Enterprise Risk & Data Center Governance for Industry 4.0
Keywords:
Enterprise Risk Management (ERM), Industry 4.0 Risk Systems, Data Center Governance, Cyber-Physical Risk Management, IT-OT Integration, Digital Risk Transformation, Data Quality Governance, Data Stewardship Models, Risk Analytics Frameworks, Operational Risk Management, Industrial Data Governance, Risk-Based Decision Systems, Integrated Risk Frameworks, Manufacturing Risk Analytics, Cloud-Integrated Risk Systems, Strategic Risk Governance, Layered Governance Models, Industrial Cybersecurity, Data-Driven Risk Management, Industry 4.0 Governance.Abstract
Digital transitions are revolutionising industrial processes and services offered. The new arrangements introduce new risks across the entire value chain domain from planning to supply, production and delivery of services. State-of-the-art Enterprise Risk Management (ERM) provides the necessary elements to cover risk domains associated to cyber-physical systems interconnected by the operating technology in concert with Information Technology under Industry 4.0 paradigms Control mechanisms from ERM needs new support structure provided by the Data Centre Governance principles, in particular those linked to data quality, security, privacy and stewardship, for a coherent data preparation and management able to deliver the correct and timely analytics supporting the decision making.
The effort synthesises the integrated Enterprise Risk and the Data Centre Governance Framework, integrated with data quality requirements, applying the two best practice domains to the Industry 4.0 era. The underlying concept defined a layer governance structure able to cover day-to-day operate in the production/service delivery sites and the strategic management levels for the application to any organisation supporting the integrated risk management approach. The set of guiding principles is outlined consequently. Grounding elements are drawn from literature sources, the originals support description from the Centre for Internet Security, the Industrial Internet Consortium guiding principles and recommendations, five case studies of Industry 4.0 environments for Manufacturing and the integration of cloud technologies in data centres. The resulting work delivers an integrated structure capable to capture coherent data from the production/service area and to deliver the correct analytics behind the ERM risk management for any Industry 4.0 context.
References
1. Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital twin as a service (DTaaS) in Industry 4.0: An architecture reference model. Advanced Engineering Informatics, 47, 101225.
2. Alcaraz, C., & Lopez, J. (2020). Cyber resilience and security in Industry 4.0. Computer Standards & Interfaces, 71, 103453.
3. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
4. Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). Role of data analytics and IoT in Industry 4.0 and supply chain risk management. International Journal of Production Research, 59(10), 3157–3178.
5. Bécue, A., Praça, I., & Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 54(5), 3849–3886.
6. Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2021). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26.
7. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
8. Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869.
9. Ivanov, D., Dolgui, A., & Sokolov, B. (2020). The impact of digital technology and Industry 4.0 on resilient supply chain risk management. International Journal of Production Research, 58(10), 2904–2915.
10. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2022). Understanding the adoption of Industry 4.0 technologies: Challenges and opportunities. Sustainable Operations and Computers, 3, 203–213.
11. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
12. Kagermann, H., Wahlster, W., & Helbig, J. (2021). Smart manufacturing and Industry 4.0: Future industrial transformation. Journal of Manufacturing Systems, 58, 1–12.
13. Kaur, K., Garg, S., & Kaddoum, G. (2021). Artificial intelligence for cybersecurity in Industry 4.0 environments: Challenges and future directions. IEEE Network, 35(1), 54–60.
14. Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., & Liu, C. (2021). Blockchain-empowered sustainable manufacturing and Industry 4.0. Robotics and Computer-Integrated Manufacturing, 69, 102074.
15. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
16. Lu, Y. (2020). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10.
17. Malik, A. A., Bilberg, A., & Krenkel, L. (2022). Data governance and digital transformation in Industry 4.0 manufacturing systems. Computers in Industry, 139, 103650.
18. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). Smart factory and Industry 4.0 technologies for manufacturing transformation. Procedia CIRP, 107, 1492–1497.
19. Nayeri, P., Asadi, S., & Shafiee, M. (2023). Enterprise risk management in Industry 4.0: A systematic literature review. Journal of Risk Research, 26(4), 463–484.
20. Pereira, K., Vinagre, J., Alonso, A. N., Coelho, F., & Carvalho, M. (2022, September). Privacy-preserving machine learning in life insurance risk prediction. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 44-52). Cham: Springer Nature Switzerland.
21. Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182.
22. Sony, M., Antony, J., & McDermott, O. (2022). The impact of Industry 4.0 on organizational performance and enterprise risk management. The TQM Journal, 34(6), 1570–1588.
23. Yebenes, J., & Zorrilla, M. (2021). A data governance framework for Industry 4.0. IEEE Latin America Transactions, 19(12), 2130–2138.
24. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
25. Zorrilla, M., & Yebenes, J. (2022). A reference framework for the implementation of data governance systems for Industry 4.0. Computer Standards & Interfaces, 81, 103595.
26. Zhang, Y., Ren, S., Liu, Y., Si, S., & Jones, L. (2021). A big data analytics architecture for cleaner manufacturing and maintenance processes in Industry 4.0. Journal of Cleaner Production, 279, 123896.
27. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.
28. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
29. Alcaraz, C., Roman, R., Najera, P., & Lopez, J. (2021). Security of industrial sensor networks based on wireless sensor networks and Industry 4.0. Ad Hoc Networks, 118, 102512.
30. Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776.
31. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
32. Ben-Daya, M., Hassini, E., & Bahroun, Z. (2021). Internet of Things and smart manufacturing: Applications, opportunities, and challenges. International Journal of Production Research, 59(15), 4493–4509.
33. Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), 1–38.
34. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
35. Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2021). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383–394.
36. Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D. P., Gustafsson, A., Hinsch, C., Jebabli, I., ... Wamba, S. F. (2022). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. International Journal of Information Management, 71, 102642.
37. Reddy, V. A. R. (2022). Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms. International Journal of Science, Research and Technology, 5(5), 8511-8521.
38. Ellinas, C., Allan, N., & Johansson, A. (2021). Data governance in digital transformation: Systematic review and future research directions. Information Systems Frontiers, 23(6), 1523–1542.
39. Fatorachian, H., & Kazemi, H. (2021). A critical investigation of Industry 4.0 in manufacturing: Theoretical operationalisation framework. Production Planning & Control, 32(3), 215–230.
40. Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2021). A hybrid deep learning model for efficient intrusion detection in Industry 4.0. IEEE Transactions on Industrial Informatics, 17(11), 7704–7714.
41. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
42. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruption risks and resilience in Industry 4.0. Production Planning & Control, 32(9), 775–788.
43. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2022). Digital technologies for Industry 4.0 and smart manufacturing: A review. Sustainable Operations and Computers, 3, 275–284.
44. Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831.
45. Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
46. Lee, J., Davari, H., Singh, J., & Pandhare, V. (2020). Industrial artificial intelligence for Industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23.
47. Liu, Y., Zhang, Y., Ren, S., Yang, M., Wang, Y., & Huisingh, D. (2021). How can smart technologies contribute to sustainable product lifecycle management? Journal of Cleaner Production, 249, 119423.
48. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
49. Moeuf, A., Lamouri, S., Pellerin, R., Tamayo-Giraldo, S., Tobon-Valencia, E., & Eburdy, R. (2020). Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs. International Journal of Production Research, 58(5), 1384–1400.
50. Pan, S. L., & Zhang, S. (2021). From fighting COVID-19 to managing Industry 4.0: Digital transformation and enterprise resilience. Information Systems Frontiers, 23(4), 947–957.
51. Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2021). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 319(1), 115–143.
52. Sarker, I. H. (2022). Smart cybersecurity framework for Industry 4.0 using artificial intelligence. Journal of Big Data, 9(1), 1–30.
53. Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
54. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535.
55. Addepalli, S., Sarker, I. H., & Rahman, M. (2023). Artificial intelligence-driven cyber risk management for Industry 4.0 environments. Journal of Information Security and Applications, 73, 103417.
56. Alcaraz, C., Lopez, J., & Roman, R. (2023). Cybersecurity governance for Industry 4.0: Challenges, trends, and future research directions. Computers & Security, 126, 103053.
57. Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2023). Big data analytics capability and organizational resilience in Industry 4.0. Technological Forecasting and Social Change, 188, 122284.
58. Chatterjee, S., Chaudhuri, R., Vrontis, D., & Thrassou, A. (2023). Digital transformation and enterprise risk management: A systematic review. Journal of Business Research, 161, 113839.
59. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
60. Dolgui, A., Ivanov, D., & Sokolov, B. (2023). Reconfigurable supply chain resilience in the era of Industry 4.0. International Journal of Production Research, 61(8), 2524–2542.
61. El Baz, J., & Ruel, S. (2023). Can supply chain risk management create resilience during disruptions? International Journal of Logistics Management, 34(1), 1–24.
62. Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2023). Industry 4.0 technologies and organizational performance: A comprehensive review. Journal of Manufacturing Technology Management, 34(3), 489–512.
63. Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2023). Industry 4.0, digital transformation, and sustainable development: A systematic literature review. Journal of Cleaner Production, 384, 135551.
64. Ivanov, D. (2023). The Industry 5.0 framework: Viability-based integration of resilience, sustainability, and human-centricity. International Journal of Production Research, 61(5), 1684–1695.
65. Javaid, M., Haleem, A., Singh, R. P., Khan, I. H., Suman, R., & Rab, S. (2023). Data analytics applications for Industry 4.0 and smart manufacturing: A review. Sensors, 23(3), 1250.
66. Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2023). Industry 5.0: Prospect and retrospective. Journal of Manufacturing Systems, 68, 279–295.
67. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
68. Lu, Y., Zheng, H., Chand, S., Xia, W., & Liu, Z. (2023). Artificial intelligence for Industry 5.0: Architecture, applications, and challenges. Engineering, 21, 167–180.
69. Moeuf, A., Lamouri, S., Pellerin, R., & Eburdy, R. (2023). Digital transformation and enterprise performance in Industry 4.0: Empirical evidence from manufacturing organizations. Production Planning & Control, 34(10), 951–966.
70. Nayeri, P., Asadi, S., & Shafiee, M. (2023). Enterprise risk management in Industry 4.0: A systematic literature review. Journal of Risk Research, 26(4), 463–484.
71. Queiroz, M. M., Wamba, S. F., & Fosso Wamba, S. (2023). Digital supply chains, resilience, and Industry 4.0: A review and future research agenda. International Journal of Production Economics, 255, 108684.
72. Sarker, I. H. (2023). AI-enabled cybersecurity for smart manufacturing and Industry 4.0: A review. Internet of Things, 22, 100760.
73. Sony, M., Antony, J., & McDermott, O. (2023). Industry 4.0 and enterprise risk management: Critical success factors and implementation framework. The TQM Journal, 35(6), 1463–1482.
74. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2023). Smart manufacturing and digital transformation in Industry 4.0: State of the art and future directions. Journal of Manufacturing Systems, 68, 540–554.
75. Yadav, G., Luthra, S., Huisingh, D., Mangla, S. K., Narkhede, B. E., & Liu, Y. (2023). The role of digital technologies in sustainable manufacturing and Industry 4.0 implementation. Journal of Cleaner Production, 386, 135784.
76. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2023). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 19, 1–17.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2023 Benjamin Clark (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain full copyright of their published work. Under this license, others are free to share, copy, distribute, transmit, remix, transform, and build upon the published work for any purpose, including commercial use, provided that appropriate credit is given to the original authors, a link to the license is provided, and any changes made are clearly indicated. No additional restrictions may be applied that limit others from doing anything the license permits. All published articles are freely and permanently accessible online to readers worldwide without any subscription or access fees.
License URL: https://creativecommons.org/licenses/by/4.0/