Published at : 31 Mar 2026
Volume : IJtech
Vol 17, No 2 (2026)
DOI : https://doi.org/10.14716/ijtech.v17i2.7651
| 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 |
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
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