Big Data Analytics by Cloud Computing in Industry 4.0: A Review

Authors: Mohsen Soori, Behrooz Arezoo

Abstract:

In the context of Industry 4.0, cloud computing offers the scalability, flexibility, and wide range of services required to facilitate big data analytics. This enables enterprises to extract meaningful insights from the vast amounts of data produced by intelligent and networked production processes. Industry 4.0 demands real-time decision-making as cloud-based analytics enable quick processing of streaming data for immediate insights. The combination of big data analytics and cloud computing is driving the digital age in order to expand the processing power. Organizations can expand their computer capabilities according to the amount of data and processing demands thanks to cloud platforms. Cloud computing increases productivity and enables predictive maintenance by analyzing equipment data in real-time and reducing downtime. By evaluating data from several sources, cloud-based analytics enhance supply chain operations and facilitate better inventory control and logistics. Furthermore, real-time processing at the point of data production is made possible by the developing combination of edge computing and cloud analytics, which lowers latency. The present assessment underscores the revolutionary effect of merging big data analytics and cloud computing within the framework of Industry 4.0, stressing the benefits, obstacles, applications, and forthcoming patterns in this ever-evolving domain. The goal of this in-depth analysis is to further our knowledge of how important it will be for Industry 4.0 to integrate Big Data analytics with cloud computing.

Keywords: Big Data Analytics, Cloud Computing, Industry 4.0, Manufacturing management.

References:

[1] M. Azadi, Z. Moghaddas, T. Cheng, and R. Farzipoor Saen, Assessing the sustainability of cloud computing service providers for Industry 4.0: a state-of-the-art analytical approach, International Journal of Production Research, 2023, 61(12), pp. 4196-4213.
[2] S. Khanra, A. Dhir, A. N. Islam, and M. Mäntymäki, Big data analytics in healthcare: a systematic literature review, Enterprise Information Systems, 2020, 14(7), pp. 878-912.
[3] M. Y. Santos, J. Oliveira e Sá, C. Costa, J. Galvão, C. Andrade, B. Martinho, F. V. Lima, and E. Costa. A big data analytics architecture for industry 4.0. in Recent Advances in Information Systems and Technologies: Volume 2 5. 2017. Springer.
[4] R. Bonnard, M. D. S. Arantes, R. Lorbieski, K. M. M. Vieira, and M. C. Nunes, Big data/analytics platform for Industry 4.0 implementation in advanced manufacturing context, The International Journal of Advanced Manufacturing Technology, 2021, 117(5-6), pp. 1959-1973.
[5] A. Petrova, Cloud Computing in the Age of Big Data: Storage, Analytics, and Scalability, Advances in Computer Sciences, 2023, 6(1).
[6] R. Sahal, J. G. Breslin, and M. I. Ali, Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case, Journal of manufacturing systems, 2020, 54, pp. 138-151.
[7] G. Aceto, V. Persico, and A. Pescapé, Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0, Journal of Industrial Information Integration, 2020, 18, pp. 100129.
[8] S. Sahoo, Big data analytics in manufacturing: a bibliometric analysis of research in the field of business management, International Journal of Production Research, 2022, 60(22), pp. 6793-6821.
[9] A. H. A. Al-Jumaili, R. C. Muniyandi, M. K. Hasan, J. K. S. Paw, and M. J. Singh, Big data analytics using cloud computing based frameworks for power management systems: Status, constraints, and future recommendations, Sensors, 2023, 23(6), pp. 2952.
[10] N. Velásquez, E. C. Estévez, and P. M. Pesado, Cloud computing, big data and the industry 4.0 reference architectures, Journal of Computer Science & Technology, 2018, 18.
[11] J. Z. Zhang, P. R. Srivastava, D. Sharma, and P. Eachempati, Big data analytics and machine learning: A retrospective overview and bibliometric analysis, Expert Systems with Applications, 2021, 184, pp. 115561.
[12] J. C. Kabugo, S.-L. Jämsä-Jounela, R. Schiemann, and C. Binder, Industry 4.0 based process data analytics platform: A waste-to-energy plant case study, International journal of electrical power & energy systems, 2020, 115, pp. 105508.
[13] H. Jahani, R. Jain, and D. Ivanov, Data science and big data analytics: A systematic review of methodologies used in the supply chain and logistics research, Annals of Operations Research, 2023, pp. 1-58.
[14] N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review, Partners Universal International Innovation Journal, 2024, 2(3), pp. 147-171.
[15] N. Saini, A. L. Yadav, and A. Rahman. Cloud Based Predictive Maintenance System. in 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). 2024. IEEE.
[16] R. Rahmani, C. Jesus, and S. I. Lopes, Implementations of Digital Transformation and Digital Twins: Exploring the Factory of the Future, Processes, 2024, 12(4), pp. 787.
[17] M. Soori, B. Arezoo, and M. Habibi, Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system, International Journal of Computer Applications in Technology, 2017, 55(4), pp. 308-321.
[18] M. Soori, B. Arezoo, and M. Habibi, Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines, Journal of Manufacturing Systems, 2014, 33(4), pp. 498-507.
[19] M. Soori, B. Arezoo, and M. Habibi, Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system, Computer-Aided Design, 2013, 45(11), pp. 1306-1313.
[20] M. Soori, B. Arezoo, and M. Habibi, Tool deflection error of three-axis computer numerical control milling machines, monitoring and minimizing by a virtual machining system, Journal of Manufacturing Science and Engineering, 2016, 138(8), pp. 081005.
[21] M. Soori, F. K. G. Jough, and B. Arezoo, Surface quality enhancement by constant scallop-height in three-axis milling operations, Results in Surfaces and Interfaces, 2024, pp. 100208.
[22] M. Soori, M. Asmael, and D. Solyalı, Recent Development in Friction Stir Welding Process: A Review, SAE International Journal of Materials and Manufacturing, 2020(5), pp. 18.
[23] M. Soori and M. Asmael, Virtual Minimization of Residual Stress and Deflection Error in Five-Axis Milling of Turbine Blades, Strojniski Vestnik/Journal of Mechanical Engineering, 2021, 67(5), pp. 235-244.
[24] M. Soori and M. Asmael, Cutting temperatures in milling operations of difficult-to-cut materials, Journal of New Technology and Materials, 2021, 11(1), pp. 47-56.
[25] M. Soori, M. Asmael, A. Khan, and N. Farouk, Minimization of surface roughness in 5-axis milling of turbine blades, Mechanics Based Design of Structures and Machines, 2021, 51(9), pp. 1-18.
[26] M. Soori and M. Asmael, Minimization of Deflection Error in Five Axis Milling of Impeller Blades, Facta Universitatis, series: Mechanical Engineering, 2021, 21(2), pp. 175-190.
[27] M. Soori, Virtual product development. 2019: GRIN Verlag.
[28] M. Soori and M. Asmael, A Review of the Recent Development in Machining Parameter Optimization, Jordan Journal of Mechanical & Industrial Engineering, 2022, 16(2), pp. 205-223.
[29] R. Dastres, M. Soori, and M. Asmael, Radio Frequency Identification (RFID) Based Wireless Manufacturing Systems, A Review, Independent Journal of Management & Production, 2022, 13(1), pp. 258-290.
[30] M. Soori, B. Arezoo, and R. Dastres, Machine Learning and Artificial Intelligence in CNC Machine Tools, A Review, Sustainable Manufacturing and Service Economics, 2023, pp. 100009.
[31] M. Soori and B. Arezoo, A Review in Machining-Induced Residual Stress, Journal of New Technology and Materials, 2022, 12(1), pp. 64-83.
[32] M. Soori and B. Arezoo, Minimization of Surface Roughness and Residual Stress in Grinding Operations of Inconel 718, Journal of Materials Engineering and Performance, 2022, pp. 1-10.
[33] M. Soori and B. Arezoo, Cutting Tool Wear Prediction in Machining Operations, A Review, Journal of New Technology and Materials, 2022, 12(2), pp. 15-26.
[34] M. Soori and M. Asmael, Classification of research and applications of the computer aided process planning in manufacturing systems, Independent Journal of Management & Production, 2021, 12(5), pp. 1250-1281.
[35] R. Dastres and M. Soori, Advances in Web-Based Decision Support Systems, International Journal of Engineering and Future Technology, 2021, 19(1), pp. 1-15.
[36] R. Dastres and M. Soori, Artificial Neural Network Systems, International Journal of Imaging and Robotics (IJIR), 2021, 21(2), pp. 13-25.
[37] R. Dastres and M. Soori, The Role of Information and Communication Technology (ICT) in Environmental Protection, International Journal of Tomography and Simulation, 2021, 35(1), pp. 24-37.
[38] R. Dastres and M. Soori, Secure Socket Layer in the Network and Web Security, International Journal of Computer and Information Engineering, 2020, 14(10), pp. 330-333.
[39] R. Dastres and M. Soori, Advances in Web-Based Decision Support Systems, International Journal of Engineering and Future Technology, 2021.
[40] R. Dastres and M. Soori, A review in recent development of network threats and security measures, International Journal of Information Sciences and Computer Engineering, 2021.
[41] R. Dastres and M. Soori, Advanced image processing systems, International Journal of Imagining and Robotics, 2021, 21(1), pp. 27-44.
[42] M. Soori and B. Arezoo, Dimensional, geometrical, thermal and tool deflection errors compensation in 5-Axis CNC milling operations, Australian Journal of Mechanical Engineering, 2023, pp. 1-15.
[43] M. Soori, B. Arezoo, and R. Dastres, Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review, Cognitive Robotics, 2023, 3, pp. 54-70.
[44] M. Soori and B. Arezoo, Effect of Cutting Parameters on Tool Life and Cutting Temperature in Milling of AISI 1038 Carbon Steel, Journal of New Technology and Materials, 2023.
[45] M. Soori and B. Arezoo, The effects of coolant on the cutting temperature, surface roughness and tool wear in turning operations of Ti6Al4V alloy, Mechanics Based Design of Structures and Machines, 2023, pp. 1-23.
[46] M. Soori, Advanced Composite Materials and Structures, Journal of Materials and Engineering Structures, 2023.
[47] M. Soori, B. Arezoo, and R. Dastres, Internet of things for smart factories in industry 4.0, a review, Internet of Things and Cyber-Physical Systems, 2023.
[48] M. Soori and B. Arezoo, Cutting tool wear minimization in drilling operations of titanium alloy Ti-6Al-4V, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2023, pp. 13506501231158259.
[49] M. Soori and B. Arezoo, Minimization of surface roughness and residual stress in abrasive water jet cutting of titanium alloy Ti6Al4V, Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2023, pp. 09544089231157972.
[50] M. Soori, Deformation error compensation in 5-Axis milling operations of turbine blades, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2023, 45(6), pp. 289.
[51] M. Soori and B. Arezoo, Modification of CNC Machine Tool Operations and Structures Using Finite Element Methods, A Review, Jordan Journal of Mechanical and Industrial Engineering, 2023.
[52] M. Soori, B. Arezoo, and R. Dastres, Optimization of Energy Consumption in Industrial Robots, A Review, Cognitive Robotics, 2023.
[53] M. Soori, F. K. G. Jough, R. Dastres, and B. Arezoo, Blockchains for Industrial Internet of Things in Sustainable Supply Chain Management of Industry 4.0, A Review, Sustainable Manufacturing and Service Economics, 2024, pp. 100026.
[54] M. Soori, B. Arezoo, and R. Dastres, Virtual manufacturing in industry 4.0: A review, Data Science and Management, 2023.
[55] M. Soori, B. Arezoo, and R. Dastres, Artificial Neural Networks in Supply Chain Management, A Review, Journal of Economy and Technology, 2023.
[56] T. Raoofi and M. Yildiz, Comprehensive review of battery state estimation strategies using machine learning for battery Management Systems of Aircraft Propulsion Batteries, Journal of Energy Storage, 2023, 59, pp. 106486.
[57] T. Raoofi and S. Yasar, Analysis of frontier digital technologies in continuing airworthiness management frameworks and applications, Aircraft Engineering and Aerospace Technology, 2023, 95(10), pp. 1669-1677.
[58] T. Raoofi and O. Ölçen, The legal attitudes of continental aviation toward sustainable aircraft technologies and airport infrastructures, International Journal of Sustainable Aviation, 2024, 10(2), pp. 124-141.
[59] M. Soori and M. Asmael, Virtual minimization of residual stress and deflection error in the five-axis milling of turbine blades, Strojniški vestnik= Journal of Mechanical Engineering, 2021, 67(5), pp. 235-244.
[60] M. Soori and M. Asmael, Minimization of deflection error in five axis milling of impeller blades, Facta Universitatis, series: Mechanical Engineering, 2023, 21(2), pp. 175-190.
[61] M. Soori, R. Dastres, and B. Arezoo, Ai-powered blockchain technology in industry 4.0, a review, Journal of Economy and Technology, 2023, 1, pp. 222-241.
[62] M. Soori and B. Arezoo, Virtual machining systems for CNC milling and turning machine tools: a review, International Journal of Engineering and Future Technology, 2020, 18(1), pp. 56-104.
[63] M. Soori, F. K. G. Jough, and B. Arezoo, Surface quality enhancement by constant scallop-height in three-axis milling operations, Results in Surfaces and Interfaces, 2024, 14, pp. 100208.
[64] M. Soori, B. Arezoo, and R. Dastres, Advanced virtual manufacturing systems: A review, Journal of Advanced Manufacturing Science and Technology, 2023.
[65] M. Soori, F. K. G. Jough, R. Dastres, and B. Arezoo, Robotical Automation in CNC Machine Tools: A Review, acta mechanica et automatica, 2023, 18(3), pp. 434-450.
[66] M. Soori, B. Arezoo, and R. Dastres, Digital twin for smart manufacturing, A review, Sustainable Manufacturing and Service Economics, 2023, pp. 100017.
[67] M. Soori, F. K. G. Jough, R. Dastres, and B. Arezoo, AI-Based Decision Support Systems in Industry 4.0, A Review, Journal of Economy and Technology, 2024.
[68] M. Soori and M. Asmael, Mechanical behavior of materials in metal cutting operations, a review, Journal of New Technology and Materials, 2020, 10(2), pp. 79-82.
[69] C. A. Udeh, O. H. Orieno, O. D. Daraojimba, N. L. Ndubuisi, and O. I. Oriekhoe, Big data analytics: a review of its transformative role in modern business intelligence, Computer Science & IT Research Journal, 2024, 5(1), pp. 219-236.
[70] S. Kolasani, Innovations in digital, enterprise, cloud, data transformation, and organizational change management using agile, lean, and data-driven methodologies, International Journal of Machine Learning and Artificial Intelligence, 2023, 4(4), pp. 1-18.
[71] D. Uztürk and G. Büyüközkan, Industry 4.0 technologies in Smart Agriculture: A review and a Technology Assessment Model proposition, Technological Forecasting and Social Change, 2024, 208, pp. 123640.
[72] R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo, and J. Barata, Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook, IEEE Access, 2020, 8, pp. 220121-220139.
[73] J. Wang, C. Xu, J. Zhang, and R. Zhong, Big data analytics for intelligent manufacturing systems: A review, Journal of Manufacturing Systems, 2022, 62, pp. 738-752.
[74] S. Maheshwari, P. Gautam, and C. K. Jaggi, Role of Big Data Analytics in supply chain management: current trends and future perspectives, International Journal of Production Research, 2021, 59(6), pp. 1875-1900.
[75] A. Hassoun, et al., Use of industry 4.0 technologies to reduce and valorize seafood waste and by-products: A narrative review on current knowledge, Current research in food science, 2023, 6, pp. 100505.
[76] A. Bousdekis, K. Lepenioti, D. Apostolou, and G. Mentzas, Data analytics in quality 4.0: literature review and future research directions, International Journal of Computer Integrated Manufacturing, 2023, 36(5), pp. 678-701.
[77] F. Yin and F. Shi, A comparative survey of big data computing and HPC: From a parallel programming model to a cluster architecture, International Journal of Parallel Programming, 2022, 50(1), pp. 27-64.
[78] A. Islam, Hybrid Cloud Databases for Big Data Analytics: A Review of Architecture, Performance, and Cost Efficiency, International journal of management information systems and data science, 2024, 1(4), pp. 10.62304.
[79] I. H. Sarker, Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective, SN Computer Science, 2021, 2(5), pp. 377.
[80] H. Singh, Big data, industry 4.0 and cyber-physical systems integration: A smart industry context, Materials Today: Proceedings, 2021, 46, pp. 157-162.
[81] A. K. Sandhu, Big data with cloud computing: Discussions and challenges, Big Data Mining and Analytics, 2021, 5(1), pp. 32-40.
[82] L. AlSuwaidan, The role of data management in the Industrial Internet of Things, Concurrency and Computation: Practice and Experience, 2021, 33(23), pp. e6031.
[83] H. Han and S. Trimi, Towards a data science platform for improving SME collaboration through Industry 4.0 technologies, Technological Forecasting and Social Change, 2022, 174, pp. 121242.
[84] R. Chataut, A. Phoummalayvane, and R. Akl, Unleashing the power of IoT: A comprehensive review of IoT applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0, Sensors, 2023, 23(16), pp. 7194.
[85] A. A. Wagire, A. Rathore, and R. Jain, Analysis and synthesis of Industry 4.0 research landscape: Using latent semantic analysis approach, Journal of Manufacturing Technology Management, 2020, 31(1), pp. 31-51.
[86] H. C. Hsiao, M. H. Hung, C. C. Chen, and Y. C. Lin, Cloud Computing, Internet of Things (IoT), Edge Computing, and Big Data Infrastructure, Industry 4.1: Intelligent Manufacturing with Zero Defects, 2021, pp. 129-167.
[87] F. M. Awaysheh, M. N. Aladwan, M. Alazab, S. Alawadi, J. C. Cabaleiro, and T. F. Pena, Security by design for big data frameworks over cloud computing, IEEE Transactions on Engineering Management, 2021, 69(6), pp. 3676-3693.
[88] S. B. Abkenar, M. H. Kashani, E. Mahdipour, and S. M. Jameii, Big data analytics meets social media: A systematic review of techniques, open issues, and future directions, Telematics and informatics, 2021, 57, pp. 101517.
[89] A. Sabtu, N. F. Mohd Azmi, N. N. Amir Sjarif, S. Adli Ismail, O. Mohd Yusop, H. Sarkan, and S. Chuprat, The Challenges of Extract, Transform and Load (ETL) for Data Integration in Near Realtime Environment, Journal of Theoretical & Applied Information Technology, 2017, 95(22).
[90] S. Sun, X. Zheng, J. Villalba-Díez, and J. Ordieres-Meré, Data handling in industry 4.0: Interoperability based on distributed ledger technology, Sensors, 2020, 20(11), pp. 3046.
[91] A. Bousdekis and G. Mentzas, Enterprise Integration and Interoperability for big data-driven processes in the Frame of Industry 4.0, Frontiers in big Data, 2021, 4, pp. 644651.
[92] Z. Bi, Y. Jin, P. Maropoulos, W.-J. Zhang, and L. Wang, Internet of things (IoT) and big data analytics (BDA) for digital manufacturing (DM), International Journal of Production Research, 2023, 61(12), pp. 4004-4021.
[93] M. Sony and S. Naik, Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model, Technology in society, 2020, 61, pp. 101248.
[94] A. Sharma and H. Pandey, Big data and analytics in industry 4.0, A roadmap to Industry 4.0: Smart production, sharp business and sustainable development, 2020, pp. 57-72.
[95] A. Al-Abassi, H. Karimipour, H. HaddadPajouh, A. Dehghantanha, and R. M. Parizi, Industrial big data analytics: challenges and opportunities, Handbook of big data privacy, 2020, pp. 37-61.
[96] L. Duan and L. Da Xu, Data analytics in industry 4.0: A survey, Information Systems Frontiers, 2021, pp. 1-17.
[97] X. Liao, M. Faisal, Q. QingChang, and A. Ali, Evaluating the role of big data in IIOT-industrial internet of things for executing ranks using the analytic network process approach, Scientific Programming, 2020, 2020, pp. 1-7.
[98] C. Riley, J. Vrbka, and Z. Rowland, Internet of things-enabled sustainability, big data-driven decision-making processes, and digitized mass production in industry 4.0-based manufacturing systems, Journal of Self-Governance and Management Economics, 2021, 9(1), pp. 42-52.
[99] S. Sajid, A. Haleem, S. Bahl, M. Javaid, T. Goyal, and M. Mittal, Data science applications for predictive maintenance and materials science in context to Industry 4.0, Materials today: proceedings, 2021, 45, pp. 4898-4905.
[100] A. Bousdekis, K. Lepenioti, D. Apostolou, and G. Mentzas, A review of data-driven decision-making methods for industry 4.0 maintenance applications, Electronics, 2021, 10(7), pp. 828.
[101] M. Javaid, A. Haleem, R. P. Singh, and R. Suman, Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 technologies, Internet of Things and Cyber-Physical Systems, 2022, 2, pp. 49-62.
[102] T. Edwin Cheng, S. S. Kamble, A. Belhadi, N. O. Ndubisi, K.-h. Lai, and M. G. Kharat, Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms, International Journal of Production Research, 2022, 60(22), pp. 6908-6922.
[103] A. Sajjad, W. Ahmad, S. Hussain, and R. M. Mehmood, Development of innovative operational flexibility measurement model for smart systems in industry 4.0 paradigm, IEEE Access, 2021, 10, pp. 6760-6774.
[104] Y. Hajjaji, W. Boulila, I. R. Farah, I. Romdhani, and A. Hussain, Big data and IoT-based applications in smart environments: A systematic review, Computer Science Review, 2021, 39, pp. 100318.
[105] S. Aheleroff, X. Xu, Y. Lu, M. Aristizabal, J. P. Velásquez, B. Joa, and Y. Valencia, IoT-enabled smart appliances under industry 4.0: A case study, Advanced engineering informatics, 2020, 43, pp. 101043.
[106] M. Kovacova and E. Lewis, Smart factory performance, cognitive automation, and industrial big data analytics in sustainable manufacturing internet of things, Journal of Self-Governance and Management Economics, 2021, 9(3), pp. 9-21.
[107] O. E. Oluyisola, S. Bhalla, F. Sgarbossa, and J. O. Strandhagen, Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study, Journal of Intelligent Manufacturing, 2022, 33(1), pp. 311-332.
[108] M.-L. Tseng, T. P. T. Tran, H. M. Ha, T.-D. Bui, and M. K. Lim, Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: A data driven analysis, Journal of Industrial and Production Engineering, 2021, 38(8), pp. 581-598.
[109] Y. K. Teoh, S. S. Gill, and A. K. Parlikad, IoT and fog computing based predictive maintenance model for effective asset management in industry 4.0 using machine learning, IEEE Internet of Things Journal, 2021.
[110] N. Moustafa, A systemic IoT–fog–cloud architecture for big-data analytics and cyber security systems: A review of fog computing, Secure Edge Computing, 2021, pp. 41-50.
[111] P. Radanliev, D. De Roure, K. Page, J. R. Nurse, R. Mantilla Montalvo, O. Santos, L. T. Maddox, and P. Burnap, Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains, Cybersecurity, 2020, 3, pp. 1-21.
[112] M. Ammar, A. Haleem, M. Javaid, R. Walia, and S. Bahl, Improving material quality management and manufacturing organizations system through Industry 4.0 technologies, Materials Today: Proceedings, 2021, 45, pp. 5089-5096.
[113] T. Nguyen, R. G. Gosine, and P. Warrian, A systematic review of big data analytics for oil and gas industry 4.0, IEEE access, 2020, 8, pp. 61183-61201.
[114] M. Karatas, L. Eriskin, M. Deveci, D. Pamucar, and H. Garg, Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives, Expert Systems with Applications, 2022, 200, pp. 116912.
[115] Q. Qi, Z. Xu, and P. Rani, Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations, Technological Forecasting and Social Change, 2023, 190, pp. 122401.
[116] M. B. Ozcan, B. Konuk, and Y. M. Yesilcimen, Big Data Analytics in Industry 4.0, in Industry 4.0: Technologies, Applications, and Challenges. 2022, Springer. p. 171-199.
[117] M. Gupta, F. M. Awaysheh, J. Benson, M. Alazab, F. Patwa, and R. Sandhu, An attribute-based access control for cloud enabled industrial smart vehicles, IEEE Transactions on Industrial Informatics, 2020, 17(6), pp. 4288-4297.
[118] F. Muheidat and L. a. Tawalbeh, Mobile and cloud computing security, Machine intelligence and big data analytics for cybersecurity applications, 2021, pp. 461-483.
[119] S. Shafqat, S. Kishwer, R. U. Rasool, J. Qadir, T. Amjad, and H. F. Ahmad, Big data analytics enhanced healthcare systems: a review, The Journal of Supercomputing, 2020, 76, pp. 1754-1799.
[120] C. Yang, S. Lan, L. Wang, W. Shen, and G. G. Huang, Big data driven edge-cloud collaboration architecture for cloud manufacturing: a software defined perspective, IEEE access, 2020, 8, pp. 45938-45950.
[121] R. Chalmeta and N. J. Santos-deLeón, Sustainable supply chain in the era of industry 4.0 and big data: A systematic analysis of literature and research, Sustainability, 2020, 12(10), pp. 4108.
[122] G. Rathee, M. Balasaraswathi, K. P. Chandran, S. D. Gupta, and C. Boopathi, A secure IoT sensors communication in industry 4.0 using blockchain technology, Journal of Ambient Intelligence and Humanized Computing, 2021, 12, pp. 533-545.
[123] S. Shukla, M. F. Hassan, D. C. Tran, R. Akbar, I. V. Paputungan, and M. K. Khan, Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR), Cluster Computing, 2023, pp. 1-24.
[124] M. Shahin, F. F. Chen, H. Bouzary, and K. Krishnaiyer, Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises, The International Journal of Advanced Manufacturing Technology, 2020, 107, pp. 2927-2936.
[125] P. L. Martínez, R. Dintén, J. M. Drake, and M. Zorrilla, A big data-centric architecture metamodel for Industry 4.0, Future Generation Computer Systems, 2021, 125, pp. 263-284.
[126] S. Munirathinam, Industry 4.0: Industrial internet of things (IIOT), in Advances in computers. 2020, Elsevier. p. 129-164.
[127] D. Kumar, R. K. Singh, R. Mishra, and I. Vlachos, Big data analytics in supply chain decarbonisation: a systematic literature review and future research directions, International Journal of Production Research, 2024, 62(4), pp. 1489-1509.
[128] A. Costantini, et al., Iotwins: Toward implementation of distributed digital twins in industry 4.0 settings, Computers, 2022, 11(5), pp. 67.
[129] I. H. Khan and M. Javaid, Role of Internet of Things (IoT) in adoption of Industry 4.0, Journal of Industrial Integration and Management, 2022, 7(04), pp. 515-533.
[130] M. Ramaiah, V. Chithanuru, A. Padma, and V. Ravi, A review of security vulnerabilities in industry 4.0 application and the possible solutions using blockchain, Cyber Security Applications for Industry 4.0, 2022, pp. 63-95.
[131] A. Jamwal, R. Agrawal, M. Sharma, and A. Giallanza, Industry 4.0 technologies for manufacturing sustainability: a systematic review and future research directions, Applied Sciences, 2021, 11(12), pp. 5725.
[132] S. Atiewi, A. Al-Rahayfeh, M. Almiani, S. Yussof, O. Alfandi, A. Abugabah, and Y. Jararweh, Scalable and secure big data IoT system based on multifactor authentication and lightweight cryptography, IEEE Access, 2020, 8, pp. 113498-113511.
[133] B. Bajic, A. Rikalovic, N. Suzic, and V. Piuri, Industry 4.0 implementation challenges and opportunities: A managerial perspective, IEEE Systems Journal, 2020, 15(1), pp. 546-559.
[134] S. Kahveci, B. Alkan, A. Mus’ab H, B. Ahmad, and R. Harrison, An end-to-end big data analytics platform for IoT-enabled smart factories: A case study of battery module assembly system for electric vehicles, Journal of Manufacturing Systems, 2022, 63, pp. 214-223.
[135] R. D. Raut, V. S. Yadav, N. Cheikhrouhou, V. S. Narwane, and B. E. Narkhede, Big data analytics: Implementation challenges in Indian manufacturing supply chains, Computers in Industry, 2021, 125, pp. 103368.
[136] S. K. Jagatheesaperumal, M. Rahouti, K. Ahmad, A. Al-Fuqaha, and M. Guizani, The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions, IEEE Internet of Things Journal, 2021, 9(15), pp. 12861-12885.
[137] E. Peters, T. Kliestik, H. Musa, and P. Durana, Product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning in sustainable industry 4.0, Journal of Self-Governance and Management Economics, 2020, 8(3), pp. 16-22.
[138] A. Johannsen, D. Kant, and R. Creutzburg, Measuring IT security, compliance and data governance within small and medium-sized IT enterprises, Electronic Imaging, 2020, 2020(3), pp. 252-1-252-11.
[139] N. Dalčeković, G. Sladić, N. Luburić, and M. Stojkov, Automating Multidimensional Security Compliance for Cloud-Based Industry 4.0, in Industrial Innovation in Digital Age. 2020, Springer. p. 193-200.
[140] M. Kosicki, M. Tsiliakos, K. ElAshry, and M. Tsigkari, Big Data and Cloud Computing for the Built Environment, in Industry 4.0 for the Built Environment: Methodologies, Technologies and Skills. 2021, Springer. p. 131-155.
[141] G. Atharvan, S. Koolikkara Madom Krishnamoorthy, A. Dua, and S. Gupta, A way forward towards a technology‐driven development of industry 4.0 using big data analytics in 5G‐enabled IIoT, International Journal of Communication Systems, 2022, 35(1), pp. e5014.
[142] J. Li, J. Wu, G. Jiang, and T. Srikanthan, Blockchain-based public auditing for big data in cloud storage, Information Processing & Management, 2020, 57(6), pp. 102382.
[143] A. Bicaku, M. Tauber, and J. Delsing, Security standard compliance and continuous verification for Industrial Internet of Things, International Journal of Distributed Sensor Networks, 2020, 16(6), pp. 1550147720922731.
[144] R. Krishankumar, R. Sivagami, A. Saha, P. Rani, K. Arun, and K. Ravichandran, Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information, Applied Intelligence, 2022, 52(12), pp. 13497-13519.
[145] J. Dalzochio, R. Kunst, E. Pignaton, A. Binotto, S. Sanyal, J. Favilla, and J. Barbosa, Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges, Computers in Industry, 2020, 123, pp. 103298.
[146] B. Bajic, N. Suzic, N. Simeunovic, S. Moraca, and A. Rikalovic, Real-time data analytics edge computing application for industry 4.0: The mahalanobis-taguchi approach, Int. J. Ind. Eng. Manag, 2020, 11(3), pp. 146-156.
[147] S. Panicucci, et al., A cloud-to-edge approach to support predictive analytics in robotics industry, Electronics, 2020, 9(3), pp. 492.
[148] L. Silvestri, A. Forcina, V. Introna, A. Santolamazza, and V. Cesarotti, Maintenance transformation through Industry 4.0 technologies: A systematic literature review, Computers in industry, 2020, 123, pp. 103335.
[149] J. Lawrence and P. Durana, Artificial Intelligence-driven Big Data Analytics, Predictive Maintenance Systems, and Internet of Thingsbased Real-Time Production Logistics in Sustainable Industry 4.0 Wireless Networks, Journal of Self-Governance & Management Economics, 2021, 9(4).
[150] L. Tamym, M. El Oaudghiri, L. Benyoucef, and A. N. S. Moh. Big data for supply chain management in industry 4.0 context: A comprehensive survey. in 13ème Conference Internationale de Modelisation, Optimisation et Simulation (MOSIM2020), 12-14 Nov 2020, AGADIR, Maroc. 2020.
[151] I. Lee and G. Mangalaraj, Big data analytics in supply chain management: A systematic literature review and research directions, Big data and cognitive computing, 2022, 6(1), pp. 17.
[152] S. Paul, M. Riffat, A. Yasir, M. N. Mahim, B. Y. Sharnali, I. T. Naheen, A. Rahman, and A. Kulkarni, Industry 4.0 applications for medical/healthcare services, Journal of Sensor and Actuator Networks, 2021, 10(3), pp. 43.
[153] S. B. Rane and Y. A. M. Narvel, Data-driven decision making with Blockchain-IoT integrated architecture: a project resource management agility perspective of industry 4.0, International Journal of System Assurance Engineering and Management, 2022, pp. 1-19.
[154] M. Marinho, V. Prakash, L. Garg, C. Savaglio, and S. Bawa, Effective cloud resource utilisation in cloud erp decision-making process for industry 4.0 in the United States, Electronics, 2021, 10(8), pp. 959.
[155] A. S. Mohammad and M. R. Pradhan, Machine learning with big data analytics for cloud security, Computers & Electrical Engineering, 2021, 96, pp. 107527.
[156] N. Prakash, J. Vignesh, M. Ashwin, S. Ramadass, N. Veeranjaneyulu, S. V. Athawale, A. Ravuri, and B. Subramanian, Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach, Optical and Quantum Electronics, 2024, 56(2), pp. 251.
[157] M. Elsisi, K. Mahmoud, M. Lehtonen, and M. M. Darwish, Reliable industry 4.0 based on machine learning and IOT for analyzing, monitoring, and securing smart meters, Sensors, 2021, 21(2), pp. 487.
[158] A. K. Sarangi, A. G. Mohapatra, T. C. Mishra, and B. Keswani, Healthcare 4.0: A voyage of fog computing with iot, cloud computing, big data, and machine learning, Fog Computing for Healthcare 4.0 Environments: Technical, Societal, and Future Implications, 2021, pp. 177-210.
[159] S. Rogers and E. Kalinova, Big data-driven decision-making processes, real-time advanced analytics, and cyber-physical production networks in Industry 4.0-based manufacturing systems, Economics, Management and Financial Markets, 2021, 16(4), pp. 84-97.
[160] R. Anandan, S. Gopalakrishnan, S. Pal, and N. Zaman, Industrial Internet of Things (IIoT): Intelligent Analytics for Predictive Maintenance. 2022: John Wiley & Sons.
[161] S. Chauhan, R. Singh, A. Gehlot, S. V. Akram, B. Twala, and N. Priyadarshi, Digitalization of Supply Chain Management with Industry 4.0 Enabling Technologies: A Sustainable Perspective, Processes, 2022, 11(1), pp. 96.
[162] P. K. Tiwari, S. K. Pandey, W. Thamba Meshach, J. Parashar, A. Kumar, M. Altuwairiqi, and D. Krah, Improved Data Security in Cloud Environment for Test Automation Framework and Access Control for Industry 4.0, Wireless Communications and Mobile Computing, 2022, 2022.
[163] A. Adel, Unlocking the Future: Fostering Human–Machine Collaboration and Driving Intelligent Automation through Industry 5.0 in Smart Cities, Smart Cities, 2023, 6(5), pp. 2742-2782.
[164] J. Villalba-Diez and J. Ordieres-Meré, Human–machine integration in processes within industry 4.0 management, Sensors, 2021, 21(17), pp. 5928.