Authors : Ezekiel Alex Ohuei; I.S. Aji

Volume/Issue : Volume 10 - 2025, Issue 7 - July

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DOI : https://doi.org/10.38124/ijisrt/25jul799

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Abstract : Manufacturing efficiency has become crucial for industrial competitiveness in the 21st century, driven by advanced robotic systems and intelligent maintenance strategies. This systematic review examines how robotic automation and digital technologies transform modern manufacturing operations, particularly focusing on maintenance paradigms and operational performance impacts. The study defines manufacturing efficiency through two dimensions: technical efficiency (maximizing output from inputs) and allocative efficiency (optimal resource distribution). Contemporary approaches integrate product, process, and organizational complexity factors. The evolution from reactive to predictive and condition- based maintenance, powered by artificial intelligence, IoT technologies, and sensor analytics, has revolutionized equipment reliability and performance. Key findings reveal AI-powered predictive maintenance reduces unplanned downtime by 50%, cuts maintenance costs by 25%, and significantly extends equipment lifespans. Digital transformation through Industry 4.0 and emerging Industry 5.0 creates synergistic relationships between robotic systems, digital twin technologies, and intelligent maintenance frameworks. IoT sensors, machine learning algorithms, and computerized maintenance management systems enable real-time monitoring, predictive analytics, and automated responses that enhance manufacturing efficiency. Case study analysis of Innoson Vehicle Manufacturing demonstrates how emerging market manufacturers leverage robotic automation for substantial productivity gains, increasing annual production capacity from 10,000 to 60,000 vehicles through strategic automation implementation. However, challenges persist in workforce development, infrastructure limitations, cybersecurity concerns, and capital investment requirements, particularly for small and medium enterprises. Critical research gaps exist in understanding emerging market contexts, socioeconomic impacts, and long-term sustainability implications. Future directions emphasize autonomous maintenance systems, collaborative robotics, and sustainable manufacturing practices as competitive advantage enablers.

Keywords : Robotic, Manufacturing, Maintenance and Efficiency.

References :

1. Abdullayev, VA., Faizal, AZ., Seyidova, I., Mikayilov, S., Mammadova, R., Pirverdiyeva, L. and Guliyev, E. 2025. Integration of Artificial Intelligence and Robotics into the industrial sector. Data and Metadata, 4.
2. Adebayo, TA. and Oladipo, O. 2022. Challenges and opportunities in adopting robotic automation in Nigerian manufacturing industries. Journal of Industrial Technology, 38(2): 112-125.
3. Agbo, CO. 2020. Policy and the Quest for a viable Automotive Industry: A Lesson for the developing Economies. Journal of Science and Technology Management Policy, 11(4): 585-603.
4. Alade, B. 2020. National Automobile Policy still under Review, a year after. Retrieved from https://guardian.ng/features/executive-motoring/national-automotive-policy-still-under-review-a-year-after/
5. Al-Amin, MR., Hossain, R., Molla, C., Poli, TA., & Milon, MN. 2024. The adoption of Industry 4.0 technologies by using the technology organizational environment framework: The mediating role to manufacturing performance in a developing country. Business Strategy and Development, 7(2): e363.
6. Alexander, S. 2025. The Importance of Preventive Maintenance for Your Robot. Retrieved July 7, 2025, from https://www.minnesota-automation.com/blogs/why-pm-c6mca
7. Ambe, IM., and Badenhirst-Weiss, JA. 2019. Strategic supply chain framework for the automotive industry. Global Journal of Business Management, 13(1): 001-011.
8. Andrade, RG., Vinces, L., and Lau, K. 2023. A modular mechatronic gripper installed on the industrial robot KUKA KR 60-3 for boxing, unpacking and selecting of beverage bottles. International Journal on Interactive Design and Manufacturing, 17(1): 33-52.
9. APQC. 2023. Manufacturing key benchmarks: Automotive industry. Retrieved July 9, 2025, from https://www.apqc.org/resource-library/resource/manufacturing-key-benchmarks-automotive-industry
10. Arukwe, C. 2021. Innoson Unveils Two Manufacturing Plants and One Automated Robotic Spraying Booth, Get Commissioned by Minister of Finance, Budget, And National Planning. Retrieved July 9, 2025, from https://www.innosonvehicles.com/innoson-unveils-two-manufacturing-plants-and-one-automated-robotic-spraying-booth-get-commissioned-by-minister-of-finance-budget-and-national-planning/
11. Bhatta, K., and Chang, Q. 2023. An integrated control strategy for simultaneous robot assignment, tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network. Robotics and Computer Integrated Manufacturing, 84.
12. Blachowicz, T., Bysko, S., Bysko, S., Domanowska, A., Wylezek, J. and Sokol, Z. 2025. Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations. Sensors, 25(11).
13. Bokhtiar, MA., Khanh, VQ. and Nguyen, DC. 2025. Digital Twin in Industries: A Comprehensive Survey. IEEE Access, 13: 47291-47336.
14. Brügge, F. 2023. Predictive maintenance market: 5 highlights for 2024 and beyond. Retrieved July 7, 2025, from https://iot-analytics.com/predictive-maintenance-market/
15. Çalmaşur, G. 2016. Technical efficiency analysis in the automotive industry: A stochastic frontier approach. International Journal of Economics and Commerce Management, 4(4): 120-135.
16. Cheever, G. 2004. Remote services: High tech means high service performance. A BB Review. SPEC. ISS, 26–28.
17. Chen, SC., Chen, HM., Chen, HK. and Li, CL. 2024. Multi-Objective Optimization in Industry 5.0: Human-Centric AI Integration for Sustainable and Intelligent Manufacturing. Processes, 12: 2723.
18. Chen, YI., Ukaejiofo, RU., Xiaoyang, T. and Brautigam, D. 2016. Learning from China? Manufacturing, Investment, and Technology Transfer in Nigeria. Working Paper, China Africa Research Initiative, School of Advanced International Studies, Johns Hopkins University, Washington, DC, 2016(2).
19. da Costa, TP., da Costa, DM. and Murphy, F. 2024. A systematic review of real-time data monitoring and its potential application to support dynamic life cycle inventories. Environmental Impact Assessment Review, 105: 107416.
20. de Ron, AJ. and Rooda, JE. 2005. Equipment effectiveness: OEE revisited. IEEE Transactions on Semiconductor Manufacturing, 18(1): 190-196.
21. Emon, MM. and Khan, T. 2025. The transformative role of Industry 4.0 in supply chains: Exploring digital integration and innovation in the manufacturing enterprises. Journal of Open Innovation: Technology, Market, and Complexity, 11(2): 100516.
22. EY Global. 2023. Three tailwinds for robotics adoption in 2024 and beyond. Retrieved July 7, 2025, from https://www.ey.com/en_gl/insights/innovation/three-tailwinds-for-robotics-adoption-in-2024-and-beyond
23. Farell, L. 2025. 5 Challenges to Address in Scaling Robotics Solutions in the Workforce. Retrieved July 9, 2025, from https://www.artiba.org/blog/5-challenges-to-address-in-scaling-robotics-solutions-in-the-workforce
24. Filipescu, A., Simion, G., Ionescu, D. and Filipescu, A. 2024. IoT-Cloud, VPN, and Digital Twin-Based Remote Monitoring and Control of a Multifunctional Robotic Cell in the Context of AI, Industry, and Education 4.0 and 5.0. Sensors, 24(23).
25. Furtado, LS., Bessa, IS., Gurjão, NO. and Soares, JB. 2025. Integrating smart city technologies for sustainable pavement infrastructure. Canadian Journal of Civil Engineering, 52(5): 569–582.
26. Gama, J., Ribeiro, RP. and Veloso, B. 2022. Data-Driven Predictive Maintenance. IEEE Intelligent Systems, 37(4): 27–29.
27. Gill, R., Srivastava, D., Hooda, S., Singla, C. and Chaudhary, R. 2024. Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0. Engineering Management Journal, 1–19.
28. Gordon, A. 2021. Internet of things-based real-time production logistics, big data-driven decision-making processes, and industrial artificial intelligence in sustainable cyber-physical manufacturing systems. Journal of Self Governance and Management Economics, 9(3): 61–73.
29. GZ. 2024. Robotics in Industrial Maintenance: Improving Efficiency and Safety. Retrieved July 9, 2025, from https://www.gz-supplies.com/news/robotics-in-industrial-maintenance-improving-efficiency-and-safety/
30. IFR. 2024. Top 5 Robot Trends 2024. Retrieved July 7, 2025, from https://ifr.org/ifr-press-releases/news/top-5-robot-trends-2024
31. Islam, MT., Sepanloo, K., Woo, S., Woo, SH. and Son, YJ. 2025. A Review of the Industry 4.0 to 5.0 Transition: Exploring the Intersection, Challenges, and Opportunities of Technology and Human–Machine Collaboration. Machines, 13: 267.
32. Jaen-Cuellar, AY., Elvira-Ortiz, DA. and Saucedo-Dorantes, JJ. 2023. Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM. Machines, 11(7).
33. Kahnamouei, JT. and Moallem, M. 2024. Advancements in control systems and integration of artificial intelligence in welding robots: A review. Ocean Engineering, 312.
34. Khanchanapong, T., Prajogo, D., Sohal, AS., Cooper, BK., Yeung, AC. and Cheng, TC. 2014. The unique and complementary effects of manufacturing technologies and lean practices on manufacturing operational performance. International Journal of Production Economics, 153: 191-203.
35. Kolvig-Raun, ES., Kjaergaard, MB. and Brorsen, R. 2024. Joint Stress Estimation and Remaining Useful Life Prediction for Collaborative Robots to Support Predictive Maintenance. IEEE Robotics and Automation Letters, 9(4): 3554–3561.
36. Kostavelis, I. and Gasteratos, A. 2022. Conceptualizing Industry 4.0 for Greek Manufacturing Sector. Journal of Engineering Science and Technology Review, 15(2): 1–7.
37. Le, AV., Veerajagadeshwar, P., Shi, Y., Mohan, RE., Naing, MY., Tan, NN., . . . Vu, MB. 2022. Long-term trials for improvement of autonomous area coverage with a Tetris inspired tiling self-reconfigurable system. Expert Systems with Applications, 206.
38. Lee, J., Bagheri, B. and Jin, C. 2019. Introduction to cyber manufacturing. Manufacturing Letters, 20: 1-4.
39. Li, W., Peng, Y., Zhu, Y., Pham, DT., Nee, AY. and Ong, SK. 2024. End-of-life electric vehicle battery disassembly enabled by intelligent and human-robot collaboration technologies: A review. Robotics and Computer-Integrated Manufacturing, 89: 102758.
40. Liberati, AA., Tetzlaff, J., Mulrow, C., Gøtzsche, PC., Ioannidis, JP. and Moher, D. 2009. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of Clinical Epidemiology, 6(10): e1–e34.
41. Luz Tortorella, G., Cauchick-Miguel, PA., Li, W., Staines, J. and McFarlane, D. 2021. What does operational excellence mean in the Fourth Industrial Revolution era? International Journal of Production Research, 60(9): 2901–2917.
42. Makris, S., Michalos, G., Dimitropoulos, N., Krueger, J. and Haninger, K. 2024. Seamless Human–Robot Collaboration in Industrial Applications. In T. Tolio (Ed.), CIRP Novel Topics in Production Engineering: Volume 1. Lecture Notes in Mechanical Engineering (pp. 39-73). Cham: Springer.
43. Meegle. 2025. Robotics maintenance. Retrieved July 7, 2025, from https://www.meegle.com/en_us/topics/robotics/robotics-maintenance
44. MicroMain. 2024. Robotics and Automation in Maintenance: Transforming Industry Practices. Retrieved July 7, 2025, from https://micromain.com/robotics-and-automation-in-maintenance-transforming-industry-practices-2/
45. Morgan, M. 2025. To Reduce Equipment Downtime, Manufacturers Turn to AI Predictive Maintenance Tools. Retrieved July 9, 2025, from https://biztechmagazine.com/article/2025/03/reduce-equipment-downtime-manufacturers-turn-ai-predictive-maintenance-tools
46. Mourtzis, D., Angelopoulus, J. and Panapoulos, N. 2022. Chapter 2 - Digital Manufacturing: the evolution of traditional manufacturing toward an automated and interoperable Smart Manufacturing Ecosystem. In BL. MacCarthy, & D. Ivanov (Eds.), The digital supply chain (pp. 27-45). Elsevier.
47. Mujtaba, A., Islam, F., Kaeding, P., Lindemann, T. and Gangadhara Prusty, B. 2025. Machine-learning based process monitoring for automated composites manufacturing. Journal of Intelligent Manufacturing, 36(2): 1095–1110.
48. Odette. 2020. Key Performance Indicators for Automotive Supply Chain Management. London: Odette International Ltd.
49. Oguejiofor, AR. 2023. Innoson's Robots Push Annual Production Capacity to 60,000. Retrieved July 6, 2025, from https://www.thevaluechainng.com/innosons-robots-push-annual-production-capacity-to-60000/
50. Okokpujie, IP. 2025. Study of the economic viability of internet of things (IoTs) in additive and advanced manufacturing: A comprehensive review. Progress in Additive Manufacturing, 10: 3175–3194.
51. Okorie, IJ. 2024. Vehicle manufacturer Innoson unveils its first made-in-Nigeria electric vehicle. Retrieved July 9, 2025, from https://techpoint.africa/news/innoson-unveils-first-ev-nigeria/
52. Olawade, DB., Fapohunda, O., Wada, OZ., Usman, SO., Ige, AO., Ajisafe, O. and Oladapo, BI. 2024. Smart waste management: A paradigm shift enabled by artificial intelligence. Waste Management Bulletin, 2(2): 244-263.
53. Ortiz, JS., Quishpe, EK., Sailema, GX. and Guamán, NS. 2025. Digital Twin-Based Active Learning for Industrial Process Control and Supervision in Industry 4.0. Sensors, 25: 2076.
54. Otto, WL., Schalkwyk, D. and Schutte, CS. 2020. Implementation framework for automotive technology: how to introduce hybrid and electric vehicles into automotive production lines. South African Journal of Industrial Engineering, 31(1): 10-25.
55. Patil, D. 2024. Artificial Intelligence-Driven Predictive Maintenance in Manufacturing: Enhancing Operational Efficiency, Minimizing Downtime, And Optimizing Resource Utilization. Retrieved July 5, 2025, from http://dx.doi.org/10.2139/ssrn.5057406
56. Patrício, L., Varela, L. and Silveira, Z. 2025. Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance. Applied Sciences, 15: 854.
57. Rahama, M., Putra, KT., Irawan, B. and Biyanto, TK. 2025. Industrial evolution: The integration of AI robotics and drone systems. In AI Developments for Industrial Robotics and Intelligent Drones (pp. 85-114). Hershey: IGI Global Scientific Publishing.
58. Rakholia, R., Suarez-Cetrulo, AL., Singh, M. and Simon Carbajo, R. 2024. Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges, and Future Direction. IEEE Access, 12: 131621–131637.
59. Ramesh, PG., Dutta, SJ., Neog, SS., Baishya, P. and Bezbaruah, I. 2020. Implementation of Predictive Maintenance Systems in Remotely Located Process Plants under Industry 4.0 Scenario. Springer Series in Reliability Engineering, 2(3).
60. Rashid, AB. and Kausik, AK. 2024. AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 7: 100277.
61. Reşat, HG. 2021. Automotive Sector Analysis Report and Guide TR42 Region (Kocaeli, Sakarya, Düzce, Bolu, Yalova). Ankara, Turkey: Ministry of Industry and Technology General Directorate of Development.
62. Rinaldi, G., Thies, PR., & Johanning, L. 2021. Current status and future trends in the operation and maintenance of offshore wind turbines: A review. Energies, 14(9).
63. Sodhi, T. 2024. The Future of Auto Repair Robotics. Retrieved July 6, 2025, from https://conceptualminds.com/the-future-of-auto-repair-robotics/
64. Stefko, R., Michalikova, KF., Strakova, J. and Novak, A. 2025. Digital twin-based virtual factory and cyber-physical production systems, collaborative autonomous robotic and networked manufacturing technologies, and enterprise and business intelligence algorithms for industrial metaverse. Equilibrium Quarterly Journal of Economics and Economic Policy, 20(1): 389–425.
65. Susilawati, A. 2021. Productivity enhancement: lean manufacturing performance measurement based multiple indicators of decision making. Production Engineering, 15: 343–359.
66. Tanimu, JA. and Abada, W. 2025. Addressing cybersecurity challenges in robotics: A comprehensive overview. Cyber Security and Applications, 3: 100074.
67. Tran, B. and Attorney, P. 2025. Robotics Maintenance costs: Operating efficiency data. Retrieved July 7, 2025, from https://patentpc.com/blog/robotics-maintenance-costs-operating-efficiency-data
68. Vanguard. 2023. Innoson acquires robot equipment, increases production capacity to 60,000. Retrieved July 9, 2025, from https://www.vanguardngr.com/2023/10/innoson-acquires-robot-equipment-increases-production-capacity-to-60000/
69. Waltersmann, L., Kiemel, S., Stuhlsatz, J., Sauer, A. and Miehe, R. 2021. Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review. Sustainability, 13: 6689.
70. West, J., Siddhpura, M., Evangelista, A. And Haddad, A. 2024. Improving Equipment Maintenance—Switching from Corrective to Preventative Maintenance Strategies. Buildings, 14: 3581.
71. Wohlin, C., Kalinowski, M., Felizardo, KR., & Mendes, E. 2022. Successful combination of database search and snowballing for identification of primary studies in systematic literature studies. Information and Software Technology, 147: 106908.
72. XMPro. 2024. Predictive Maintenance for Robotic Arms in the Automotive Industry. Retrieved July 8, 2025, from https://xmpro.com/solutions-library/asset-performance-management,manufacturing,predictive-maintenance,use-cases/predictive-maintenance-for-robotic-arms-in-the-automotive-industry/