T.G. (2004, January 13–19). An environmental analysis of machining. Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Anaheim, CA, USA.10.1115/IMECE2004-62600 ↗
5. Gutowski, T., Dahmus, J., Thiriez, A., Branham, M., and Jones, A. (2007, January 7–10). A thermodynamic characterization of manufacturing processes. Proceedings of the 2007 IEEE International Symposium on Electronics and the Environment, Orlando, FL, USA.10.1109/ISEE.2007.369382 ↗
6. Energy-efficient production systems through schedule-based operations10.1109/TASE.2012.2202226 ↗
7. Li, W., Zein, A., Kara, S., and Herrmann, C. (2011, January 2–4). An investigation into fixed energy consumption of machine tools. Proceedings of the 18th CIRP International Conference on Life Cycle Engineering (LCE), Braunschweig, Germany.10.1007/978-3-642-19692-8_47 ↗
8. Smart statistical process control for Industry 4.0: A data-driven framework for real-time quality monitoring
9. Adaptive multivariate functional EWMA control charts for high-dimensional profiles
10. An adaptive multivariate functional control chart for monitoring high-dimensional processes
11. Industry 5.0 and smart manufacturing: A systematic review of human-centric and sustainable industrial systems
12. Big Data-driven decision support systems for Industry 5.0 smart factories
13. International Energy Agency (IEA) (2024). Energy Efficiency 2024, IEA.
14. Energy-efficient control strategies for machine tools with stochastic arrivals10.1109/TASE.2014.2344507 ↗
15. Energy saving by switch-off policy in a pull-controlled production line10.1016/j.spc.2018.05.006 ↗
16. Renna, P. (2025). Switch-Off Policy in Flow Lines with Dynamic Fractional Task Assignation. Sustainability, 17.10.3390/su17094096 ↗
17. Energy-efficient on/off control in serial production lines with Bernoulli machines10.1007/s10696-022-09481-8 ↗
18. Bokor, B., Altendorfer, K., and Matta, A. (2025). Optimizing Energy Consumption in Stochastic Production Systems: Using a Simulation-Based Approach for Stopping Policy. arXiv.10.1016/j.jclepro.2025.146335 ↗
19. Dynamic Energy-saving Control Method for Intermittent State of Machine Tools Driven by Stochastic Tasks10.3901/JME.2025.11.348 ↗
20. Fernandes, J.M.R.C., Homayouni, S.M., and Fontes, D.B.M.M. (2022). Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review. Sustainability, 14.10.3390/su14106264 ↗
21. Missaoui, A., Ozturk, C., O’Sullivan, B., and Garraffa, M. (2024). Energy-Efficient Manufacturing Scheduling: A Systematic Literature Review. Handbook of Formal Optimization, Springer.10.1007/978-981-97-3820-5_56 ↗
22. Beier, J. (2017). Existing Approaches in the Field of Energy Flexible Manufacturing Systems. Simulation Approach Towards Energy Flexible Manufacturing Systems, Springer.10.1007/978-3-319-46639-2 ↗
23. Energy efficiency in the manufacturing industry—A tertiary review and a conceptual knowledge-based framework10.1016/j.egyr.2023.03.107 ↗
24. An online policy for energy-efficient state control of manufacturing equipment10.1109/TASE.2020.3044107 ↗
25. Li, H., Pangborn, H.C., and Kovalenko, I. (2023, January 26–30). A system-level energy-efficient digital twin framework for runtime control of batch manufacturing processes. Proceedings of the 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand.10.1109/CASE56687.2023.10260642 ↗
26. Alex, B., and Johnson, M. (2025). A Framework for IoT-Enabled Smart Manufacturing for Energy and Resource Optimization. arXiv.
27. Ioshchikhes, B., Frank, M., and Weigold, M. (2024). A systematic review of expert systems for improving energy efficiency in the manufacturing industry. Energies, 17.10.20944/preprints202408.1783.v2 ↗
28. Hsu, C.C., Jiang, B.H., and Lin, C.C. (2023). A survey on recent applications of artificial intelligence and optimization for smart grids in smart manufacturing. Energies, 16.10.3390/en16227660 ↗
29. Achieving energy efficiency in industrial manufacturing10.1016/j.rser.2025.115619 ↗
30. Muzayyanah, S., Hong, C.-Y., Adha, R., and Yang, S.-F. (2023). The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach. Sustainability, 15.10.3390/su15129404 ↗
31. A novel EWMA-based adaptive control chart for industrial application by using hastings approximation10.1038/s41598-024-83780-y ↗
32. An adaptive multivariate functional EWMA control chart10.1080/00224065.2024.2383674 ↗
33. Pawanr, S., and Gupta, K. (2024). A review on recent advances in the energy efficiency of machining processes for sustainability. Energies, 17.10.3390/en17153659 ↗
34. State-of-the-art review of energy consumption in machining operations: Challenges and trends10.1016/j.rser.2025.116073 ↗
35. Energy monitoring as a sustainable maintenance: A case study10.1088/1755-1315/1386/1/012024 ↗
36. Statistical process control in the era of Big Data and Industry 4.0
37. Machine-learning-enhanced SPC for multivariate monitoring in smart factories
38. IoT-enabled statistical process control for predictive monitoring in smart manufacturing
39. Digital-twin-driven quality and process monitoring using advanced SPC techniques
40. Industry 5.0 and the human-centric manufacturing paradigm
41. Towards Industry 5.0: Human-centricity and resilient cyber-physical production control systems