This review examines how uncertainty-informed scheduling strengthens the credibility of project time forecasting in complex delivery environments. Its purpose is to assess the limitations of fixed-date planning and to explain how probabilistic analysis improves understanding of completion risk, float consumption, and critical-path instability. The study adopts a narrative review approach, synthesising scholarly and professional literature on deterministic scheduling, schedule risk modelling, Monte Carlo simulation, network sensitivity, governance, and data-led project control. Emphasis is placed on how probability-based outputs can support more defensible planning, contingency allocation, and executive decision-making in uncertain operating contexts.
The review finds that deterministic schedules remain necessary for defining work logic, dependencies, milestones, and baseline accountability, but they are analytically constrained when used as instruments of prediction. Single-point duration estimates, static critical paths, and nominal float values often conceal uncertainty arising from procurement delays, productivity variation, design development, resource limitations, stakeholder interfaces, and systemic operational risk. Monte Carlo simulation is shown to provide a more rigorous basis for schedule assurance by generating completion-date distributions, confidence levels, sensitivity rankings, and risk-driver insights. The study further establishes that float is not a permanent reserve but a dynamic network property that may erode rapidly as near-critical paths emerge and project assumptions change.
The review concludes that probability-based schedule analysis should be institutionalised as a governance practice rather than treated as an optional technical exercise. It recommends improved schedule-quality assurance, transparent modelling assumptions, sensitivity-based monitoring, integration of real-time performance data, and stronger executive capacity to interpret probabilistic evidence. These measures can enhance schedule realism, strengthen accountability, and improve delivery confidence across complex projects.
Dominic Feboh, Abeebat Ajirotutu, Ogochukwu T Izuchukwu· International Journal of Mul...· 0 citations
This review examines the architectural, technological and organisational conditions required to develop scalable extract, transform and load pipelines for real-time transaction analytics. Its purpose is to clarify how contemporary data-engineering capabilities can be aligned with operational decision-making across transaction-intensive enterprises. The study adopts a structured narrative review of scholarly and technical literature on batch, micro-batch, stream-processing, Lambda, Kappa, event-driven, cloud-native, lakehouse and serverless architectures, with additional attention to governance, security, observability, resilience and emerging-economy implementation contexts.
The findings indicate that no single architectural model is universally optimal. Batch processing remains valuable for reconciliation, regulatory reporting and historical analysis, whereas stream-oriented and event-driven designs are better suited to fraud detection, payment monitoring, inventory visibility and other latency-sensitive operations. Hybrid architectures offer the strongest balance between speed, correctness, recoverability and cost. The review further finds that scalability depends not only on distributed computing, but also on partitioning, state management, change data capture, automated testing, lineage, data contracts, quality controls and service-level objectives. Organisational alignment, cross-functional ownership and regulatory compliance are equally decisive in determining whether technical capability produces measurable business value.
The study concludes that real-time analytical performance must be evaluated through both engineering and operational outcomes. It recommends use-case-driven architectural selection, resilient hybrid deployment, embedded security and governance, automated quality assurance, transparent AI-assisted pipeline management and stronger collaboration between technical and business teams. Future research should develop standardised benchmarks combining latency, accuracy, resilience, cost, sustainability and operational impact, while giving greater attention to infrastructure-constrained and emerging-economy environments. These priorities are essential for building data infrastructures capable of supporting responsive, evidence-based enterprise operations at scale.
Ogochukwu T Izuchukwu, Dominic Feboh, Ayokunle Olamide Ijagbemi et al.· International Journal of Mul...· 0 citations
This study examines how universities can govern cybersecurity, privacy, and healthcare information within increasingly interconnected digital environments. Its purpose is to clarify how ISO/IEC 27001, the General Data Protection Regulation, and the Health Insurance Portability and Accountability Act can be aligned without obscuring their distinct legal and operational requirements. A structured narrative review was undertaken using peer-reviewed literature, recognised standards, regulatory guidance, and relevant institutional studies published up to 2023. The analysis focused on smart campus architecture, data flows, cyber-risk exposure, information security management, privacy accountability, healthcare data protection, regulatory interoperability, governance barriers, and emerging technologies.
The findings indicate that ISO/IEC 27001 provides an effective institutional backbone for risk management, leadership accountability, control assurance, and continual improvement. However, GDPR introduces broader obligations relating to lawful processing, transparency, data-subject rights, and privacy by design, while HIPAA imposes specialised safeguards for protected health information within covered university healthcare functions. Significant convergence exists in access control, incident response, supplier oversight, documentation, workforce training, and continuous monitoring, yet divergence remains in legal scope, enforcement, individual rights, and breach obligations. The review further identifies fragmented authority, legacy infrastructure, shadow systems, cross-border processing, third-party dependence, and emerging technologies as major governance challenges.
The study concludes that universities require a layered, integrated governance model rather than separate compliance silos. It recommends multidisciplinary oversight, harmonised control catalogues, precise data classification, Zero Trust access, privacy and security by design, recurring impact assessments, supplier accountability, and measurable assurance. It also emphasises ethical governance of artificial intelligence, analytics, and connected infrastructure. Future research should empirically evaluate integrated models across jurisdictions, institutional types, and resource-constrained settings.
Dominic Feboh, Ayokunle Olamide Ijagbemi, Stanley Nwakamma et al.· International Journal of Mul...· 0 citations
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 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.
Ogochukwu T Izuchukwu, Dominic Feboh, Ayokunle Olamide Ijagbemi et al.· International Journal of Mul...· 0 citations
