Jul 2026· IAIC Transactions on Sustainable Digital Innovation (ITSDI)· 0 citations
Abstract
Overhead crane operations remain a high-risk activity in heavy manufacturing, yet safety management often relies on static assessments that fail to capture real-time operational dynamics. In developing economies such as Indonesia, a significant digital gap hinders the adoption of high-cost IoT solutions, leaving safety data fragmented and reactive. This study aims to bridge this gap by developing and validating a Business Intelligence (BI) Safety Dashboard that utilizes bridge technologies, defined as cost-effective digital solutions that leverage existing administrative and operational data instead of dedicated IoT infrastructure, to provide real-time predictive risk insights. Following a Design Science Research (DSR) framework, a three-year longitudinal study (2024–2026) was conducted at a metal fabrication facility in West Java. A Weighted Dynamic Risk Score (WDRS) was formulated using Data Analysis Expressions (DAX), integrating incident logs, maintenance records, and operator certification data into a unified star schema model. The results demonstrate a 95% reduction in data processing time and a 30% increase in near-miss reporting. The proposed artifact successfully identified critical risk outliers, such as Crane 08 (WDRS = 8.3), and generated spatiotemporal heatmaps that pinpointed specific risk hotspots within the facility. These findings confirm that the BI Dashboard is a feasible and highly practical solution for resource-constrained environments, providing a scalable blueprint for Indonesian SMEs to achieve Industry 4.0 safety standards by leveraging existing administrative data for predictive maintenance and proactive safety interventions.
The study explores how integrating AI, the Internet of Things (IoT), and big data analytics can enable the integration of DTs to support real-time operations management, informed decision-making, and the mitigation of vulnerabilities in logistics management.
Hiba Marva Kallidumbil Nachithadathil, Halaa Dhahi Al Waleed Al Maymani, D. Roy· Digital Technologies Researc...· 0 citations
It is demonstrated that intelligent automation increases resilience not through extreme autonomy, but through a combination of forecasting, digital twins, hybrid rules, and managed escalation.
R. V. Khrunichev, D. Orekhvo· EKONOMIKA I UPRAVLENIE: PROB...· 0 citations
Abstract This article examines the applications of artificial intelligence (AI) in risk management process in energy sector and the emergent adoption patterns across subsectors. The paper focuses on practical and observable deployments, building on recent academic work that frames AI as a driver for the energy transition while also highlighting governance, data and cybersecurity concerns. A dataset of 50 AI use-case instances was compiled and assessed, employing a structured content analysis, which were gathered from public data available for leading energy companies. Each case was coded according to four risk management stages (identification, analysis/assessment, prediction and mitigation/treatment), a primary AI capability class (e.g. predictive maintenance, forecasting/optimization, computer vision inspection, digital twins, robotics, neuro linguistic programming (NLP)/Generative AI, cybersecurity analytics) and a dominant risk-control archetype (e.g. reliability control, inspection at scale, resilience forecasting and response optimization, operational decision support).The results suggest that AI is most frequently integrated into mitigation pathways rather than utilized as a standalone analytics layer. There are two primary patterns that emerge: (1) reliability oriented solutions that focus on time-series anomaly detection and predictive maintenance, which connect sensor signals to maintenance actions and (2) resilience and inspection oriented solutions, particularly in utilities and nuclear contexts, that prioritize interventions and reduce exposure to hazards by utilizing forecasting/optimization and computer vision/robotics methods. Subsector comparisons demonstrate domain-specific risk fit: upstream / offshore prioritize reliability control and digital twins; utilities prioritize resilience forecasting and inspection at scale; and refining/downstream prioritize decision support and compliance-linked use cases. The results indicate that the value of AI in risk management is the reduction of the detection to action cycle. Equally, to progress on the automation maturity, it is necessary to improve cybersecurity governance, transparency and validation.
Delia Iacob· Proceedings of the Internati...· 0 citations
The study proposes a layered architecture linking logs, metrics, traces and service dependency graphs with predictive analytics, automation policy and reliability dashboards, and gives a maturity roadmap for organisations that want to transition from reactive monitoring to proactive, self-improving operations.
Mohsin Tahir· Global academic journal of e...· 0 citations
This paper assesses the value-add of AI in safety performance by deeply integrating advanced industrial safety engineering risk analysis methodologies, including Hazard Identification and Risk Assessment (HIRA), Fault Tree Analysis (FTA), and Failure Mode and Effects Analysis (FMEA).
Shruti Pawar and Dr Neeta Banger· International Journal of Adv...· 0 citations
Saudi Arabia's mega-project portfolio is reshaping the operational logic of the built environment. Smart districts, high-rise mixed-use assets, hospitals, transport hubs, hotels and mission-critical public facilities are expected to perform as continuously optimised systems rather than static construction outputs. This review examines how digital twin applications can improve smart building operations, maintenance and risk management in Saudi mega projects under Vision 2030. The paper synthesises peer-reviewed literature from 2020 to 2025 and develops a building lifecycle framework linking BIM, IoT, building management systems, artificial intelligence, facility management platforms and governance controls. The central argument is that a digital twin becomes valuable only when it converts live building data into verified operational decisions: fault detection, energy optimisation, predictive maintenance, safety alerts, resilience planning, asset lifecycle analysis and executive risk visibility. The review identifies four implementation layers: data foundation, analytical intelligence, operational intervention and governance assurance. Findings show that digital twins can reduce fragmentation between design, construction and facility management; however, success depends on data quality, interoperability, cybersecurity, workforce capability, procurement discipline and ethical use of automation. For Saudi mega projects, the strongest value is expected in MEP-intensive assets where energy, cooling, lifts, water systems, fire safety, indoor environmental quality and equipment availability directly affect public value. The paper proposes a practical roadmap for staged adoption, beginning with priority assets and moving toward portfolio-level digital twin governance. It concludes that digital twins should be treated as socio-technical operating systems for smart buildings, not merely 3D visual models.
Ibad Ullah· Global academic journal of e...· 0 citations
