Impact of IoT Technology Implementation in the Manufacturing Sector: A Systematic Literature Review
Main Article Content
Keywords
Internet of Things (IoT), Smart Manufacturing, Digital Twin, Industry 4.0, enabling technologies, systematic literature review
Abstract
The rapid development of IoT research in various fields has promoted the evolution of manufacturing in the Industry 4.0 context. However, the growing and dispersed literature makes it difficult to see the dominant trends and open challenges. The aim of the study is to synthesize the existing IoT research in the manufacturing, by analyzing the sectoral adoption, enabling technologies and implementation objectives. The review develops a systematic understanding of the links between manufacturing sectors, IoT technologies and operational priorities to identify dominant research directions and gaps for future research. A systematic literature review was conducted according to the PRISMA guidelines, screening and analysing peer-reviewed studies along three analytical dimensions: distribution by manufacturing sector, typologies of IoT technologies and strategic objectives of implementation. The analysis identified shared adoption patterns in some manufacturing sectors, common use of sensor-based and cloud-enabled technologies, and a high emphasis on productivity, monitoring and efficiency of operations. The results reveal a significant concentration of IoT research in discrete manufacturing, as well as noticeable attention in process manufacturing, healthcare and general manufacturing, while other sectors remain less explored, indicating an uneven research focus across industries. In terms of technology, Industrial IoT and smart manufacturing solutions are the most common, followed by IoT-enabled digital twin technologies, while the combination of IoT with artificial intelligence, machine learning, and computer vision indicates a growing shift towards more adaptive and intelligent systems. A smaller portion of IoT implementations are related to sensors and monitoring applications, blockchain enabled IoT solutions and distributed architectures, while middleware and system integration appear least often. Regarding implementation objectives, efficiency enhancement is the main driver, followed by predictive maintenance, quality control and productivity enhancement, and real-time monitoring, showing a strong orientation toward improving operational performance. In summary, the synthesis implies that the IoT research in manufacturing is mainly focused on discrete manufacturing applications, operational efficiency objectives, and intelligent automation technologies. The concentration indicates a continued research focus on production optimization, while broader contexts of industrial integration are relatively underexplored.
Downloads
Download data is not yet available.
References
[1] H. Yang, S. Kumara, S. T. S. Bukkapatnam, and F. Tsung, “The internet of things for smart manufacturing: A review,” IISE Trans., vol. 51, no. 11, pp. 1190–1216, 2019, doi: 10.1080/24725854.2018.1555383.
[2] M. Asif, L. Yang, and M. Hashim, “The Role of Digital Transformation, Corporate Culture, and Leadership in Enhancing Corporate Sustainable Performance in the Manufacturing Sector of China,” 2024.
[3] E. Alfaqiyah, A. Alzubi, and H. Y. Aljuhmani, “How Industry 4.0 Technologies Enhance Supply Chain Resilience: The Interplay of Agility, Adaptability, and Customer Integration in Manufacturing Firms,” 2025.
[4] M. A. Munir, M. Mujtaba, A. Qamar, and M. Farooq, “Managing volatile markets: A dynamic capability approach to analytics-driven performance in fast-moving supply chains for sustainable development,” Sustain. Futur., vol. 10, no. December 2024, p. 101338, 2025, doi: 10.1016/j.sftr.2025.101338.
[5] M. Alquraish, “Digital Transformation, Supply Chain Resilience, and Sustainability: A Comprehensive Review with Implications for Saudi Arabian Manufacturing,” 2025.
[6] U. N. Chinedu and E. Brendan, “The Impact of Internet of Things in Manufacturing Management,” Engineering, vol. 15, no. 09, pp. 533–560, 2023, doi: 10.4236/eng.2023.159039.
[7] P. Malhotra, Y. Singh, P. Anand, D. Bangotra, P. Singh, and W. Hong, “Internet of Things: Evolution, Concerns and Security Challenges,” 2021.
[8] K. Wójcicki, M. Biegańska, B. Paliwoda, and J. Górna, “Challenges and Opportunities — A Review,” MDPI, vol. 15, no. 1806, 2022.
[9] M. Soori, B. Arezoo, and R. Dastres, “Internet of things for smart factories in industry 4.0, a review,” Internet Things Cyber-Physical Syst., vol. 3, no. April, pp. 192–204, 2023, doi: 10.1016/j.iotcps.2023.04.006.
[10] G. Aggarwal, H. Chaukse, and A. Sualeh, “IoT Implementation in Manufacturing using Data Analysis and Data Management,” vol. 3075, no. 1, pp. 371–375, 2019, doi: 10.35940/ijitee.A4137.119119.
[11] Y. M. Al-Naggar, N. Jamil, M. F. Hassan, and A. R. Yusoff, “Condition monitoring based on IoT for predictive maintenance of CNC machines,” Procedia CIRP, vol. 102, no. March, pp. 314–318, 2021, doi: 10.1016/j.procir.2021.09.054.
[12] L. Dhirani, E. Armstrong, and T. Newe, “Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap,” vol. 21, no. 11, pp. 1–30, 2021.
[13] M. Saqlain, M. Piao, Y. Shim, and J. Y. Lee, “Framework of an IoT-based Industrial Data Management for Smart Manufacturing,” J. Sens. Actuator Networks, vol. 8, no. 2, 2019, doi: 10.3390/jsan8020025.
[14] T. Kalsoom et al., “Impact of IoT on manufacturing industry 4.0: A new triangular systematic review,” Sustain., vol. 13, no. 22, pp. 1–22, 2021, doi: 10.3390/su132212506.
[15] H. Alrashede, F. Eassa, A. M. Ali, F. Albalwy, and H. Aljihani, “A Blockchain-Based Security Framework for East-West Interface of SDN,” 2024.
[16] Y. Sanjalawe, S. Fraihat, S. Al-e, and S. N. Makhadmeh, “A review of artificial intelligence-based intrusion detection in industrial internet of things,” 2026.
[17] R. J. Raimundo and A. T. Rosário, “Cybersecurity in the Internet of Things in Industrial Management,” Appl. Sci., 2022.
[18] M. Sujatha et al., “IoT and Machine Learning-Based Smart Automation System for Industry 4.0 Using Robotics and Sensors,” J. Nanomater., vol. 2022, 2022, doi: 10.1155/2022/6807585.
[19] S. Afrin et al., “Computers in Industry Industrial Internet of Things: Implementations, challenges, and potential solutions across various industries,” Comput. Ind., vol. 170, no. July 2024, p. 104317, 2025, doi: 10.1016/j.compind.2025.104317.
[20] R. Y. Zhong, L. Wang, and X. Xu, “An IoT-enabled Real-time Machine Status Monitoring Approach for Cloud Manufacturing,” Procedia CIRP, vol. 63, pp. 709–714, 2017, doi: 10.1016/j.procir.2017.03.349.
[21] M. S. Al-Rakhami and M. Al-Mashari, “ProChain: Provenance-Aware Traceability Framework for IoT-Based Supply Chain Systems,” IEEE Access, vol. 10, pp. 3631–3642, 2022, doi: 10.1109/ACCESS.2021.3135371.
[22] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” Syst. Rev., vol. 10, no. 1, pp. 1–11, 2021, doi: 10.1186/s13643-021-01626-4.
[23] N. R. Haddaway et al., “A suggested data structure for transparent and repeatable reporting of bibliographic searching,” Campbell Syst. Rev., vol. 18, no. 4, pp. 1–12, 2022, doi: 10.1002/cl2.1288.
[24] B. Maqbool and S. Herold, “Potential effectiveness and efficiency issues in usability evaluation within digital health: A systematic literature review,” J. Syst. Softw., vol. 208, no. September 2022, 2024, doi: 10.1016/j.jss.2023.111881.
[25] K. Gregory, P. Groth, H. Cousijn, A. Scharnhorst, and S. Wyatt, “Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines,” J. Assoc. Inf. Sci. Technol., vol. 70, no. 5, pp. 419–432, 2019, doi: 10.1002/asi.24165.
[26] L. Schmidt, B. K. Olorisade, L. A. McGuinness, J. Thomas, and J. P. T. Higgins, “Data extraction methods for systematic review (semi)automation: A living systematic review,” F1000Research, vol. 10, p. 401, 2021, doi: 10.12688/f1000research.51117.1.
[27] M. Javaid, Abid Haleem, R. Pratap Singh, S. Rab, and R. Suman, “Upgrading the manufacturing sector via applications of Industrial Internet of Things (IIoT),” Sensors Int., vol. 2, no. August, p. 100129, 2021, doi: 10.1016/j.sintl.2021.100129.
[28] M. Shahin, F. Frank Chen, H. Bouzary, and A. Hosseinzadeh, “Deploying Convolutional Neural Network to reduce waste in production system,” Manuf. Lett., vol. 35, pp. 1187–1195, 2023, doi: 10.1016/j.mfglet.2023.08.127.
[29] D. Stock, D. Schel, and T. Bauernhansl, “Middleware-based cyber-physical production system modeling for operators,” Procedia Manuf., vol. 42, no. 2019, pp. 111–118, 2020, doi: 10.1016/j.promfg.2020.02.031.
[30] F. Piedade, M. Baptista, and P. Chaves, “In2DIG - Implementation of a digital manufacturing system in a production cell of the metal mold industry: From planning to action,” Procedia Manuf., vol. 42, no. 2019, pp. 104–110, 2020, doi: 10.1016/j.promfg.2020.02.030.
[31] F. Henriksson and K. Johansen, “Integrated Product and Production Research on Introducing Internet of Things in Swedish Wood Industry Products,” Procedia Manuf., vol. 25, pp. 10–16, 2018, doi: 10.1016/j.promfg.2018.06.051.
[32] C. A. P. Rocha, E. Rauch, T. Vaimel, M. A. R. Garcia, and R. Vidoni, “Implementation of a Vision-Based Worker Assistance System in Assembly: A Case Study,” Procedia CIRP, vol. 96, no. March, pp. 295–300, 2021, doi: 10.1016/j.procir.2021.01.090.
[33] N. Chuenmee, N. Phothi, K. Chamniprasart, S. Khaengkarn, and J. Srisertpol, “Machine learning for predicting resistance spot weld quality in automotive manufacturing,” Results Eng., vol. 25, no. December 2024, p. 103570, 2025, doi: 10.1016/j.rineng.2024.103570.
[34] P. Dobra and J. Jósvai, “Enhance of OEE by hybrid analysis at the automotive semi-automatic assembly lines,” Procedia Manuf., vol. 54, pp. 184–190, 2021, doi: 10.1016/j.promfg.2021.07.028.
[35] B. Puniani and S. Abdoli, “Development of Capability for Integrated Smart Production-Spare Part Warehouse System for SMEs in Fast-Moving Consumer Goods sector,” Procedia CIRP, vol. 136, pp. 159–164, 2025, doi: 10.1016/j.procir.2025.08.029.
[36] F. Chiacchio, L. Compagno, D. D’Urso, L. Velardita, and P. Sandner, “A decentralized application for the traceability process in the pharma industry,” Procedia Manuf., vol. 42, no. 2019, pp. 362–369, 2020, doi: 10.1016/j.promfg.2020.02.063.
[37] D. Mueller and F. Vogelsang, “Towards smart manufacturing logistics: A case study of potentials of smart label data in electronics manufacturing,” Procedia CIRP, vol. 104, no. March, pp. 1741–1746, 2021, doi: 10.1016/j.procir.2021.11.293.
[38] K. Jackson, K. Efthymiou, and J. Borton, “Digital Manufacturing and Flexible Assembly Technologies for Reconfigurable Aerospace Production Systems,” Procedia CIRP, vol. 52, pp. 274–279, 2016, doi: 10.1016/j.procir.2016.07.054.
[39] G. Koulinas, P. Paraschos, and D. Koulouriotis, “A machine learning-based framework for data mining and optimization of a production system,” Procedia Manuf., vol. 55, no. C, pp. 431–438, 2021, doi: 10.1016/j.promfg.2021.10.059.
[40] S. Salman, S. M. Morshed, M. R. Karim, R. Rahman, S. Hasanat, and A. Ahsan, “Exploring lean manufacturing drivers for enhancing circular economy performance in the pharmaceutical industry: a Bayesian best–worst approach,” Int. J. Ind. Eng. Oper. Manag., vol. 7, no. 1, pp. 68–96, 2025, doi: 10.1108/IJIEOM-10-2023-0074.
[41] M. R. Hoque, S. R. Tushar, M. A. S. Shafil, M. M. Bappy, and M. G. S. Rayhan, “An integrated VSM 4.0 and interval-valued q-Rung orthopair fuzzy approach to sustainable process development in the apparel manufacturing industry,” Int. J. Ind. Eng. Oper. Manag., no. September, 2025, doi: 10.1108/IJIEOM-12-2024-0094.
[42] S. Gupta et al., “From failure to success: a framework for successful deployment of Industry 4.0 principles in the aerospace industry,” Int. J. Ind. Eng. Oper. Manag., vol. 6, no. 4, pp. 277–298, 2024, doi: 10.1108/IJIEOM-04-2023-0042.
[43] R. F. Pitzalis, A. Giordano, A. Di Spigno, A. Cowell, O. Niculita, and G. Berselli, “Application of augmented reality-based digital twin approaches: a case study to industrial equipment,” Int. J. Adv. Manuf. Technol., vol. 138, no. 7, pp. 3747–3763, 2025, doi: 10.1007/s00170-025-15755-w.
[44] C. S. Chen and P. Y. Pan, “Applied Internet of Things to Analyze Vibration, Workpiece Roughness, and Tool Wear: Case Study of Successive Milling,” Processes, vol. 13, no. 4, pp. 1–20, 2025, doi: 10.3390/pr13040978.
[45] G. Luisi, V. Di Pasquale, M. C. Pietronudo, S. Riemma, and M. Ferretti, “A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process,” Appl. Sci., vol. 13, no. 22, 2023, doi: 10.3390/app132212145.
[46] S. Jagtap, C. Bhatt, J. Thik, and S. Rahimifard, “Monitoring potato waste in food manufacturing using image processing and internet of things approach,” Sustain., vol. 11, no. 11, 2019, doi: 10.3390/su11113173.
[47] F. Chiacchio, D. D’Urso, L. M. Oliveri, A. Spitaleri, C. Spampinato, and D. Giordano, “A Non‐Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain,” Appl. Sci., vol. 12, no. 8, 2022, doi: 10.3390/app12084019.
[48] Z. Lv, H. Lv, and M. Fridenfalk, “Digital Twins in the Marine Industry,” Electron., vol. 12, no. 9, pp. 1–26, 2023, doi: 10.3390/electronics12092025.
[49] C. Verdouw and J. W. Kruize, “Digital twins in farm management: illustrations from the FIWARE accelerators SmartAgriFood and Fractals,” 7th Asian-Australasian Conf. Precis. Agric., pp. 1–5, 2017, doi: 10.5281/zenodo.893662.
[50] T. Erol, A. F. Mendi, and D. Dogan, “The Digital Twin Revolution in Healthcare,” 4th Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2020 - Proc., no. October, 2020, doi: 10.1109/ISMSIT50672.2020.9255249.
[51] D. Piromalis and A. Kantaros, “Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence,” Appl. Syst. Innov., vol. 5, no. 4, pp. 1–12, 2022, doi: 10.3390/asi5040065.
[52] T. R. Wanasinghe et al., “Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges,” IEEE Access, vol. 8, pp. 104175–104197, 2020, doi: 10.1109/ACCESS.2020.2998723.
[53] L. Li, S. Aslam, A. Wileman, and S. Perinpanayagam, “Digital Twin in Aerospace Industry: A Gentle Introduction,” IEEE Access, vol. 10, pp. 9543–9562, 2022, doi: 10.1109/ACCESS.2021.3136458.
[54]J. Monteiro, J. Barata, M. Veloso, L. Veloso, and J. Nunes, “Towards sustainable digital twins for vertical farming,” 2018 13th Int. Conf. Digit. Inf. Manag. ICDIM 2018, no. Icdim, pp. 234–239, 2018, doi: 10.1109/ICDIM.2018.8847169.
[55] C. Cronin, A. Conway, and J. Walsh, “Flexible manufacturing systems using IIoT in the automotive sector,” Procedia Manuf., vol. 38, no. 2019, pp. 1652–1659, 2019, doi: 10.1016/j.promfg.2020.01.119.
[56] N. Intalar, K. Chumnumporn, C. Jeenanunta, and A. Tunpan, “Towards Industry 4.0: Digital transformation of traditional safety shoes manufacturer in Thailand with a development of production tracking system,” Eng. Manag. Prod. Serv., vol. 13, no. 4, pp. 79–94, 2021, doi: 10.2478/emj-2021-0033.
[57] Orive, A. Agirre, H.-L. Truong, I. Sarachaga, and M. Marcos, “Quality of Service Aware Orchestration for Cloud–Edge Continuum Applications,” 2022.
[58] E. Jovicic, D. Primorac, M. Cupic, and A. Jovic, “Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review,” IEEE Access, vol. 11, no. June, pp. 73505–73520, 2023, doi: 10.1109/ACCESS.2023.3295113.
[59] P. Tanuska, L. Spendla, M. Kebisek, D. Rastislav, and M. Stremy, “Maintenance in Compliance with Industry 4.0,” Sensors 2021, vol. 21, p. 2376, 2021.
[60] J. Wang, L. Bi, L. Wang, M. Jia, and D. Mao, “A Mining Technology Collaboration Platform Theory and Its Product Development and Application to Support China’s Digital Mine Construction,” Appl. Sci., vol. 9, no. 24, pp. 1–33, 2019, doi: 10.3390/app9245373.
[61] T. Tanizaki and R. Yamashita, “Application of Metaheuristics to Packing Formation Support Systems of Pre-Cut Lumber Factory,” Int. J. Autom. Technol., vol. 16, no. 3, pp. 269–279, 2022, doi: 10.20965/ijat.2022.p0269.
[62] Y. Saif et al., “Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques,” Appl. Sci., vol. 13, no. 20, 2023, doi: 10.3390/app132011419.
[63] Y. J. Chen, Y. H. Yeh, and J. F. Yang, “A System for the Real-Time Detection of the U-Shaped Steel Bar Straightness on a Production Line,” Sensors, vol. 25, no. 13, pp. 1–21, 2025, doi: 10.3390/s25133972.
[64] A. Eskandari, and M. Aghaei, “Photovoltaic fault detection and classification: Reconsideration of classic machine learning and dataset shrinkage techniques for simplification,” Results Eng., vol. 27, no. May, p. 106356, 2025, doi: 10.1016/j.rineng.2025.106356.
[65] G. Simion, A. Filipescu, D. Ionescu, and A. Filipescu, “Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach,” Sensors, vol. 25, no. 2, pp. 1–36, 2025, doi: 10.3390/s25020591.
[66] Kampa, “Modeling and Simulation of a Digital Twin of a Production System for Industry 4.0 with Work-in-Process Synchronization,” Appl. Sci., vol. 13, no. 22, 2023, doi: 10.3390/app132212261.
[67] V. Liubčuk, V. Radziukynas, G. Kairaitis, and D. Naujokaitis, “Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies,” Inventions, vol. 10, no. 2, 2025, doi: 10.3390/inventions10020030.
[68] C. O’Donovan, I. Popov, G. Todeschini, and C. Giannetti, “Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation,” Int. J. Adv. Manuf. Technol., vol. 126, no. 3–4, pp. 1397–1416, 2023, doi: 10.1007/s00170-023-11151-4.
[69] S. Gadhave et al., “Enhancing agricultural efficiency in India with IoT based smart boats,” Sigma J. Eng. Nat. Sci., vol. 43, no. 2, pp. 570–581, 2025, doi: 10.14744/sigma.2025.00045.
[70] M. Staude, P. Brożek, E. Kostecka, D. Tarnapowicz, and J. Wysocki, “Remote Water Quality Monitoring System for Use in Fairway Applications,” Appl. Sci., vol. 14, no. 23, 2024, doi: 10.3390/app142311406.
[71] H. H. Hosamo, H. K. Nielsen, D. Kraniotis, P. R. Svennevig, and K. Svidt, “Improving building occupant comfort through a digital twin approach: A Bayesian network model and predictive maintenance method,” Energy Build., vol. 288, p. 112992, 2023, doi: 10.1016/j.enbuild.2023.112992.
[72] H. Hashim et al., “Development of Machine Down-Time Monitoring System for Production Line Efficiency Evaluation,” J. Adv. Res. Appl. Mech., vol. 133, no. 1, pp. 1–11, 2025, doi: 10.37934/aram.133.1.111.
[2] M. Asif, L. Yang, and M. Hashim, “The Role of Digital Transformation, Corporate Culture, and Leadership in Enhancing Corporate Sustainable Performance in the Manufacturing Sector of China,” 2024.
[3] E. Alfaqiyah, A. Alzubi, and H. Y. Aljuhmani, “How Industry 4.0 Technologies Enhance Supply Chain Resilience: The Interplay of Agility, Adaptability, and Customer Integration in Manufacturing Firms,” 2025.
[4] M. A. Munir, M. Mujtaba, A. Qamar, and M. Farooq, “Managing volatile markets: A dynamic capability approach to analytics-driven performance in fast-moving supply chains for sustainable development,” Sustain. Futur., vol. 10, no. December 2024, p. 101338, 2025, doi: 10.1016/j.sftr.2025.101338.
[5] M. Alquraish, “Digital Transformation, Supply Chain Resilience, and Sustainability: A Comprehensive Review with Implications for Saudi Arabian Manufacturing,” 2025.
[6] U. N. Chinedu and E. Brendan, “The Impact of Internet of Things in Manufacturing Management,” Engineering, vol. 15, no. 09, pp. 533–560, 2023, doi: 10.4236/eng.2023.159039.
[7] P. Malhotra, Y. Singh, P. Anand, D. Bangotra, P. Singh, and W. Hong, “Internet of Things: Evolution, Concerns and Security Challenges,” 2021.
[8] K. Wójcicki, M. Biegańska, B. Paliwoda, and J. Górna, “Challenges and Opportunities — A Review,” MDPI, vol. 15, no. 1806, 2022.
[9] M. Soori, B. Arezoo, and R. Dastres, “Internet of things for smart factories in industry 4.0, a review,” Internet Things Cyber-Physical Syst., vol. 3, no. April, pp. 192–204, 2023, doi: 10.1016/j.iotcps.2023.04.006.
[10] G. Aggarwal, H. Chaukse, and A. Sualeh, “IoT Implementation in Manufacturing using Data Analysis and Data Management,” vol. 3075, no. 1, pp. 371–375, 2019, doi: 10.35940/ijitee.A4137.119119.
[11] Y. M. Al-Naggar, N. Jamil, M. F. Hassan, and A. R. Yusoff, “Condition monitoring based on IoT for predictive maintenance of CNC machines,” Procedia CIRP, vol. 102, no. March, pp. 314–318, 2021, doi: 10.1016/j.procir.2021.09.054.
[12] L. Dhirani, E. Armstrong, and T. Newe, “Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap,” vol. 21, no. 11, pp. 1–30, 2021.
[13] M. Saqlain, M. Piao, Y. Shim, and J. Y. Lee, “Framework of an IoT-based Industrial Data Management for Smart Manufacturing,” J. Sens. Actuator Networks, vol. 8, no. 2, 2019, doi: 10.3390/jsan8020025.
[14] T. Kalsoom et al., “Impact of IoT on manufacturing industry 4.0: A new triangular systematic review,” Sustain., vol. 13, no. 22, pp. 1–22, 2021, doi: 10.3390/su132212506.
[15] H. Alrashede, F. Eassa, A. M. Ali, F. Albalwy, and H. Aljihani, “A Blockchain-Based Security Framework for East-West Interface of SDN,” 2024.
[16] Y. Sanjalawe, S. Fraihat, S. Al-e, and S. N. Makhadmeh, “A review of artificial intelligence-based intrusion detection in industrial internet of things,” 2026.
[17] R. J. Raimundo and A. T. Rosário, “Cybersecurity in the Internet of Things in Industrial Management,” Appl. Sci., 2022.
[18] M. Sujatha et al., “IoT and Machine Learning-Based Smart Automation System for Industry 4.0 Using Robotics and Sensors,” J. Nanomater., vol. 2022, 2022, doi: 10.1155/2022/6807585.
[19] S. Afrin et al., “Computers in Industry Industrial Internet of Things: Implementations, challenges, and potential solutions across various industries,” Comput. Ind., vol. 170, no. July 2024, p. 104317, 2025, doi: 10.1016/j.compind.2025.104317.
[20] R. Y. Zhong, L. Wang, and X. Xu, “An IoT-enabled Real-time Machine Status Monitoring Approach for Cloud Manufacturing,” Procedia CIRP, vol. 63, pp. 709–714, 2017, doi: 10.1016/j.procir.2017.03.349.
[21] M. S. Al-Rakhami and M. Al-Mashari, “ProChain: Provenance-Aware Traceability Framework for IoT-Based Supply Chain Systems,” IEEE Access, vol. 10, pp. 3631–3642, 2022, doi: 10.1109/ACCESS.2021.3135371.
[22] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” Syst. Rev., vol. 10, no. 1, pp. 1–11, 2021, doi: 10.1186/s13643-021-01626-4.
[23] N. R. Haddaway et al., “A suggested data structure for transparent and repeatable reporting of bibliographic searching,” Campbell Syst. Rev., vol. 18, no. 4, pp. 1–12, 2022, doi: 10.1002/cl2.1288.
[24] B. Maqbool and S. Herold, “Potential effectiveness and efficiency issues in usability evaluation within digital health: A systematic literature review,” J. Syst. Softw., vol. 208, no. September 2022, 2024, doi: 10.1016/j.jss.2023.111881.
[25] K. Gregory, P. Groth, H. Cousijn, A. Scharnhorst, and S. Wyatt, “Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines,” J. Assoc. Inf. Sci. Technol., vol. 70, no. 5, pp. 419–432, 2019, doi: 10.1002/asi.24165.
[26] L. Schmidt, B. K. Olorisade, L. A. McGuinness, J. Thomas, and J. P. T. Higgins, “Data extraction methods for systematic review (semi)automation: A living systematic review,” F1000Research, vol. 10, p. 401, 2021, doi: 10.12688/f1000research.51117.1.
[27] M. Javaid, Abid Haleem, R. Pratap Singh, S. Rab, and R. Suman, “Upgrading the manufacturing sector via applications of Industrial Internet of Things (IIoT),” Sensors Int., vol. 2, no. August, p. 100129, 2021, doi: 10.1016/j.sintl.2021.100129.
[28] M. Shahin, F. Frank Chen, H. Bouzary, and A. Hosseinzadeh, “Deploying Convolutional Neural Network to reduce waste in production system,” Manuf. Lett., vol. 35, pp. 1187–1195, 2023, doi: 10.1016/j.mfglet.2023.08.127.
[29] D. Stock, D. Schel, and T. Bauernhansl, “Middleware-based cyber-physical production system modeling for operators,” Procedia Manuf., vol. 42, no. 2019, pp. 111–118, 2020, doi: 10.1016/j.promfg.2020.02.031.
[30] F. Piedade, M. Baptista, and P. Chaves, “In2DIG - Implementation of a digital manufacturing system in a production cell of the metal mold industry: From planning to action,” Procedia Manuf., vol. 42, no. 2019, pp. 104–110, 2020, doi: 10.1016/j.promfg.2020.02.030.
[31] F. Henriksson and K. Johansen, “Integrated Product and Production Research on Introducing Internet of Things in Swedish Wood Industry Products,” Procedia Manuf., vol. 25, pp. 10–16, 2018, doi: 10.1016/j.promfg.2018.06.051.
[32] C. A. P. Rocha, E. Rauch, T. Vaimel, M. A. R. Garcia, and R. Vidoni, “Implementation of a Vision-Based Worker Assistance System in Assembly: A Case Study,” Procedia CIRP, vol. 96, no. March, pp. 295–300, 2021, doi: 10.1016/j.procir.2021.01.090.
[33] N. Chuenmee, N. Phothi, K. Chamniprasart, S. Khaengkarn, and J. Srisertpol, “Machine learning for predicting resistance spot weld quality in automotive manufacturing,” Results Eng., vol. 25, no. December 2024, p. 103570, 2025, doi: 10.1016/j.rineng.2024.103570.
[34] P. Dobra and J. Jósvai, “Enhance of OEE by hybrid analysis at the automotive semi-automatic assembly lines,” Procedia Manuf., vol. 54, pp. 184–190, 2021, doi: 10.1016/j.promfg.2021.07.028.
[35] B. Puniani and S. Abdoli, “Development of Capability for Integrated Smart Production-Spare Part Warehouse System for SMEs in Fast-Moving Consumer Goods sector,” Procedia CIRP, vol. 136, pp. 159–164, 2025, doi: 10.1016/j.procir.2025.08.029.
[36] F. Chiacchio, L. Compagno, D. D’Urso, L. Velardita, and P. Sandner, “A decentralized application for the traceability process in the pharma industry,” Procedia Manuf., vol. 42, no. 2019, pp. 362–369, 2020, doi: 10.1016/j.promfg.2020.02.063.
[37] D. Mueller and F. Vogelsang, “Towards smart manufacturing logistics: A case study of potentials of smart label data in electronics manufacturing,” Procedia CIRP, vol. 104, no. March, pp. 1741–1746, 2021, doi: 10.1016/j.procir.2021.11.293.
[38] K. Jackson, K. Efthymiou, and J. Borton, “Digital Manufacturing and Flexible Assembly Technologies for Reconfigurable Aerospace Production Systems,” Procedia CIRP, vol. 52, pp. 274–279, 2016, doi: 10.1016/j.procir.2016.07.054.
[39] G. Koulinas, P. Paraschos, and D. Koulouriotis, “A machine learning-based framework for data mining and optimization of a production system,” Procedia Manuf., vol. 55, no. C, pp. 431–438, 2021, doi: 10.1016/j.promfg.2021.10.059.
[40] S. Salman, S. M. Morshed, M. R. Karim, R. Rahman, S. Hasanat, and A. Ahsan, “Exploring lean manufacturing drivers for enhancing circular economy performance in the pharmaceutical industry: a Bayesian best–worst approach,” Int. J. Ind. Eng. Oper. Manag., vol. 7, no. 1, pp. 68–96, 2025, doi: 10.1108/IJIEOM-10-2023-0074.
[41] M. R. Hoque, S. R. Tushar, M. A. S. Shafil, M. M. Bappy, and M. G. S. Rayhan, “An integrated VSM 4.0 and interval-valued q-Rung orthopair fuzzy approach to sustainable process development in the apparel manufacturing industry,” Int. J. Ind. Eng. Oper. Manag., no. September, 2025, doi: 10.1108/IJIEOM-12-2024-0094.
[42] S. Gupta et al., “From failure to success: a framework for successful deployment of Industry 4.0 principles in the aerospace industry,” Int. J. Ind. Eng. Oper. Manag., vol. 6, no. 4, pp. 277–298, 2024, doi: 10.1108/IJIEOM-04-2023-0042.
[43] R. F. Pitzalis, A. Giordano, A. Di Spigno, A. Cowell, O. Niculita, and G. Berselli, “Application of augmented reality-based digital twin approaches: a case study to industrial equipment,” Int. J. Adv. Manuf. Technol., vol. 138, no. 7, pp. 3747–3763, 2025, doi: 10.1007/s00170-025-15755-w.
[44] C. S. Chen and P. Y. Pan, “Applied Internet of Things to Analyze Vibration, Workpiece Roughness, and Tool Wear: Case Study of Successive Milling,” Processes, vol. 13, no. 4, pp. 1–20, 2025, doi: 10.3390/pr13040978.
[45] G. Luisi, V. Di Pasquale, M. C. Pietronudo, S. Riemma, and M. Ferretti, “A Hybrid Architectural Model for Monitoring Production Performance in the Plastic Injection Molding Process,” Appl. Sci., vol. 13, no. 22, 2023, doi: 10.3390/app132212145.
[46] S. Jagtap, C. Bhatt, J. Thik, and S. Rahimifard, “Monitoring potato waste in food manufacturing using image processing and internet of things approach,” Sustain., vol. 11, no. 11, 2019, doi: 10.3390/su11113173.
[47] F. Chiacchio, D. D’Urso, L. M. Oliveri, A. Spitaleri, C. Spampinato, and D. Giordano, “A Non‐Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain,” Appl. Sci., vol. 12, no. 8, 2022, doi: 10.3390/app12084019.
[48] Z. Lv, H. Lv, and M. Fridenfalk, “Digital Twins in the Marine Industry,” Electron., vol. 12, no. 9, pp. 1–26, 2023, doi: 10.3390/electronics12092025.
[49] C. Verdouw and J. W. Kruize, “Digital twins in farm management: illustrations from the FIWARE accelerators SmartAgriFood and Fractals,” 7th Asian-Australasian Conf. Precis. Agric., pp. 1–5, 2017, doi: 10.5281/zenodo.893662.
[50] T. Erol, A. F. Mendi, and D. Dogan, “The Digital Twin Revolution in Healthcare,” 4th Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2020 - Proc., no. October, 2020, doi: 10.1109/ISMSIT50672.2020.9255249.
[51] D. Piromalis and A. Kantaros, “Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence,” Appl. Syst. Innov., vol. 5, no. 4, pp. 1–12, 2022, doi: 10.3390/asi5040065.
[52] T. R. Wanasinghe et al., “Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges,” IEEE Access, vol. 8, pp. 104175–104197, 2020, doi: 10.1109/ACCESS.2020.2998723.
[53] L. Li, S. Aslam, A. Wileman, and S. Perinpanayagam, “Digital Twin in Aerospace Industry: A Gentle Introduction,” IEEE Access, vol. 10, pp. 9543–9562, 2022, doi: 10.1109/ACCESS.2021.3136458.
[54]J. Monteiro, J. Barata, M. Veloso, L. Veloso, and J. Nunes, “Towards sustainable digital twins for vertical farming,” 2018 13th Int. Conf. Digit. Inf. Manag. ICDIM 2018, no. Icdim, pp. 234–239, 2018, doi: 10.1109/ICDIM.2018.8847169.
[55] C. Cronin, A. Conway, and J. Walsh, “Flexible manufacturing systems using IIoT in the automotive sector,” Procedia Manuf., vol. 38, no. 2019, pp. 1652–1659, 2019, doi: 10.1016/j.promfg.2020.01.119.
[56] N. Intalar, K. Chumnumporn, C. Jeenanunta, and A. Tunpan, “Towards Industry 4.0: Digital transformation of traditional safety shoes manufacturer in Thailand with a development of production tracking system,” Eng. Manag. Prod. Serv., vol. 13, no. 4, pp. 79–94, 2021, doi: 10.2478/emj-2021-0033.
[57] Orive, A. Agirre, H.-L. Truong, I. Sarachaga, and M. Marcos, “Quality of Service Aware Orchestration for Cloud–Edge Continuum Applications,” 2022.
[58] E. Jovicic, D. Primorac, M. Cupic, and A. Jovic, “Publicly Available Datasets for Predictive Maintenance in the Energy Sector: A Review,” IEEE Access, vol. 11, no. June, pp. 73505–73520, 2023, doi: 10.1109/ACCESS.2023.3295113.
[59] P. Tanuska, L. Spendla, M. Kebisek, D. Rastislav, and M. Stremy, “Maintenance in Compliance with Industry 4.0,” Sensors 2021, vol. 21, p. 2376, 2021.
[60] J. Wang, L. Bi, L. Wang, M. Jia, and D. Mao, “A Mining Technology Collaboration Platform Theory and Its Product Development and Application to Support China’s Digital Mine Construction,” Appl. Sci., vol. 9, no. 24, pp. 1–33, 2019, doi: 10.3390/app9245373.
[61] T. Tanizaki and R. Yamashita, “Application of Metaheuristics to Packing Formation Support Systems of Pre-Cut Lumber Factory,” Int. J. Autom. Technol., vol. 16, no. 3, pp. 269–279, 2022, doi: 10.20965/ijat.2022.p0269.
[62] Y. Saif et al., “Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques,” Appl. Sci., vol. 13, no. 20, 2023, doi: 10.3390/app132011419.
[63] Y. J. Chen, Y. H. Yeh, and J. F. Yang, “A System for the Real-Time Detection of the U-Shaped Steel Bar Straightness on a Production Line,” Sensors, vol. 25, no. 13, pp. 1–21, 2025, doi: 10.3390/s25133972.
[64] A. Eskandari, and M. Aghaei, “Photovoltaic fault detection and classification: Reconsideration of classic machine learning and dataset shrinkage techniques for simplification,” Results Eng., vol. 27, no. May, p. 106356, 2025, doi: 10.1016/j.rineng.2025.106356.
[65] G. Simion, A. Filipescu, D. Ionescu, and A. Filipescu, “Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach,” Sensors, vol. 25, no. 2, pp. 1–36, 2025, doi: 10.3390/s25020591.
[66] Kampa, “Modeling and Simulation of a Digital Twin of a Production System for Industry 4.0 with Work-in-Process Synchronization,” Appl. Sci., vol. 13, no. 22, 2023, doi: 10.3390/app132212261.
[67] V. Liubčuk, V. Radziukynas, G. Kairaitis, and D. Naujokaitis, “Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies,” Inventions, vol. 10, no. 2, 2025, doi: 10.3390/inventions10020030.
[68] C. O’Donovan, I. Popov, G. Todeschini, and C. Giannetti, “Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation,” Int. J. Adv. Manuf. Technol., vol. 126, no. 3–4, pp. 1397–1416, 2023, doi: 10.1007/s00170-023-11151-4.
[69] S. Gadhave et al., “Enhancing agricultural efficiency in India with IoT based smart boats,” Sigma J. Eng. Nat. Sci., vol. 43, no. 2, pp. 570–581, 2025, doi: 10.14744/sigma.2025.00045.
[70] M. Staude, P. Brożek, E. Kostecka, D. Tarnapowicz, and J. Wysocki, “Remote Water Quality Monitoring System for Use in Fairway Applications,” Appl. Sci., vol. 14, no. 23, 2024, doi: 10.3390/app142311406.
[71] H. H. Hosamo, H. K. Nielsen, D. Kraniotis, P. R. Svennevig, and K. Svidt, “Improving building occupant comfort through a digital twin approach: A Bayesian network model and predictive maintenance method,” Energy Build., vol. 288, p. 112992, 2023, doi: 10.1016/j.enbuild.2023.112992.
[72] H. Hashim et al., “Development of Machine Down-Time Monitoring System for Production Line Efficiency Evaluation,” J. Adv. Res. Appl. Mech., vol. 133, no. 1, pp. 1–11, 2025, doi: 10.37934/aram.133.1.111.
Article Sidebar
Published
Jun 30, 2026
Article Details
How to Cite
[1]
R. D. E. Putra, “Impact of IoT Technology Implementation in the Manufacturing Sector: A Systematic Literature Review”, J. Optimasi Sist. Ind., vol. 25, no. 1, pp. 94–119, Jun. 2026.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Ownership
- Authors who publish their articles in Jurnal Optimasi Sistem Industri (JOSI) retain full copyright of their work.
- JOSI does not require authors to transfer their copyright to the journal or its publisher, Universitas Andalas. The authors grant JOSI a license for first publication.
All articles published in Jurnal Optimasi Sistem Industri (JOSI) are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
This means that users are free to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material.
Under the following terms:
- Attribution (BY): You must give appropriate credit to the author(s) and JOSI as the original source of publication (including the article title, author(s) names, journal name, volume, issue, page numbers, year, and DOI), provide a link to the CC BY-NC-SA 4.0 license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial (NC): You may not use the material for commercial purposes.
- ShareAlike (SA): If you remix, transform, or build upon the material, you must distribute your contributions under the same CC BY-NC-SA 4.0 license as the original.
A link to the full license text should also be provided: https://creativecommons.org/licenses/by-nc-sa/4.0/
Author Biographies
M. Arif Munanda, Universitas Bengkulu
Dept. of Mechanical Engineering
Muhamad Yasin, Universitas Bengkulu
Dept. of Mechanical Engineering
