• International Journal of Technology (IJTech)
  • Vol 17, No 2 (2026)

Lights-Out Factory : Advancements, Challenges, and Prospects for Fully Autonomous Manufacturing

Lights-Out Factory : Advancements, Challenges, and Prospects for Fully Autonomous Manufacturing

Title: Lights-Out Factory : Advancements, Challenges, and Prospects for Fully Autonomous Manufacturing
Sivarao Subramonian, Kumaran Kadirgama, Zuhair Khalim, Abdulkareem Al-Obaidi, Satish Pujari, Rakesh Kumar Phanden, Anuar Kassim, Shukor Salleh, Umesh Vates, Amran Ali

Corresponding email:


Cite this article as:
Subramonian, S., Kadirgama, K., Khalim, Z., Al-Obaidi, A., Pujari, S., Phanden, R. K., Kassim, A., Salleh, S., Vates, U., & Ali, A. (2026). Lights-out factory : Advancements, challenges, and prospects for fully autonomous manufacturing. International Journal of Technology, 17 (2), 537–564


12
Downloads
Sivarao Subramonian Centre for Smart Systems and Innovative Design, Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia
Kumaran Kadirgama Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
Zuhair Khalim Centre for Smart Systems and Innovative Design, Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia
Abdulkareem Al-Obaidi School of Engineering, Taylor’s University, Taylor’s Lakeside Campus, 47500, Subang Jaya, Selangor Darul Ehsan, Malaysia
Satish Pujari Department of Mechanical Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, 535005, Andhra Pradesh, India
Rakesh Kumar Phanden 1. Institute of Innovation, Science and Sustainability, Federation University Australia, PO Box 663, Ballarat, VIC 3353, Australia 2. Department of Mechanical Engineering, Amity School of Engineerin
Anuar Kassim Centre of Robotics and Industrial Automation, Faculty of Technology and Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
Shukor Salleh Centre for Smart Systems and Innovative Design, Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia
Umesh Vates Department of Mechanical Engineering, Amity School of Engineering & Technology, Amity University Uttar Pradesh, Sector 125, Noida 201303, India
Amran Ali Centre for Smart Systems and Innovative Design, Faculty of Industrial & Manufacturing Technology & Engineering, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia
Email to Corresponding Author

Abstract
Lights-Out Factory : Advancements, Challenges, and Prospects for Fully Autonomous Manufacturing

Lights-out factories, or fully autonomous factories, integrate robotics, artificial intelligence (AI), and the Internet of Things (IoT) to enable continuous, human-free production. Initially conceptualized in the 1980s, early implementations faced technological and economic barriers. However, advancements in AI-driven predictive maintenance, real-time analytics, and IoT connectivity have enhanced feasibility. This study paper employs a qualitative comparative analysis framework to examine the evolution, key technologies, and challenges of lights-out factories, particularly through Fuji Automatic Numerical Control (FANUC) and Tesla case studies. The concept of lights-out factories is evolving rapidly, driven by advances in artificial intelligence (AI), Internet of Things (IoT), robotics, and cyber-physical systems (CPS). Predictive maintenance algorithms, for instance, reduce downtime by 30%, while smart sensors boost production efficiency by 20%. Fully autonomous factories have shown labour cost savings of over 35%, especially in high-volume operations. These trends highlight the shift toward data-driven, self-regulating manufacturing with minimal human involvement. While lights-out factory increases efficiency, lowers labour costs, and enhances product quality, challenges such as cybersecurity risks, high capital investment, and adaptability to production variability remain. Workforce displacement further necessitates reskilling initiatives to sustain employment. AI-driven decision-making, collaborative robotics, and blockchain-secured IoT networks will improve flexibility and security in the future. Industry 5.0 emphasizes human-machine collaboration, shifting from full automation to synergy between AI and human oversight. Addressing integration challenges through strategic investments, innovation, and regulatory frameworks will determine the long-term success of autonomous manufacturing. This study provides a comprehensive analysis of the opportunities and challenges associated with lights-out factories, offering insights into their viability in modern industrial landscapes.

Advanced manufacturing; Dark-manufacturing; Fully autonomous manufacturing; Internet of thing (IoT); Lights-out factory; Robotics manufacturing

References

Akkaladevi, S. C., Pichler, A., Plasch, M., Ikeda, M., & Hofmann, M. (2019). Skill-based programming of complex robotic assembly tasks for industrial application. Elektrotechnik Und Informationstechnik, 136 (7). https://doi.org/10.1007/s00502-019-00741-4

Arjun Santhosh, Risya Unnikrishnan, Sillamol Shibu, Meenakshi, K. M., & Joseph, G. (2023). Ai impact on job automation. International Journal of Engineering Technology and Management Sciences, 7 (4). https://doi.org/10.46647/ijetms.2023.v07i04.055

Atalay, ?I., Isen, O. A., Cantez, E., Aydin, S., & Akyel, O. (2020). Integrated real time image processing in robotic automation line. Academic Perspective Procedia, 3 (1). https://doi.org/10.33793/acperpro.03.01.33 

Atieh, A. M., Cooke, K. O., & Osiyevskyy, O. (2023). The role of intelligent manufacturing systems in the implementation of industry 4.0 by small and medium enterprises in developing countries. Engineering Reports, 5 (3). https://doi.org/10.1002/eng2.12578

Blau, J. (2007). Philips tears down eindhoven r&d fence. Research Technology Management, 50 (6).

Boeck, H., Lefebvre, L. A., & Lefebvre, E. (2017). Technological requirements and derived benefits from rfid enabled receiving in a supply chain. In Rfid handbook: Applications, technology, security, and privacy. CRC Press. https://doi.org/10.1201/9781420055009

Borowiecki, M., Machado, D., Paunov, C., & Planes-Satorra, S. (2019). Supporting research for sustainable development (tech. rep. No. 78). OECD.

Bouyahrouzi, E. M., El Kihel, A., Embarki, S., & El Kihel, B. (2023). Maintenance 4.0 model development for production lines in industry 4.0 using a deep learning approach and iot data in real-time: An experimental case study. Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS. https://doi.org/10.1109/IDAACS58523.2023.10348845

Busom, I., Corchuelo, B., & Mart ??nez-Ros, E. (2014). Tax incentives. . . or subsidies for business r&d? Small Business Economics, 43 (3). https://doi.org/10.1007/s11187-014-9569-1

Carabin, G., Wehrle, E., & Vidoni, R. (2017). A review on energy-saving optimization methods for robotic and automatic systems. Robotics, 6 (4). https://doi.org/10.3390/robotics6040039

CB Insights. (2018). The future of the factory: How technology is transforming manufacturing (tech. rep.). CB Insights.

Chaudhari, N. C., Patil, P. D., Chaudhari, M. R., Lanje, P. K., & More, M. S. (2017). Increasing productivity & quality of products by implementations of automation in manufacturing sectors. International Journal of Advance Research, Ideas and Innovations in Technology, 3 (2).

Chen, W. (2020). Intelligent manufacturing production line data monitoring system for industrial internet of things. Computer Communications, 151. https://doi.org/10.1016/j.comcom.2019.12.035

Chobanov, V., & Hardalov, I. (2022). The cost of man and machine labor in 21-st century. HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings. https://doi.org/10.1109/HORA55278.2022.9799915

Cholewa, A., Swider, J., & Zbilski, A. (2016). Numerical model of fanuc am100ib robot. ? IOP Conference Series: Materials Science and Engineering, 145 (5). https://doi.org/10.1088/1757-899X/145/5/052002

Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability (Switzerland), 12 (2). https://doi.org/10.3390/su12020492

Cordova, A. C. Q., Damiano, V. B. A., & Quiroz-Flores, J. C. (2023). Improving availability by lean manufacturing and tpm tools in an sme in the plastics sector. 2023 9th International Conference on Innovation and Trends in Engineering, CONIITI 2023 - Proceedings. https://doi.org/10.1109/CONIITI61170.2023.10324215

Cui, P. H., Wang, J. Q., & Li, Y. (2022). Data-driven modelling, analysis and improvement of multistage production systems with predictive maintenance and product quality. International Journal of Production Research, 60 (22). https://doi.org/10.1080/00207543.2021.1962558

de Mendon ?ca Santos, A., Silva, M. M., Godina, R., & Matias, J. C. O. (2024). Industry 4.0 technologies for sustainability within small and medium-sized enterprises: A systematic literature review. Journal of Cleaner Production, 442, 140960.

Devesh, M., Kant, A. K., Suchit, Y. R., Tanuja, P., & Kumar, S. N. (2020). Fruition of cps and iot in context of industry 4.0. In S. C. Satapathy, V. Bhateja, J. R. Mohanty, & S. K. Udgata (Eds.), Advances in intelligent systems and computing (pp. 455–465, Vol. 989). Springer. https://doi.org/10.1007/978-981-13-8618-3 39

Dodampegama, S., Hou, L., Asadi, E., Zhang, G., & Setunge, S. (2024). Revolutionizing construction and demolition waste sorting: Insights from artificial intelligence and robotic applications. Resources, Conservation and Recycling, 202. https://doi.org/10.1016/j.resconrec.2023.107375

Erdo ?gan, G. (2019). Land selection criteria for lights out factory districts during the industry 4.0 process. Journal of Urban Management, 8 (3). https://doi.org/10.1016/j.jum.2019.01.001

Ezenkwu, C. P., & Starkey, A. (2019). Machine autonomy: Definition, approaches, challenges and research gaps. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Advances in intelligent systems and computing (pp. 335–348, Vol. 997). Springer. https://doi.org/10.1007/978-3-030-22871-2_24

Fang, A., Chen, V., & McDonald, M. (2023). Breaking down the impact of automation in manufacturing. MIT Science Policy Review, 4. https://doi.org/10.38105/spr.ja3pmglhj7

Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., & D’amato, E. (2020). Towards digital twin implementation for assessing production line performance and balancing. Sensors (Switzerland), 20 (1). https://doi.org/10.3390/s20010097

Garc ??a, A. J. L., & Alvarado, A., I. (2013). Problems in the implementation process of advanced manufacturing technologies. International Journal of Advanced Manufacturing Technology, 64 (1-4). https://doi.org/10.1007/s00170-012-4011-9

Ghodsian, N., Benfriha, K., Olabi, A., Gopinath, V., Talhi, E., Hof, L. A., & Arnou, A. (2023). A framework to integrate mobile manipulators as cyber–physical systems into existing production systems in the context of industry 4.0. Robotics and Autonomous Systems, 169. https://doi.org/10.1016/j.robot.2023.104526

Gunasekaran, K., Vinoth Kumar, V., Kaladevi, A. C., Mahesh, T. R., Rohith Bhat, C., & Venkatesan, K. (2023). Smart decision-making and communication strategy in industrial internet of things. IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3258407

Honig, S., & Oron-Gilad, T. (2018). Understanding and resolving failures in human-robot interaction: Literature review and model development. Frontiers in Psychology, 9 (JUN). https://doi.org/10.3389/fpsyg.2018.00861

Hu, L., Miao, Y., Wu, G., Hassan, M. M., & Humar, I. (2019). Irobot-factory: An intelligent robot factory based on cognitive manufacturing and edge computing. Future Generation Computer Systems, 90. https://doi.org/10.1016/j.future.2018.08.006

Huysveld, S., Hubo, S., Ragaert, K., & Dewulf, J. (2019). Advancing circular economy benefit indicators and application on open-loop recycling of mixed and contaminated plastic waste fractions. Journal of Cleaner Production, 211. https://doi.org/10.1016/j.jclepro.2018.11.110

Ibrahim, A., & Kumar, G. (2024). Selection of industry 4.0 technologies for lean six sigma integration using fuzzy dematel approach. International Journal of Lean Six Sigma. https://doi.org/10.1108/IJLSS-05-2023-0090

Irshad Ali, S., Yousof, J., Rauf Khan, M., & Ather Masood, S. (2011). Evaluation of performance in manufacturing organization through productivity and quality. African Journal of Business Management, 5 (6).

Ivanova, L., & Ivanov, S. (2024). High-tech incomplete vehicle production. Science Intensive Technologies in Mechanical Engineering, 41–48. https://doi.org/10.30987/2223-4608-2024-4-41-48

Jain, P., Pateria, N., Anjum, G., Tiwari, A., & Tiwari, A. (2023). Edge ai and on-device machine learning for real time processing. International Journal of Innovative Research in Computer and Communication Engineering, 12 (05), 8137–8146. https://doi.org/10.15680/IJIRCCE.2024.1205364

Jauregui-Becker, J. M., & Wits, W. W. (2013). An information model for product development: A case study at philips shavers. Procedia CIRP, 9. https://doi.org/10.1016/j.procir.2013.06.175 

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Enabling flexible manufacturing system (fms) through the applications of industry 4.0 technologies. Internet of Things and Cyber-Physical Systems, 2. https://doi.org/10.1016/j.iotcps.2022.05.005

Jiang, T., & Wu, G. (2022). Design of online machining and monitoring management system based on fanuc machine tools. ACM International Conference Proceeding Series. https://doi.org/10.1145/3548608.3559296

Kadne, A., Kamath, P., Karvat, M., Bodkhe, M., & Sharma, S. (2024). A comprehensive study on industry 4.0 technologies. In E. names not available in provided information (Ed.), Lecture notes in mechanical engineering (pp. 217–230). Springer. https://doi.org/10.1007/978-981-99-8343-8_17

Kahan, T., Bukchin, Y., Menassa, R., & Ben-Gal, I. (2009). Backup strategy for robots’ failures in an automotive assembly system. International Journal of Production Economics, 120 (2). https://doi.org/10.1016/j.ijpe.2007.09.015

Kang, S., & Chung, K. (2020). Iot framework for interoperability in the onem2m architecture. Advances in Electrical and Computer Engineering, 20 (2). https://doi.org/10.4316/AECE.2020.02002

Katz, Y. (2021). Government’s role in advancing innovation. Randwick International of Social Science Journal, 2 (2). https://doi.org/10.47175/rissj.v2i2.213

Khorram Niaki, M., & Nonino, F. (2018). Selection and implementation of additive manufacturing. In Springer series in advanced manufacturing (Chapter 7). Springer. https://doi.org/10.1007/978-3-319-56309-1 7

Kondratenko, Y., Atamanyuk, I., Sidenko, I., Kondratenko, G., & Sichevskyi, S. (2022). Machine learning techniques for increasing efficiency of the robot’s sensor and control information processing. Sensors, 22 (3). https://doi.org/10.3390/s22031062

Kurniawati, A. M., Sutisna, N., Zakaria, H., Nagao, Y., Mengko, T. L., & Ochi, H. (2023). High throughput and low latency wireless communication system using bandwidth-efficient transmission for medical internet of thing. International Journal of Technology, 14 (4). https://doi.org/10.14716/ijtech.v14i4.5234

Lal, B., Vishnu Sakravarthy, N., Kumar, M. A., Chinthamu, N., & Pokhriyal, S. (2023). Development of product quality with enhanced productivity in industry 4.0 with ai driven automation and robotic technology. Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023. https://doi.org/10.1109/ICAISS58487.2023.10250736

Lee, W.-J., kim, J.-H., Kang, S.-W., & Kang, K.-S. (2015). A case for productivity improvement by time study in high tech industry. Journal of the Korea Safety Management and Science, 17 (1). https://doi.org/10.12812/ksms.2015.17.1.225

Li, J., Papadopoulos, C. T., & Zhang, L. (2016). Continuous improvement in manufacturing and service systems. International Journal of Production Research, 54 (21). https://doi.org/10.1080/00207543.2016.1228235

Li, L. (2022). Reskilling and upskilling the future-ready workforce for industry 4.0 and beyond. Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10308-y

Lin, H. C., Liu, C., & Tomizuka, M. (2018). Fast robot motion planning with collision avoidance and temporal optimization. 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018. https://doi.org/10.1109/ICARCV.2018.8581194

Linke, B., Huang, Y. C., & Dornfeld, D. (2012). Establishing greener products and manufacturing processes. International Journal of Precision Engineering and Manufacturing, 13 (7). https://doi.org/10.1007/s12541-012-0134-z

Lins, R. G., & Givigi, S. N. (2021). Cooperative robotics and machine learning for smart manufacturing: Platform design and trends within the context of industrial internet of things. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2021.3094374

Liu, Y., Li, Y., Zhuang, Z., & Song, T. (2020). Improvement of robot accuracy with an optical tracking system. Sensors (Switzerland), 20 (21). https://doi.org/10.3390/s20216341

Ludlow, K., Bowman, D. M., Gatof, J., & Bennett, M. G. (2015). Regulating emerging and future technologies in the present. NanoEthics, 9 (2). https://doi.org/10.1007/s11569-015-0223-4

Maddikunta, P. K. R., Pham, Q. V., B, P., Deepa, N., Dev, K., Gadekallu, T. R., Ruby, R., & Liyanage, M. (2022). Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 26. https://doi.org/10.1016/j.jii.2021.100257

Ma’ruf, A., Nasution, A. A. R., & Leuveano, R. A. C. (2024). Machine learning approach for early assembly design cost estimation: A case from make-to-order manufacturing industry. International Journal of Technology, 15 (4), 1037. https://doi.org/10.14716/ijtech.v15i4.5675

Meng, Z., Wu, Z., & Gray, J. (2020). Architecting ubiquitous communication and collaborative-automation-based machine network systems for flexible manufacturing. IEEE Systems Journal, 14 (1). https://doi.org/10.1109/JSYST.2019.2918542

Mohamed, N., Al-Jaroodi, J., & Lazarova-Molnar, S. (2019). Industry 4.0: Opportunities for enhancing energy efficiency in smart factories. SysCon 2019 - 13th Annual IEEE International Systems Conference, Proceedings. https://doi.org/10.1109/SYSCON.2019.8836751

Mohd Ghazali, M. H., & Rahiman, W. (2021). Vibration analysis for machine monitoring and diagnosis: A systematic review. Shock and Vibration, 2021. https://doi.org/10.1155/2021/9469318

M ?uller, R., H ?orauf, L., Speicher, C., Koch, J., & Drieß, M. (2019). Simulation based online production planning. Procedia Manufacturing, 38. https://doi.org/10.1016/j.promfg.2020.01.140

Murray, S. (2018). New technologies create opportunities. In C. Newman, J. Page, J. Rand, A. Shimeles, M. S ?oderbom, & F. Tarp (Eds.), Industries without smokestacks: Industrialization in africa reconsidered (Chapter 3). Oxford University Press. https://doi.org/10.1093/oso/9780198821885.003.0002

Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Brahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Frontiers in Surgery, 9. https://doi.org/10.3389/fsurg.2022.862322

Nixdorf, S., Ansari, F., & Sihn, W. (2021). Work-based learning in smart manufacturing: Current state and future perspectives. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3858379

Olivier, L. E., & Craig, I. K. (2017). Lights-out process control - analysis and framework. 2017 IEEE AFRICON: Science, Technology and Innovation for Africa, AFRICON 2017. https://doi.org/10.1109/AFRCON.2017.8095515

Omni Metalcraft. (2019, April). Double row forming with arb - omni metalcraft [[accessed 14 August 2025]].

Patic, P. C., Pascale, L., & M ?antescu, G. (2014). Simulation of a flexible manufacturing system for semi-products packaging. Applied Mechanics and Materials, 536-537. https://doi.org/10.4028/www.scientific.net/AMM.536-537.1654

Polishchuk, M., & Tkach, M. (2020). Experimental studies of robotic assembly of precision parts. FME Transactions, 49 (1). https://doi.org/10.5937/FME2101044P

Pop, E., Campean, E., Braga, I. C., & Ispas, D. (2022). New product development of a robotic soldering cell using lean manufacturing methodology. Sustainability (Switzerland), 14 (21). https://doi.org/10.3390/su142114057

Qu, T., Lei, S. P., Wang, Z. Z., Nie, D. X., Chen, X., & Huang, G. Q. (2016). Iot-based real-time production logistics synchronization system under smart cloud manufacturing. International Journal of Advanced Manufacturing Technology, 84 (1-4). https://doi.org/10.1007/s00170-015-7220-1

Raffik, R., Vaishali, V., Balavedhaa, S., Jyothi, L. N., & Sathya, R. R. (2023). Industry 5.0: Enhancing human-robot collaboration through collaborative robots - a review. 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023. https://doi.org/10.1109/ICAECA56562.2023.10201120

Ren, J., Wu, J., Ravn, O., & Nalpantidis, L. (2023). Functional requirements elicitation approach for the design and integration of robotic system for automation. 2023 5th International Conference on System Reliability and Safety Engineering, SRSE 2023. https://doi.org/10.1109/SRSE59585.2023.10336092

Resende, C., Folgado, D., Oliveira, J., Franco, B., Moreira, W., Oliveira-Jr, A., Cavaleiro, A., & Carvalho, R. (2021). Tip4.0: Industrial internet of things platform for predictive maintenance. Sensors, 21 (14). https://doi.org/10.3390/s21144676

Rodr ??guez Aguilar, M. J., Cardiel, I. A., & Somolinos, J. A. C. (2024). Iiot system for intelligent detection of bottleneck in manufacturing lines. Applied Sciences (Switzerland), 14 (1). https://doi.org/10.3390/app14010323

Rudigkeit, N., & Gebhard, M. (2019). Amicus—a head motion-based interface for control of an assistive robot. Sensors (Switzerland), 19 (12). https://doi.org/10.3390/s19122836

Rudra Kumar, M., Rupa Devi, B., Rangaswamy, K., Sangeetha, M., & Kumar, K. V. R. (2023). Iot-edge computing for efficient and effective information process on industrial automation. Proceedings of the 1st IEEE International Conference on Networking and Communications 2023, ICNWC 2023. https://doi.org/10.1109/ICNWC57852.2023.10127492

Rumsey, A., Morehouse, J. B., & Densmore, C. (2019). Evaluating manufacturing workforce development initiatives in georgia. Procedia Manufacturing, 34, 34–41. https://doi.org/10.1016/j.promfg.2019.06.231

Scaria, B., Aziz, N. A., & Panthakkan, A. (2019). Cost effective real time vision interface for off line simulation of fanuc robots. 2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019. https://doi.org/10.1109/ICSPIS48135.2019.9045895

Schwabe, H., & Castellacci, F. (2020). Automation, workers’ skills and job satisfaction. PLoS ONE, 15 (11 November). https://doi.org/10.1371/journal.pone.0242929

Sharma, A., & Kumar Tiwari, M. (2023). Digital twin design and analytics for scaling up electric vehicle battery production using robots. International Journal of Production Research, 61 (24). https://doi.org/10.1080/00207543.2022.2152896

She, C., Lin, Y., & Zhuang, W. (2018). Study of industrial robot numerical control program based on stationary tool control [ecar]. DEStech Transactions on Engineering and Technology Research. https://doi.org/10.12783/dtetr/ecar2018/26320

Silva, B., Sousa, J., & Alenya, G. (2021). Data acquisition and monitoring system for legacy injection machines. CIVEMSA 2021 - IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings. https://doi.org/10.1109/CIVEMSA52099.2021.9493675

Silva, C. S., Borges, A. F., & Magano, J. (2022). Quality control 4.0: A way to improve the quality performance and engage shop floor operators. International Journal of Quality and Reliability Management, 39 (6), 210–225.

Silva, M. D., Regnier, R., Makarov, M., Avrin, G., & Dumur, D. (2023). Evaluation of intelligent collaborative robots: A review. 2023 IEEE/SICE International Symposium on System Integration, SII 2023. https://doi.org/10.1109/SII55687.2023.10039365

Sithole, M., Telukdarie, A., & Katsumbe, T. (2023). Quality performance improvement through robotic process automation in rail manufacturing. PICMET 2023 - Portland International Conference on Management of Engineering and Technology: Managing Technology, Engineering and Manufacturing for a Sustainable World, Proceedings. https://doi.org/10.23919/PICMET59654.2023.10216813

Sizwe, N. (2022). Aligning education and workforce training with industry needs: A perspective on human capital development. International Journal of Workforce Development, 5 (2), 45–56. https://doi.org/10.46254/au01.20220348

Solanki, S. M. (2023). Industry 4.0 and smart manufacturing: Exploring the integration of advanced technologies in manufacturing. Revista Review Index Journal of Multidisciplinary, 3 (2). https://doi.org/10.31305/rrijm2023.v03.n02.005

Sowmya, K., & Chetan, N. (2016). A review on effective utilization of resources using overall equipment effectiveness by reducing six big losses. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), 2 (1), 102–110.

Srinivasan, R., Kumar, M., & Narayanan, S. (2020). Human resource management in an industry 4.0 era: A supply chain management perspective. In T. Y. Choi, J. J. Li, D. S. Rogers, T. Schoenherr, & S. M. Wagner (Eds.), The oxford handbook of supply chain management. Oxford University Press.

Suryadevara, M., Rangineni, S., & Venkata, S. (2023). Optimizing efficiency and performance: Investigating data pipelines for artificial intelligence model development and practical applications. International Journal of Science and Research (IJSR), 12 (7). https://doi.org/10.21275/sr23719211528

Sutarman, A., Kadim, A., & Garad, A. (2024). The effect of competence and organizational commitment on work productivity of indonesian manufacturing industries. International Journal of Technology, 15 (5), 1449. https://doi.org/10.14716/ijtech.v15i5.5775

Tercan, H., & Meisen, T. (2023). Online quality prediction in windshield manufacturing using data-efficient machine learning. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3580305.3599880

Thakkar, D., & Kumar, R. (2024). Ai-driven predictive maintenance for industrial assets using edge computing and machine learning. Journal for Research in Applied Sciences and Biotechnology, 3 (1), 363–367. https://doi.org/10.55544/jrasb.3.1.55

Tripathy, S. M., Chouhan, A., Dix, M., Kotriwala, A., Kl ?opper, B., & Prabhune, A. (2022). Explaining anomalies in industrial multivariate time-series data with the help of explainable ai. Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022. https://doi.org/10.1109/BigComp54360.2022.00051

TXOne Networks. (2023). Fanuc robot off-line programming path traversal vulnerability (cve-2023-1027 1864) [[accessed 19 February 2025]].

Uhlmann, E. (2023). Recent advances in precision, sustainability and safety of machine tools. Journal of Machine Engineering, 23 (3). https://doi.org/10.36897/jme/169941

Ungan, M. C. (2007). Manufacturing best practices: Implementation success factors and performance. Journal of Manufacturing Technology Management, 18 (3). https://doi.org/10.1108/17410380710730657

Verevka, T., & Gao, Y. (2025). Market valuation of high-tech companies in the it and automotive industries: A regression analysis of key factors. International Journal of Technology, 16 (2), 585. https://doi.org/10.14716/ijtech.v16i2.7418

Vilela De Souza, B., Barros Dos Santos, S. R., De Oliveira, A. M., & Givigi, S. N. (2022). Analyzing and predicting overall equipment effectiveness in manufacturing industries using machine learning. SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings. https://doi.org/10.1109/SysCon53536.2022.9773846

Wang, Y. Q., Hu, Y. D., El Zaatari, S., Li, W. D., & Zhou, Y. (2021). Optimised learning from demonstrations for collaborative robots. Robotics and Computer-Integrated Manufacturing, 71. https://doi.org/10.1016/j.rcim.2021.102169

Whulanza, Y., Kusrini, E., Sangaiah, A. K., Hermansyah, H., Sahlan, M., Asvial, M., Harwahyu, R., & Fitri, I. R. (2024). Bridging human and machine cognition: Advances in brain-machine interface and reverse engineering the brain. International Journal of Technology, 15 (5), 1194. https://doi.org/10.14716/ijtech.v15i5.7297

Wieland, S., Gonzalez-Aguirre, D., Vahrenkamp, N., Asfour, T., & Dillmann, R. (2009). Combining force and visual feedback for physical interaction tasks in humanoid robots. 9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09. https://doi.org/10.1109/ICHR.2009.5379544

Worrell, E., Bernstein, L., Roy, J., Price, L., & Harnisch, J. (2009). Industrial energy efficiency and climate change mitigation. Energy Efficiency, 2 (2). https://doi.org/10.1007/s12053-008-9032-8

Xia, T., An, X., Yang, H., Jiang, Y., Xu, Y., Zheng, M., & Pan, E. (2023). Efficient energy use in manufacturing systems—modeling, assessment, and management strategy. Energies, 16 (3). https://doi.org/10.3390/en16031095

Zhang, B., Wu, S., Wang, D., Yang, S., Jiang, F., & Li, C. (2023). A review of surface quality control technology for robotic abrasive belt grinding of aero-engine blades. Measurement: Journal of the International Measurement Confederation, 220. https://doi.org/10.1016/j.measurement.2023.113381

Zhang, R., Li, X., Zheng, Y., Lv, J., Li, J., Zheng, P., & Bao, J. (2022). Cognition-driven robot decision making method in human-robot collaboration environment. IEEE International Conference on Automation Science and Engineering, 2022-August. https://doi.org/10.1109/CASE49997.2022.9926617

Zhao, Y., He, Y., Zhou, D., Zhang, A., Han, X., Li, Y., & Wang, W. (2021). Functional risk-oriented integrated preventive maintenance considering product quality loss for multi-state manufacturing systems. International Journal of Production Research, 59 (4). https://doi.org/10.1080/00207543.2020.1713416

Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3 (5), 616–630.

Zou, J., Chang, Q., Lei, Y., & Arinez, J. (2018). Production system performance identification using sensor data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48 (2). https://doi.org/10.1109/TSMC.2016.2597062

Zou, J., Rong, B., Liu, Y., Rui, X., & Wang, G. (2024). Dynamics simulation and product quality consistency optimization of energetic material extrusion process. International Journal of Advanced Manufacturing Technology, 131 (3-4). https://doi.org/10.1007/s00170-024-13185-8