2020· International Journal of Multidisciplinary Research and Growth Evaluation· 0 citations
Abstract
The increasing complexity of built-asset operations, rising service costs, and persistent efficiency constraints in emerging economies have intensified the need for more intelligent approaches to facility and property management. This study critically examines how digital systems and business intelligence capabilities can improve cost control, operational visibility, asset performance, and managerial decision-making across diverse property environments. A structured narrative review was adopted, drawing on interdisciplinary literature from facility management, real estate, construction, information systems, energy management, and business analytics, with particular attention to evidence from developing and emerging-market contexts.
The review finds that integrated data platforms, building information modelling, sensor-enabled monitoring, predictive analytics, digital dashboards, and automated reporting can reduce expenditure through energy optimisation, preventive maintenance, improved space utilisation, stronger procurement control, and more accurate lifecycle planning. It further reveals that these benefits depend on reliable data, interoperable systems, workforce competence, executive sponsorship, cybersecurity, and effective governance. Major barriers include fragmented records, inadequate infrastructure, high implementation costs, limited technical skills, weak standards, resistance to organisational change, and insufficient alignment between technology investments and operational priorities.
The study concludes that sustainable value is most likely when implementation is phased, problem-led, and supported by measurable performance baselines. Organisations should prioritise high-cost areas, strengthen data governance, develop staff capability, and scale technologies only after verified results. Policymakers and professional bodies should promote common standards, digital handover protocols, cybersecurity guidance, and sector-specific training. Future research should emphasise longitudinal evaluation, comparative regional studies, and locally calibrated models capable of assessing artificial intelligence, digital twins, and cloud-based solutions under emerging-market conditions. These measures can improve resilience, accountability, service quality, and long-term value.
This paper examines how contemporary project-control practice is being transformed by the integration of Earned Value Management, Business Intelligence, predictive analytics, and real-time performance visualization. Its purpose is to clarify how these interrelated capabilities strengthen cost, schedule, risk, resource, and governance visibility in complex project environments. The study adopts a conceptual review approach, drawing on scholarly and professional literature on project controls, performance measurement, analytics, data architecture, cybersecurity, forecasting, and digital transformation. Through this approach, the paper synthesises existing knowledge to explain the technical, organisational, and strategic conditions required for project-control systems to become more predictive, transparent, and decision-oriented.
The review finds that Earned Value Management remains a foundational framework for measuring cost and schedule performance, but its value increases substantially when integrated with Business Intelligence platforms and visual analytics. Business Intelligence enables the consolidation of dispersed project data, while real-time dashboards translate complex indicators into accessible intelligence for managers, executives, contractors, and other stakeholders. The study further finds that predictive analytics and early-warning systems can improve forecasting accuracy, reveal emerging delivery risks, and support corrective action before deviations become irreversible. However, these benefits depend on reliable data architecture, strong governance, cybersecurity assurance, employee readiness, and a culture that supports evidence-based decision-making.
The paper concludes that modern project controls should evolve from retrospective reporting into strategic performance governance. It recommends that organisations standardise control metrics, invest in interoperable digital systems, strengthen data governance, develop analytical competencies, and embed human-centred automation into formal decision processes to improve delivery certainty and organisational accountability across diverse, data-intensive project-based organisations and institutional delivery contexts.
Ogochukwu T Izuchukwu, Dominic Feboh, Abeebat Ajirotutu· International Journal of Mul...· 0 citations
The accelerating pace of digital transformation has fundamentally redefined the strategic role of corporate finance. Financial management is no longer confined to budgeting, reporting, and capital allocation; it has evolved into an intelligent decision-making function that integrates artificial intelligence, automation, advanced analytics, and real-time digital information to support enterprise-wide strategy. As organizations operate within increasingly volatile economic environments characterized by technological disruption, geopolitical uncertainty, regulatory complexity, and rapidly changing customer expectations, traditional financial planning models have become insufficient for sustaining long-term competitiveness. Static financial systems, historical reporting practices, and periodic decision cycles are gradually being replaced by adaptive financial architectures capable of generating predictive insights, supporting continuous planning, and enabling executives to make faster and more informed strategic decisions.
This article examines how digital transformation is reshaping financial strategy through the integration of artificial intelligence, intelligent automation, predictive analytics, and executive decision intelligence. Rather than treating digital technologies as isolated operational tools, the study argues that they collectively represent a new strategic infrastructure that transforms finance into an organizational intelligence center. The discussion explores the evolution of financial strategy from retrospective financial control toward predictive and autonomous decision support while analyzing the growing importance of data governance, digital leadership, organizational resilience, cybersecurity, and ethical AI in financial management. Particular attention is given to the changing role of executive leadership, where Chief Financial Officers increasingly function as strategic architects responsible for integrating technological innovation with long-term value creation. The article further proposes a comprehensive framework for intelligent financial strategy that combines human expertise with machine intelligence to strengthen organizational adaptability and sustainable competitive advantage. The findings suggest that organizations capable of integrating AI-driven financial intelligence with strategic executive leadership are better positioned to improve decision quality, enhance financial resilience, accelerate innovation, and create enduring enterprise value in the digital economy.
Efe Calguner· International Journal of Res...· 0 citations
A Data-Driven Operations Synchronization Stack is proposed that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture in high-throughput manufacturing contexts.
A. Y. I. ElGabroni, Paulo Peças· Systems· 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
Building Information Modelling (BIM) is increasingly positioned as a digital information environment for facilities management (FM), asset management and operation and maintenance (O&M) decision-making. Its operational value, however, remains constrained by fragmented information exchange, incomplete asset information, inconsistent requirements, limited facility manager involvement and weak integration between BIM and FM systems. This paper presents a bounded, corpus-based systematic literature review of 51 documents covering BIM-FM integration, asset information delivery, Industry 4.0 technologies, digital twins and visual analytics. The starting repository was pre-assembled rather than generated through a new bibliographic database search. To improve methodological transparency, the review applies explicit eligibility criteria, a consolidated Boolean verification string, a documented corpus-selection process and qualitative thematic synthesis. Four themes emerged: interoperability and information exchange; asset information quality, data requirements and early FM involvement; BIM convergence with Industry 4.0 technologies and digital twin-enabled FM; and visual analytics, systems-centric modelling and fault diagnosis. The synthesis also distinguishes the implementation emphasis reported in developed-country and developing-country contexts. The paper contributes a provisional, literature-derived seven-stage framework for BIM-enabled data integration and visual analytics in FM. The framework has not yet been validated through practitioner or expert assessment and is therefore presented as a testable conceptual proposition rather than an implementation-ready model. The findings are intended to support bounded theoretical synthesis and future empirical validation, not statistical generalisation across the entire BIM-FM domain.
Zarith Anisa Idris, M. Awang, N. Hamidon et al.· International journal of res...· 0 citations
The study concludes that enterprises should treat automation and data governance as an integrated strategic agenda rather than as separate technical initiatives and recommends governance-by-design, phased implementation, workforce reskilling, periodic maturity and impact assessments, stronger model and data inventories, and independent assurance for high-risk systems.
Bisola AkejuQ, Ayokunle Olamide Ijagbemi, Shalom Alugwe· International Journal of Mul...· 0 citations
