RESEARCH PAPER
Towards ensuring sustainable production - experimental study of the assembly process in the automotive industry.
1
Institute of Mechanical Engineering, University of Zielona Góra, Poland
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-01-11
Final revision date: 2026-05-24
Acceptance date: 2026-08-27
Online publication date: 2026-10-30
Corresponding author
KEYWORDS
monitoringsustainable productionInternet of Thingspredictive analyticsEnterprise Resource Planningassembly process efficiency
TOPICS
ABSTRACT
Enhancing sustainable production (SP) requires monitoring of industrial activities, particularly in relation to the Sustainable Development Goals (SDGs). In the automotive industry, implementing actions to enhance SP in line with the SDGs remains a key challenge. This study fills a research gap and develops a tool leveraging Industry 4.0 technologies for monitoring and reporting selected indicators characterizing SP, while maintaining high efficiency in core manufacturing operations. Experimental work is conducted based on the surface-mount assembly process for the production of electronic components used in a measuring device in the automotive industry. In the analysed case study the research results demonstrate the main relationships between assembly line efficiency and selected SP factor, namely energy consumption. Next, it proposes AI-based model for predicting energy consumption in assembly process towards ensuring SP. The results are generic for automotive industry due to a novel approach to integrate assembly efficiency parameters and SP indicators using IoT and ERP.
REFERENCES (31)
1.
Wang L, Keshavarzmanesh S, Feng HY, Buchal R. Assembly process planning and its future in collaborative manufacturing: a review. The International Journal of Advanced Manufacturing Technology 2009; 41: 132-144. https://doi.org/10.1007/s00170....
2.
Franke J, Wang L, Bock K, Wilde J. Electronic module assembly. CIRP Annals 2021; 70(2): 471-493. https://doi.org/10.1016/j.cirp....
3.
Gontermann A, Polzin J. Die globale Elektroindustrie-Daten, Zahlen und Fakten. ZVEI - Zentralverband Elektrotechnikund Elektronikindustrie e. V.Frank- furt am Main: 2020.
4.
Wang HK, Yang TY, Wang YH, Wu C. Hybrid dispatching and genetic algorithm for the surface mount technology scheduling problem in semiconductor factories. International Journal of Production Economics 2025; 280: 109500. https://doi.org/10.1016/j.ijpe....
5.
Pfab K, Eichler R, Mallandur A, Rothering M. Data of automated optical inspection of surface-mounted technology electronic production. Data in Brief 2024; 53:110110. https://doi.org/10.1016/j.dib.....
6.
Chen Y, Zhong J, Mumtaz J, Zhou S, Zhu L. An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line. Expert Systems with Applications 2023; 229: 120600. https://doi.org/10.1016/j.eswa....
7.
Veleva K, Hart M, Greiner T., Crumbley C. Indicators of sustainable production. Journal of Cleaner Production 2001; 9 (5): 447-452. https://doi.org/10.1016/S0959-....
8.
Lee J, Kao HA, Yang S. Service innovation and smart analytics for Industry 4.0 and the big data environment. Product-service systems and value creation. Proceedings of the 6th CIRP Conference on Industrial Product-Service Systems 2014; 16: 3-8. https://www.sciencedirect.com/....
9.
Beltrami M, Orzes G, Sarkis J, Sartor M. Industry 4.0 and sustainability: Towards conceptualization and theory. Journal of Cleaner Production 2021; 312: 127733. https://doi.org/10.1016/j.jcle....
10.
The Sustainable Development Goals Report, 2025, https://unstats.un.org/sdgs/re.... [accessed 5 January 2026].
12.
Silva FJG. Green Automation Increasing Sustainability, From Industry to Our Home. CRC Press Taylor & Francis, 2024.
13.
Todescato M, Braholli O, Chaltsev D, Blasio I, Don D, Egger G, Emig J, Monizza GP, Sacco P, Siegele D, Steiner D, Terzer M, Riedl M, Giusti A, Matt D. Sustainable manufacturing through application of reconfigurable and intelligent systems in production processes: a system perspective. Scientific Reports 2023; 13: 22374. https://doi.org/10.1038/s41598....
14.
Costa R, Sousa V, Silva FJG, Campilho R, Sá JC, Pinto A, Pereira J. Increasing the Sustainability of Manufacturing Processes in Plastic Injection: Recovering Out-Of-Service Robots to Eliminate Manual Assembly Operations. Sustainability 2022; 14; 12300. https://doi.org/10.3390/su1419....
15.
Zafarzadeh M, Flores-García E, Jeong Y, Wiktorsson M. A framework and system architecture for value-oriented digital services in data-driven production logistics. International Journal of Production Research 2025; 63(18); 6648-6668. https://doi.org/10.1080/002075....
16.
Uhlmann E, Bolz R, Schweitzer L, Loitsch L, Hmida N, Trabasso LG, Teixeira MF, de Souza D, Ribeiro J, Becker L, Domingues B. Smart Stamping and Injection Molding Tools by Innovative Measuring System. Procedia Computer Science 2026; 276: 316-323. https://doi.org/10.1016/j.proc....
17.
Marimin, Darmawan MA, Widhiarti RP, Teniwut YK. Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. Journal of Cleaner Production 2018; 191: 273-282. https://doi.org/10.1016/j.jcle....
18.
Khalid M, Peng Q. Investigation of Printing Parameters of Additive Manufacturing Process for Sustainability Using Design of Experiments Available to Purchase. Journal of Mechanical Design 2021; 143(3): 032001. https://doi.org/10.1115/1.4049....
19.
Hlavac M, Linde H, Riedel O. AI-Driven Concept for Monitoring Grinding Wheel Conditions, Procedia CIRP 2026; 138: 516-521. https://doi.org/10.1016/j.proc....
20.
Penne R, Silva F, Campilho R, et al. A new approach to increase the environmental sustainability of the discharging process in the over-injection of conduits for bowden cables using automation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 2022; 236(16): 8823-8833. https://doi:10.1177/0954406222....
21.
Dias P, Silva FJG, Campilho RDSG, Ferreira LP, Santos T. Analysis and Improvement of an Assembly Line in the Automotive Industry. Procedia Manufacturing 2019; 38: 1444-1452. https://doi.org/10.1016/j.prom....
22.
Sarkar P, Khanapuri VB, Tiwari MK. Strategic decision-making for sustainable production and distribution in automotive industry: a machine learning enabled dynamic multi-objective optimisation. International Journal of Production Research 2025; 63(7): 2339-2362. https://doi.org/10.1080/002075....
23.
Hevner AR, March ST, Park J, Ram S. Design Science in Information Systems Research. MIS Quarterly 2004; 28 (1): 75-105. https://doi.org/10.2307/251486....
24.
Holmström J, Ketokivi M, Hameri AP. Bridging Practice and Theory: A Design Science Approach. Decision Sciences 2009; 40 (1): 65-87.
25.
Bobko P. Correlation and regression: Applications for industrial organizational psychology and management. Sage: 2001.
26.
Kłos S, Patalas-Maliszewska J, Bazel M. Analysis and prediction of leak detection in the low-pressure heat treatment of metal equipment. Eksploatacja i Niezawodność – Maintenance and Reliability 2022; 24(4): 719-727. https://doi.org/10.17531/ein.2....
27.
Ponti MA, Ribeiro LSF, Nazare TS, Bui T, Collomosse J. Everything you wanted to know about deep learning for computer vision but were afraid to ask. 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T) 2017; 17-41. https://doi:10.1109/SIBGRAPI-T....
28.
Silva, JC. Silva FJG, Salgueiral Gomes Campilho RD, Vieira de Sá JC, Nunes Pinto Ferreira LCR. A Model for Productivity Improvement on Machining of Components for Stamping Dies. International Journal of Industrial Engineering and Management 2021; 12(2): 85-101. https://doi.org/10.24867/IJIEM....
29.
Ahanger TA, Bhatia M, Alabduljabbar A, Albanyan A. Applied artificial intelligence-based equipment condition monitoring in manufacturing industry. Engineering Applications of Artificial Intelligence 2026; 163(3): 113138. https://doi.org/10.1016/j.enga....
30.
Oduoza CF, Framework for Sustainable Risk Management in the Manufacturing Sector. Procedia Manufacturing 2020; 51: 1290-1297. https://doi.org/10.1016/j.prom....
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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