2026· International Journal of Multidisciplinary Research and Growth Evaluation· Vol 7, pp. 203-216· 0 citations
TL;DR
The paper concludes that modern project controls should evolve from retrospective reporting into strategic performance governance, and 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.
Abstract
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.
Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector.
James O. B. Rotimi, Upuli Rasanjani Kaluarachchi Kaluarachchillage· Buildings· 0 citations
Digital transformation has increased organizational reliance on big data analytics (BDA) to support strategic decisions and improve business performance. This study synthesizes evidence on how BDA contributes to strategic decision-making, organizational performance, and innovation through a systematic literature review. The review followed the PRISMA 2020 framework and searched Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language journal and conference publications from 2021 to 2026. After identification, screening, and full-text eligibility assessment, 33 studies were included and examined using thematic analysis. The findings show that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights. BDA is also associated with operational efficiency, project success, organizational agility, customer personalization, competitive advantage, sustainability, and innovation capability. The dominant themes were strategic decision-making, business performance, sustainability and innovation, and artificial intelligence with predictive analytics. However, the literature provides limited evidence on explainable and ethical artificial intelligence, human-AI collaboration, real-time analytics, and BDA adoption among small and medium-sized enterprises and organizations in developing economies. The review contributes an integrated view of BDA as a socio-technical and strategic capability and recommends transparent, scalable, and human-centered analytics governance.
Amelia Contesa, Ilzi Adrolis, Wenni Syafitri et al.· Business System & Innova...· 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
The paper presents a theoretical synthesis on how changes in the financial responsiveness, the operational adaptiveness, and the governance posture of digital enterprises are transformed by AI-enabled cloud ERP, by drawing from a systematic analysis of a curated Scopus-indexed corpus.
Lohgaindran Jeyeselan, Nurul Adha A Rihim, Zakiyah Awang et al.· 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
Big Data Analytics (BDA) has evolved from a predominantly technical batch function into a socio-technical capability integrating cloud-native platforms, stream processing, Lakehouse architecture, machine learning operations (MLOps), visualization, governance, and managerial judgment. This paper proposes an integrated BDA decision-making framework developed through a structured conceptual synthesis of research on data platforms, analytical capabilities, decision processes, organizational readiness, technology adoption, governance, and responsible artificial intelligence. The framework comprises seven interconnected stages: data sources, ingestion and integration, storage and platform, processing, analytics and artificial intelligence, visualization and interpretation, and decision, action, and learning. Governance, human oversight, organizational readiness, task characteristics, and continuous feedback influence all stages. Key implementation requirements include data quality, interoperability, security, privacy, scalability, cost, explainability, bias, skills, and sustainability. The proposed configurable reference architecture links technical integration, task–analytics fit, governance assurance, human judgment, and organizational readiness with decision quality and organizational outcomes. Organizational size and maturity, sectoral risk, decision criticality, technological context, and regulatory environment are defined as boundary conditions for future empirical validation.
W. Alma'aitah, Fatima N. Al-Aswadi, Addy Quraan et al.· Computers· 0 citations
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