Saudi Arabia is increasing renewable energy generation, electrifying its systems and implementing digital technologies in the grid, all against a backdrop of a climate in which summer temperatures, dust, humidity and other co-occurring environmental stresses simultaneously raise demand and degrade the electricity infrastructure. This study synthesizes the evidence published between 2020 and 2025 to examine how climate-resilient smart-grid planning can help reduce these interconnected risks. A structured integrative review was carried out by selecting a purposeful collection of peer-reviewed studies and official energy and climate reports, with a focus on Saudi Arabia, the Gulf Cooperation Council, arid power systems, and methods of resistance that can be transferred to other contexts. The evidence was analysed along four dimensions: hazard, exposure, vulnerability, and adaptive capacity, and then linked to the areas of generation, transmission, distribution, demand, storage, digital control, and governance. The synthesis reveals that extreme heat is not simply a single issue relating to equipment; it causes cooling peaks at the same time, reduces both generation and network capacity, accelerates thermal ageing, and can occur alongside photovoltaic soiling, constraints on water use, and communication or cyber-physical failures. The most effective approach to planning therefore involves a multi-layered system that incorporates climate-based load forecasting, dynamic asset ratings, heat-resistant equipment, a varied geographical mix of renewable energy sources, storage, demand response, microgrids for critical loads, advanced metering, artificial intelligence, and regional interconnection. The review suggests a planning framework specific to Saudi Arabia that takes into account resilience together with cost and decarbonisation, using measures such as critical-load continuity, energy not served, restoration time, thermal headroom, and forecast error in severe situations. It finds that climate resilience should be integrated into long-term capacity expansion, grid codes, asset standards and operational digital twins, rather than an add-on for emergency management.
Government and enterprise applications increasingly depend on cloud platforms for elasticity, rapid service delivery, analytics and digital integration, yet mission-critical workloads cannot be optimized through a simple public-cloud-first migration. They require deliberate placement across private, public, sovereign and on-premise environments according to service-level objectives, data sensitivity, latency, dependency and recovery requirements. This review examines how hybrid cloud architecture can be optimized for mission-critical applications in Saudi Arabia. It synthesizes literature on multi-cloud orchestration, workload portability, containerized resource management, observability, cybersecurity and resilience, and interprets these findings against Saudi Cloud First policy, Digital Government Authority guidance, Communications, Space and Technology Commission regulations, National Cybersecurity Authority controls and Vision 2030 priorities. The analysis finds that hybrid cloud value depends less on the number of connected platforms than on unified policy, application-aware placement, automated orchestration, end-to-end observability and tested continuity. A Saudi-oriented reference model is proposed in which governance and data classification drive placement decisions, while cloud-native portability, automation and resilience controls sustain service continuity. The review concludes that hybrid cloud can support Vision 2030 digital-government and enterprise goals when architecture is treated as a governed operating model rather than a collection of disconnected infrastructures.
Mohammad Younus
Glob Acad J Econ Buss; 2026, 8(5): 1087-1098
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.003
The vast number of projects carried out by Saudi Arabia involve a great deal of construction, a wide range of different kinds of buildings, modern building services, and very strict deadlines for completion. Under these circumstances, fire safety resilience depends not only on the design complying with the relevant codes but also on the systems installed being clearly complete, capable of working together, and able to be restored before the projects are handed over. This study treats inspection, testing and commissioning (ITC) as a continuous process for making sure that the fire protection and life-safety systems work in Saudi giga-projects. A structured and integrative review was carried out in accordance with the PRISMA 2020 reporting guidelines and included peer-reviewed studies, standards and Saudi policy documents published between 2020 and 2025. The evidence was divided into five themes: the quality of inspection, the functional testing of subsystems, integrated system testing, digital verification and lifecycle governance. The review shows that resilience is reduced when verification is left to the individual contractors, when the documentation is fragmented, when interface tests are delayed, or when acceptance is based on the activation of components rather than on the system's performance in a given scenario. On the other hand, risk-based inspection, functional testing that is witnessed, integrated sequences covering alarm systems, smoke control, emergency power and egress systems, and digitally traceable evidence all increase the likelihood that the safety functions will operate if a real fire takes place. Even though digital twins, IoT sensing, computer vision and BIM can improve traceability and situational awareness, they do not substitute for competent inspection or independent witnessing. The paper presents a seven-stage Fire Safety Resilience Assurance Framework which is in line with the Saudi Fire Protection Code and with current NFPA commissioning practices. The framework unites the design intent, the verification of installation, pre-functional checks, functional tests, integrated scenarios, handover evidence and periodic retesting. It provides owners, regulatory authorities, commissioning teams and contractors with a flexible approach for reducing latent defects and for guaranteeing operational dependability in the context of complex Vision 2030 developments.
Mohammed Juned Nijami
Glob Acad J Econ Buss; 2026, 8(5): 1099-1110
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.004
At present, intelligent oil and gas facilities are using safety instrumented systems (SIS), distributed control systems (DCS), connected field instrumentation, historians, edge computing, and advanced analytics. Although the large amount of data generated can improve process safety, this benefit can only be achieved if the analytical integration still satisfies the required standards concerning independence, determinism, and assurance for safety functions. This article looks at how the SIS, the DCS, and the real-time instrumentation analytics can be combined so as to establish an evidence architecture that helps in the early detection of abnormal conditions, the evaluation of barrier health, the contextuation of alarms, and the provision of risk-informed support for human decision-making. A structured integrative review has been carried out in line with the PRISMA 2020 and PRISMA-S reporting guidelines in order to summarise the peer-reviewed and credible literature published between 2020 and 2025. The evidence is divided into five areas: functional-safety architecture, control and alarm management, instrumentation data quality, fault diagnosis and dynamic risk analytics, and cyber-human assurance. The synthesis shows that the most defensible design is not one in which the analytics control the SIS, but rather a layered architecture in which both the SIS and the DCS keep their present operational roles while authenticated, time-aligned, read-only data are sent to an independent analytics environment. This setup allows for predictive and contextual insights absent creating an unverified actuation path into the protection layers. The paper suggests an assured integration architecture and a five-level implementation maturity model. It concludes that the future value for process safety will depend less on the novelty of the algorithms themselves and more on traceable measurements, validated models, secure interfaces, explainable outputs, disciplined management of change, and clear human authority over safety-critical decisions.
Khaleel Anwar Ali Khan
Glob Acad J Econ Buss; 2026, 8(5): 1111-1123
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.005
The health sector's programme for transformation in Saudi Arabia needs a medical supplies system which can provide the correct product, in the right condition, at the right place, together with reliable information concerning cost and traceability. This study looks at enterprise resource planning as the core element for improving procurement, inventory management, warehousing, distribution, supplier management, and financial control within the Kingdom's healthcare and medical supplies sector. A structured integrative review of 30 sources published between 2020 and 2025 was carried out, with a focus on peer-reviewed literature relating to healthcare supply chains, ERP systems, digital transformation, resilience, analytics, and the Saudi health system, as well as on official Vision 2030 and Ministry of Health documents. The findings show that the value of ERP is mainly derived not from the installation of software but from standardised master data, end-to-end workflows, interoperable interfaces, well-disciplined inventory policies, and timely analytics. It also appears that artificial intelligence, the Internet of Things, blockchain, and business analytics generate greater operational value when they are linked to a stable ERP system rather than when they are introduced as separate applications. For Saudi Arabia, the most important fields for improvement are integrated demand planning, visibility into supplier performance, control of batches and expiry dates, automated replenishment, readiness for recalls, and transparency of spending across both central and provider-level networks. The review suggests a framework for optimisation that is aligned with Vision 2030 and links ERP capabilities to process mechanisms, measurable supply outcomes, and the objectives of the healthcare system. It concludes that a transformation centred on ERP can improve availability, spending efficiency, resilience, localisation, and governance, provided that the implementation is supported by data governance, interoperability standards, workforce development, cybersecurity, and continuous performance management.
Saudi Arabia’s electronic invoicing programme has converted tax invoicing from a document-generation task into a regulated, API-mediated financial process. For enterprises running Oracle ERP, compliance therefore depends on more than producing a valid invoice: data must be transformed into the prescribed XML structure, validated against business rules, secured with approved credentials, submitted to the FATOORA platform within the applicable clearance or reporting model, reconciled with the ERP ledger, and retained with auditable evidence. This review examines how a cloud-native architecture can integrate Oracle ERP, ZATCA interfaces and automated financial controls while supporting the Kingdom’s wider digital transformation agenda. Evidence published between 2020 and 2025 is synthesised across electronic invoicing, cloud ERP, API security, continuous auditing, digital tax administration and Saudi regulatory guidance. The review develops an architecture organised around transaction capture, canonical data mapping, validation, security, API orchestration, response handling, immutable evidence, reconciliation and control monitoring. It argues that compliance reliability is improved when ZATCA rules are treated as executable control requirements rather than downstream reporting checks. The proposed model separates enterprise accounting logic from regulatory integration, supports resilient retries without duplicate submission, and creates traceable links between source transactions, signed XML, platform responses and accounting entries. The paper concludes that cloud-native integration can strengthen compliance, scalability and auditability, but only where enterprises govern master data, credentials, exception handling, segregation of duties and change management as tightly as the integration code itself.
Advanced manufacturing in Saudi Arabia is moving from capacity expansion toward digitally enabled productivity, reliability, localization, and resource efficiency. This review examines how four complementary mechanisms—predictive maintenance, Overall Equipment Effectiveness (OEE), digital twins, and Lean Six Sigma—can be integrated to optimize advanced manufacturing machinery under the industrial priorities of Saudi Vision 2030. A structured integrative review of literature published from 2020 to 2025 was undertaken across smart manufacturing, maintenance engineering, production performance, digital-twin applications, and operational excellence, with Saudi industrial strategy documents used to contextualize implementation. The synthesis shows that predictive maintenance improves failure anticipation but requires disciplined data quality and maintenance workflows; OEE converts equipment losses into an operational performance language but can mislead when treated as an isolated utilization target; digital twins create a dynamic decision layer linking physical assets with simulation and prognostics; and Lean Six Sigma converts diagnostic insight into controlled, repeatable improvement. Their greatest value therefore emerges through integration rather than parallel deployment. The paper develops a closed-loop reliability-performance framework and a staged maturity roadmap for Saudi factories, emphasizing brownfield connectivity, criticality-based sensing, interoperable data architecture, workforce capability, cybersecurity, and value-based governance. The proposed approach supports higher asset availability, stable cycle performance, lower quality loss, and stronger local manufacturing capability while avoiding technology-led transformation without measurable operational return.
Enterprise business intelligence has traditionally depended on dashboards that visualize historical and near-real-time data for human interpretation. Although dashboards remain essential for transparency and performance monitoring, they often require decision makers to detect anomalies, interpret root causes, and translate insights into action. This paper proposes an agentic artificial intelligence framework for enterprise business intelligence modernization, shifting BI from passive reporting toward autonomous, governed, and decision-oriented intelligence. The study uses a conceptual design methodology supported by literature on agentic AI, enterprise analytics, decision intelligence, and national digital transformation priorities. The proposed framework integrates five layers: unified data foundation, semantic and KPI governance, agent orchestration, human-in-the-loop decision control, and performance assurance. It explains how AI agents can monitor business metrics, detect exceptions, generate contextual explanations, recommend actions, and support executive decision-making while preserving governance, accountability, and responsible AI controls. From a business and economic perspective, the framework links analytics modernization to productivity improvement, cost optimization, working-capital visibility, faster management response, and measurable return on AI investment. The framework is positioned for Saudi enterprises aligned with Vision 2030, where data and AI are central enablers of economic diversification, operational efficiency, and digital maturity. The paper contributes a practical modernization model for organizations seeking to evolve from descriptive dashboards to agentic intelligence ecosystems.
Saudi Arabia is enlarging its pharmaceutical and biotechnology base as part of a wider retreat from import dependence. For advanced drug-delivery products, however, adding capacity is not the same as adding capability. Performance in these products is governed by particle size, polymer microstructure, mixing history, sterile handling, device geometry, release kinetics and analytical control, and these variables prove stubbornly difficult to reproduce once a manufacturing site changes. This review examines how technology transfer and process scale-up must be combined if advanced drug-delivery manufacture is to take root in the Kingdom, covering modified-release oral products, hot-melt-extruded systems, lipid and polymeric nanoparticles, long-acting injectables, microneedle platforms and digitally manufactured dosage forms. A structured narrative method was used to assemble peer-reviewed evidence published between 2020 and 2025 together with the quality frameworks issued in that period. The Kingdom's starting position is quantified rather than asserted: analysis of the Saudi Food and Drug Authority formulary shows that 22.55% of registered items are manufactured domestically against 43.86% originating in Europe, that four producers account for 63% of domestic output, and that approximately 86% of surveyed national manufacturers cannot at present enable nanotechnology-based medicines even though more than 93% regard such products as important. Against this baseline the review argues that localization should be treated as a capability-transfer programme rather than an equipment-purchase exercise, requiring a product-specific knowledge package covering critical material attributes, critical process parameters, critical quality attributes, analytical procedures, process models and change-control logic. A phased pathway is proposed in which secondary operations and sterile fill-and-finish establish a learning base, followed by formulation ownership, platform manufacturing, advanced process control and regional export.
Muhammad Anas
Glob Acad J Econ Buss; 2026, 8(5): 1164-1172
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.010
Saudi Arabia’s electricity system is undergoing a capital-intensive transition, in which efficiency improvements at existing and new generating assets must compete for funding with renewables, grids, storage, and gas infrastructure. This review explores how financing design is able to transform technically credible energy-efficiency opportunities in power generation into investable projects aligned with Vision 2030. Rather than viewing efficiency solely as an engineering issue, the paper integrates evidence on heat-rate improvement, combined-cycle optimisation, digital operation and maintenance, auxiliary-load reduction, flexible generation, energy-performance contracting, green bonds, sukuk, and blended finance. A structured review of peer-reviewed literature from 2020 to 2025 links technical savings to risk allocation, cash-flow certainty, and cost of capital. The findings show that financing quality significantly affects the bankability of efficiency projects, as verified savings are often distributed across fuel, maintenance, reliability, and capacity benefits rather than consolidated in a single revenue stream. In Saudi Arabia, the most compelling near-term approach is to aggregate repeatable efficiency measures across generation portfolios, apply transparent baselines and performance guarantees, and connect refinancing to verified outcomes. The review concludes that energy efficiency should be considered productive infrastructure: a financing category that can reduce system costs, support renewable integration, conserve fuels, and improve the credibility of transition investments under Vision 2030.
Saudi Arabia’s construction pipeline creates a practical resource-management challenge: project teams must reduce dependence on virgin aggregates and clinker-intensive products while controlling the quality, cost, durability and traceability risks associated with secondary materials. This review therefore moves beyond a general catalogue of recycled construction materials and develops a project-level implementation framework for Saudi building and infrastructure delivery. Thirty publications from 2020–2025 were synthesized around five decision stages: source identification, material qualification, application selection, procurement and approval, and site production control. Evidence on recycled concrete aggregate (RCA), reclaimed components, waste glass, plastics, steel slag, supplementary cementitious materials, geopolymer systems and recycled water is translated into practical acceptance gates for design, materials engineering, procurement, QA/QC and construction teams. The review shows that recycled content alone is a weak decision metric. For Saudi projects, the more defensible approach is to match the secondary material to a defined application and exposure class, verify source quality and contamination limits, validate trial mixtures or product performance, control moisture and batch variability, and document environmental and economic benefit at the functional-unit level. A role-based implementation matrix, stage-gate approval protocol and project KPI set are proposed to support real project execution under Vision 2030. The resulting framework is intended to help project teams convert circular-economy objectives into auditable material approvals, procurement decisions, inspection points and measurable resource-efficiency outcomes rather than treating recycling as a stand-alone sustainability claim.
Essam Ali Hassan Abdelaal
Glob Acad J Econ Buss; 2026, 8(5): 1185-1195
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.012
Saudi Arabia's economic diversification and rapid digitalization have increased strategic risks linked to fragmented governance, risk management, and compliance. This integrative review examines how governance, risk, and compliance (GRC) can be structured as an organizational resilience capability within the Vision 2030 framework, rather than as separate control functions. The review synthesizes peer-reviewed research on enterprise risk management, corporate governance, compliance, digital transformation, cybersecurity, RegTech, and organizational resilience, focusing on evidence relevant to Saudi institutions and technology-intensive sectors. A transparent search and quality-assessment protocol prioritizes literature from 2020 to 2025, while retaining key foundational studies and a recent Saudi empirical anchor. The synthesis shows that resilience develops when governance defines decision rights and risk appetite, risk management translates uncertainty into prioritized action, compliance ensures reliable control behavior, and digital GRC enables timely, shared intelligence. These mechanisms strengthen anticipatory capacity, disruption absorption, adaptive coordination, recovery, and organizational renewal. However, integration may fail if GRC becomes overly centralized, metric-driven, technologically opaque, or disconnected from business ownership. Saudi evidence indicates that governance reform, ESG reporting, foreign investment, cyber governance, and digital transformation create mutually reinforcing pressures for integrated assurance. This paper presents a GRC-to-resilience framework and a four-stage maturity model for Saudi organizations. It concludes that resilience relies more on the integration of GRC into strategy, operations, data architecture, learning routines, and board oversight than on the mere existence of GRC functions. The review identifies research priorities including causal measurement, sector comparisons, AI-enabled GRC, regulatory interoperability, and resilience outcomes.
Saudi Arabia's capital market is moving from scale expansion toward deeper institutionalization, digital participation, and sophisticated trading infrastructure. Brokerage firms sit at a critical interface between investor intentions and exchange-level outcomes. This review examines how advanced decision analytics can improve brokerage operations while supporting liquidity, price discovery, resilience, and investor confidence under Vision 2030. A structured integrative review synthesized 30 peer-reviewed and institutional sources published from 2020 to 2025 across market microstructure, algorithmic and high-frequency trading, machine learning, reinforcement learning, financial risk analytics, securities regulation, and Saudi market development. Five analytics domains emerge: client and order intelligence, execution optimization, liquidity and market-making analytics, real-time risk and compliance, and operational resilience. Evidence indicates that analytics can reduce implementation shortfall, improve order routing and inventory control, and accelerate information incorporation into prices; however, benefits depend on market design, liquidity regimes, model governance, and automated participant behavior. Saudi evidence is timely because algorithmic and high-frequency traders now represent material shares of liquidity while market-making and post-trade infrastructure are expanding. The paper proposes a Saudi Brokerage Decision Analytics Framework linking governed data, predictive and prescriptive models, human oversight, execution controls, and market-quality metrics. Decision analytics should therefore be treated as a governed market capability whose success is measured jointly by brokerage efficiency and market integrity.
Cloud databases are now foundational to enterprise digital operations; however, their elasticity and distributed architecture increase the complexity of manual administration. This review investigates how artificial intelligence can facilitate bounded autonomy in cloud database management to enhance performance, reliability, and operational efficiency, with a specific focus on Saudi enterprises. A structured integrative review synthesizes peer-reviewed literature published primarily between 2020 and 2025, spanning database systems, cloud resource management, anomaly diagnosis, autonomous tuning, and Saudi cloud adoption. Learned query optimization and cardinality estimation improve execution decisions; reinforcement and Bayesian learning accelerate configuration tuning; workload-aware resource orchestration manages latency and utilization; and AI-supported monitoring reduces diagnosis and recovery cycles. Nevertheless, the literature reveals persistent limitations: model transfer across workloads is unreliable, optimization objectives are often narrower than enterprise service-level requirements, online exploration can introduce availability risks, and most evaluations are laboratory-based rather than longitudinal production studies. Saudi adoption studies further demonstrate that security, privacy, trust, and organizational readiness influence cloud decisions, indicating that technically advanced autonomy will not be adopted without auditable controls and clear accountability. Accordingly, this review proposes a closed-loop architecture in which telemetry, AI decision engines, policy constraints, database control actions, and human oversight function as a governed feedback system. The central conclusion is that autonomous cloud database management should be implemented as progressively delegated, observable, and reversible decision-making, rather than as unrestricted automation.
Building Information Modelling (BIM) is changing quantity surveying from document-based measurement toward model-based, data-rich commercial management. This review examines how BIM-enabled quantity surveying can automate quantity take-off, strengthen cost estimation, improve change control and support faster commercial decision-making, with particular relevance to Saudi Arabia’s rapidly expanding construction and infrastructure market. An integrative review approach was adopted, drawing on recent peer-reviewed research on 5D BIM, automated quantity take-off, model-based cost control, machine-learning-supported estimating, BIM adoption and Saudi construction management. The evidence indicates that BIM can improve measurement speed, traceability, visualization and consistency, but automation is not inherently reliable. Quantity outputs depend on model completeness, classification, level of information need, measurement rules, object parameters and disciplined model governance. The review therefore distinguishes between automated extraction and professionally validated measurement. It also shows that 5D BIM creates greater value when quantities are connected to cost libraries, work breakdown structures, procurement packages, change records, progress data and forecast-at-completion processes rather than being used only to produce an initial bill of quantities. A BIM-enabled Commercial Intelligence Framework is proposed that integrates model assurance, automated measurement, cost intelligence, change management, risk and dashboard-based decision support across project stages. In the Saudi context, this approach can support Vision 2030 objectives by improving transparency and commercial control on complex programs while developing digitally capable quantity-surveying teams. The paper concludes that the future quantity surveyor is not displaced by automation; instead, professional value shifts toward data assurance, commercial interpretation, scenario analysis, contractual judgment and governance of model-based decisions.
Offshore flowlines connect subsea wells, manifolds, and processing facilities, but any loss of containment can quickly escalate into environmental, operational, or reputational incidents. This review explores how smart leakage detection can mitigate environmental risk in Saudi Arabia’s offshore oil and gas flowlines by integrating process-based monitoring, distributed sensing, subsea robotics, machine learning, and environmental surveillance. A structured integrative review of recent peer-reviewed studies assessed leak initiation, signal discrimination, localization, environmental consequences, and integrity decision-making. Findings indicate that no single technology meets all requirements for early detection, low false-alarm rates, accurate localization, multiphase tolerance, and subsea maintainability. Internal methods are cost-effective and scalable but susceptible to instrumentation uncertainty and operational transients. External acoustic and fiber-optic methods enhance local sensitivity but depend on appropriate deployment and robust data interpretation. Machine learning improves discrimination when trained on representative field data, while digital twins link detection outputs to asset condition, consequences, and intervention strategies. For Saudi offshore environments, a layered, risk-based architecture is most effective: process analytics should provide broad surveillance, distributed or point sensing should protect critical segments, and mobile or satellite observations should verify uncertain events and support environmental response. The review recommends a confidence-rated decision pathway connecting detection to isolation, verification, environmental assessment, and integrity learning. Research priorities include validated multiphase datasets, small-leak field trials, uncertainty-aware fusion, cyber-resilient subsea sensing, and performance metrics focused on avoided releases rather than alarm accuracy alone.
Abdul Rehman Barok
Glob Acad J Econ Buss; 2026, 8(5): 1241-1251
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.017
Artificial intelligence is gradually transitioning from being a tool used in the classroom to becoming an organizational capability that affects the way schools interpret evidence, distribute their attention, and coordinate their efforts for improvement. This study looks at how artificial intelligence can help leaders in Saudi secondary schools make data-driven decisions while at the same time keeping professional judgment, equity, and accountability intact. Unlike the reference study mentioned which looks at predictive models for academic outcomes in Saudi higher education, the present paper concentrates on leadership decisions and the organizational factors that make algorithmic evidence useful in the context of secondary schooling. Using a structured integrative review guided by the PRISMA 2020 principles, the review brings together peer-reviewed literature published between 2020 and 2025 as well as relevant Saudi and international policy documents. The evidence is arranged around four questions: which leadership decisions can make use of AI-powered analytics; what capabilities are needed to turn data into action; what ethical and regulatory protections are necessary; and how the introduction of AI can be carried out in the Saudi setting. The synthesis shows that AI is most useful when it aids in the interpretation of early-warning signals, in monitoring instruction, in prioritising resources, in analysing the school climate, and in forecasting operations, rather than taking over leaders' decisions. The benefits depend on the quality of the data, the level of data literacy among staff, the existence of collaborative practices, the explainability of the results, and clear governance. The paper suggests a human-centered decision cycle for secondary schools in Saudi Arabia, connecting the definition of purpose, data stewardship, interpretable analysis, deliberation, action, and monitoring. The review ends by stating that leadership capacity and the level of maturity of governance are more important in ensuring responsible value than the complexity of the algorithms alone.
Syed Mohammad Ali
Glob Acad J Econ Buss; 2026, 8(5): 1252-1264
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.018
Saudi Arabia’s Vision 2030 places industrial development, localization, non-oil growth and stronger participation in regional and global supply chains at the center of economic transformation. However, localization policies can produce limited domestic value when they focus only on final assembly, first-tier procurement or aggregate import substitution. Sustainable industrial diversification requires a deeper understanding of how value is distributed across upstream inputs, intermediate processing, core manufacturing, industrial services and downstream activities. This paper develops an industrial value-chain analytics framework for identifying and prioritizing localization opportunities in Saudi Arabia. Using a structured review and conceptual-framework methodology, the study integrates recent literature on global value chains, industrial upgrading, supply-chain resilience, Industry 4.0 and economic diversification with official Saudi Vision 2030, National Industrial Strategy and GASTAT evidence. The framework combines five analytical stages: market and trade screening, value-chain decomposition, domestic capability mapping, gap and linkage analytics, and opportunity prioritization. It proposes a Value-Chain Localization Potential Index that assesses domestic value capture, market attractiveness, capability readiness, linkage intensity, strategic importance and export potential. The analysis demonstrates that localization should be evaluated according to depth and connectivity rather than the location of final production alone. Upstream materials, components, tooling, engineering, maintenance, testing, logistics and downstream services can generate substantial economic spillovers even when complete product localization is not feasible. The proposed approach supports Saudi Vision 2030 by helping policymakers, industrial organizations and investors identify value-chain nodes that can strengthen supplier ecosystems, increase domestic value added, attract technology-intensive investment, improve resilience and support non-oil exports. The paper concludes that data-driven value-chain analytics can help Saudi Arabia shift from isolated localization projects toward connected industrial ecosystems capable of sustained upgrading and economic diversification.
Mohammed Afzal Shareef
Glob Acad J Econ Buss; 2026, 8(5): 1265-1277
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.019
Large-scale database environments supporting digital government, financial services, healthcare, smart-city platforms, cloud applications, and national data services generate continuous streams of performance metrics, logs, query statistics, configuration events, and security signals. Static thresholds and manual dashboard inspection are increasingly inadequate because normal behaviour changes with workload cycles, application releases, data growth, infrastructure scaling, and distributed-system dependencies. This paper develops an artificial-intelligence-enabled framework for intelligent anomaly detection in large-scale database environments, with particular attention to Saudi Arabia's rapidly expanding digital infrastructure under Vision 2030. A structured narrative review synthesizes peer-reviewed work on KPI-based database anomaly detection, multivariate time-series modelling, log analytics, deep learning, explainable anomaly detection, AIOps, and database root-cause analysis, together with Saudi policy sources on data and AI, digital government, cybersecurity, and cloud adoption. The proposed framework integrates six layers: governed observability, context-aware data engineering, multi-model anomaly detection, multimodal correlation and root-cause analysis, risk-based alert orchestration, and human-in-the-loop learning. It distinguishes point, contextual, collective, compound, workload, resource, query, availability, configuration, and security-related anomalies, and recommends combining robust statistical baselines with unsupervised machine learning, autoencoders, recurrent or transformer models, and log-sequence analysis rather than relying on a single algorithm. Evaluation is defined using both machine-learning metrics and operational outcomes, including event-level precision and recall, false-alert burden, detection latency, time-to-diagnosis, and stability under concept drift. The framework is aligned with Saudi requirements for responsible AI, resilient digital infrastructure, data protection, and cybersecurity. The paper argues that intelligent anomaly detection should be deployed as an auditable decision-support capability before progressing toward automated remediation, enabling Saudi organizations to improve reliability while preserving governance and expert oversight.
Digital twins are moving from descriptive building models toward continuously synchronised cyber-physical systems capable of testing, diagnosing, predicting and documenting safety performance. This review examines how that transition can support fire safety testing, commissioning and risk management in Saudi smart buildings. This review adopted a structured integrative approach to synthesise peer-reviewed research on digital twins, building information modelling, Internet of Things sensing, artificial intelligence, fire dynamics, evacuation, predictive maintenance, commissioning, and Saudi digital-construction readiness. The evidence indicates that the strongest near-term value lies not in a single ‘fire twin’ application but in a lifecycle assurance architecture that connects design intent, test evidence, live sensor states, functional cause-and-effect logic, maintenance history and emergency response models. Existing studies demonstrate credible capabilities in fire-scene reconstruction, intelligent fire-protection control, evacuation tracking, dynamic route guidance and predictive maintenance. Still, they remain fragmented across prototypes, individual systems and non-Saudi test environments. For Saudi Arabia, adoption therefore depends on interoperable data models, auditable commissioning rules, calibrated sensors under harsh environmental conditions, cybersecurity, workforce competence and governance that preserves an authoritative record from design through operation. The review proposes a fire-safety digital-twin assurance model and identifies research priorities for performance-based validation, continuous commissioning, uncertainty-aware risk analytics and regulatory acceptance. The resulting agenda positions the digital twin as an evidence infrastructure for safer, more transparent, and more resilient smart-building operation, rather than merely a visualisation platform.
Agentic artificial intelligence is transforming enterprise metadata management from rule-based automation to goal-directed systems capable of discovering, interpreting, connecting, and governing metadata across heterogeneous data estates. This review evaluates how autonomous agents can facilitate metadata discovery, sensitive-data classification, semantic enrichment, lineage reconstruction, enterprise catalog maintenance, and stewardship, while maintaining accountability to established data-governance frameworks. A structured integrative review of DOI-verified peer-reviewed literature connects three typically distinct research streams: enterprise metadata and data catalogs, knowledge graphs and provenance, and large-language-model-based autonomous agents. The synthesis indicates that the most effective near-term approach is policy-constrained orchestration, where specialized agents propose metadata changes, ground decisions in catalogs and knowledge graphs, preserve evidence, and escalate significant actions to stewards. DAMA-oriented management domains provide the operational model for ownership, quality, architecture, metadata, and governance, while Saudi Arabia's NDMO context elevates requirements for classification, traceability, control evidence, and institutional accountability. The review introduces an auditable agentic metadata architecture and an autonomy-assurance loop. It concludes that enterprise value relies on provenance-preserving execution, confidence-aware escalation, semantic interoperability, human override, measurable metadata quality, and governance controls that recognize agent actions as auditable events rather than invisible automation.
Gas-insulated substations and switchgear (GIS) offer compactness, reliability and environmental protection, but the continued use of sulphur hexafluoride (SF6) creates a material climate-management obligation because small losses can dominate the greenhouse-gas footprint of otherwise efficient high-voltage assets. This review develops an integrated low-carbon operating framework for Saudi GIS portfolios by connecting four practices that are commonly managed separately: leakage detection, gas recovery and inventory control, predictive condition assessment, and risk-based asset-life extension. A structured integrative review of peer-reviewed research published from 2020 to 2025 was conducted across recent SF6-alternative, leakage-monitoring, partial-discharge, digital-twin, health-index and lifecycle studies. The evidence indicates that decarbonisation cannot be reduced to replacing the insulating gas. Existing fleets require accurate leak localisation, closed-loop gas handling, condition-informed interventions and disciplined decisions about refurbishment versus replacement. Infrared and multi-channel optical methods improve non-contact leak identification, while decomposition sensing, vibration analysis, partial-discharge diagnostics and data-driven models broaden the observable condition state. Health-index and condition-based risk-management methods then translate heterogeneous evidence into maintenance priorities and life-extension decisions. For Saudi Arabia, the proposed architecture is designed for hot, dusty, geographically dispersed and rapidly modernising grid environments, where outage avoidance, technician capability, spare-parts strategy and emissions stewardship must be considered together. The review concludes that smart GIS should be governed as a coupled carbon-reliability system in which gas balance, defect evidence, intervention history and remaining-life decisions continuously update one another.
Waqas Mohammad
Glob Acad J Econ Buss; 2026, 8(5): 1311-1322
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.023
Saudi Arabia’s tourism megaprojects are intended to do more than create visitor attractions. They are instruments of regional restructuring that can redirect investment, extend infrastructure networks, diversify local economies, improve access to peripheral destinations, and create new institutional relationships between national programs and local places. This review develops an integrated framework for understanding how those regional effects depend on four delivery capabilities: infrastructure planning, utility readiness, stakeholder coordination, and project delivery. Evidence published from 2020 to 2025 was synthesized across tourism development, spatial planning, construction management, sustainability, resilience, and digital project delivery. The literature indicates that destination success is weakened when hospitality assets advance faster than airports, roads, public transport, power, water, wastewater, digital connectivity, and municipal services. It also shows that stakeholder fragmentation, incomplete scope definition, late design changes, weak information exchange, and delayed approvals can convert strategic ambition into cost, schedule, and operational risk. Conversely, integrated spatial planning, early utility load modelling, transparent interface governance, digital coordination, and benefit-led delivery can transform individual projects into durable regional systems. The proposed framework treats a tourism megaproject as a regional platform rather than a bounded construction site. It links strategic objectives to spatial structure, enabling infrastructure, utility capacity, coordinated delivery, destination operations, and measurable regional benefits through a closed feedback loop. The review concludes that Vision 2030 outcomes will depend increasingly on the quality of integration between physical assets, institutions, local economies, and long-term operations, not simply on the completion of flagship facilities.
Muhammad Yasir Bashir
Glob Acad J Econ Buss; 2026, 8(5): 1323-1333
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.024
Financial performance management in manufacturing increasingly depends on the ability to combine transaction integrity with timely analytical interpretation. This review develops a decision-support framework that links SAP S/4HANA with Microsoft Power BI for Saudi manufacturing companies seeking stronger profitability, liquidity, cost control, working-capital discipline and investment visibility. The study uses a structured integrative review of literature published from 2020 to 2025, supplemented by recent Saudi industrial policy and technology documentation. Evidence is organized around five themes: digital ERP capabilities, business intelligence and dashboard design, data integration and governance, management-accounting decision support, and the institutional requirements of Saudi industrial transformation. The synthesis indicates that ERP value is not created by transaction automation alone. It becomes strategically useful when governed financial and operational data are transformed into decision-relevant measures, visualized at appropriate levels of granularity, and embedded in recurring planning and review routines. SAP S/4HANA provides the transactional and semantic core, while Power BI extends accessibility through interactive reporting, drill-down analysis, variance diagnostics and cross-functional views. The proposed framework therefore connects source transactions, governed data models, financial KPI logic, visual analytics, managerial interpretation and corrective action in a closed loop. For Saudi manufacturers, this architecture is particularly relevant to Vision 2030 priorities concerning industrial competitiveness, digital transformation, localization and private-sector productivity. The review concludes that successful implementation requires data ownership, metric discipline, secure integration, finance-business partnering and phased adoption rather than technology deployment in isolation.
Saudi Arabia's Vision 2030 is accelerating the digital integration of construction programmes, energy assets, cloud platforms, industrial control systems, and geographically dispersed project sites. This integration creates operational value but also expands the number of pathways through which cyber events can disrupt safety, productivity, data integrity, and service continuity. Existing cybersecurity frameworks provide strong control baselines, yet they do not fully express how a multi-site enterprise should measure resilience when temporary construction networks, contractor identities, common data environments, operational technology, remote access, and central security operations coexist. This review therefore develops a Cyber Resilience Maturity Model for multi-site construction and energy enterprises in Saudi Arabia. Evidence published between 2020 and 2025 was synthesized from peer-reviewed construction, industrial control, cyber-physical, and power-system research together with Saudi and international control frameworks. The review translates recurring evidence into eight maturity dimensions: governance and regulatory alignment; asset, identity, and architecture visibility; IT/OT segmentation and secure access; third-party and project supply-chain security; detection and security operations integration; incident response and cyber-physical recovery; workforce and contractor competence; and metrics, assurance, and adaptive improvement. Five maturity levels are proposed, progressing from fragmented practice to adaptive resilience. A criticality-weighted cross-site scoring method and minimum capability gates are introduced to prevent strong corporate controls from masking weaknesses at high-consequence locations. It provides Saudi enterprises with a practical basis for comparing sites, prioritizing investment, and linking cybersecurity improvement to continuity, safety, and Vision 2030 delivery.
Zahed Ali Sohail Mohammed
Glob Acad J Econ Buss; 2026, 8(5): 1345-1355
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.026
Hybrid-cloud adoption is reshaping the operating model of banks and treasury functions, but resilience depends on more than distributing workloads between public cloud and internal data centres. Banking infrastructure combines high-value transaction flows, privileged identities, regulatory obligations, legacy integration, real-time interfaces, and strict recovery expectations. This review develops a decision-support framework for hybrid-cloud cyber resilience in Saudi banking and treasury infrastructure by integrating AWS and Azure control patterns with identity management, secure data-centre operations, observability, recovery engineering, and governance. A structured integrative review was undertaken using peer-reviewed and authoritative sources published from 2020 to 2025. The synthesis concentrates on five recurring problems: fragmented identity control, configuration drift across heterogeneous platforms, weak visibility between cloud and on-premises domains, recovery designs that do not sufficiently address cyber compromise, and control duplication that increases complexity without improving assurance. The resulting framework treats identity as the principal trust plane, cloud landing zones as governed workload domains, the data centre as an actively secured component rather than a residual legacy environment, and resilience as a closed-loop capability linking prevention, detection, containment, recovery, and evidence. The review argues that Saudi banks can obtain greater resilience when multi-cloud flexibility is constrained by common policy, strong privilege governance, workload segmentation, immutable recovery, unified telemetry, and business-led recovery objectives. The framework also aligns architectural choices with national cloud-security expectations and Vision 2030 priorities for secure digital financial infrastructure.
Mohammad Sufian Hafiz
Glob Acad J Econ Buss; 2026, 8(5): 1356-1366
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.027
Currently in Saudi Arabia, fast-growing companies are increasingly requiring debt portfolios which enable them to finance their rapid expansion without causing growth to become a situation that makes it hard to refinance afterwards. This study looks at how companies can improve their debt capital structures by striking a balance between three conflicting aims: the cost of capital, liquidity resilience, and financial risk. It is based on evidence from peer-reviewed publications between 2020 and 2025, with a focus on Saudi Arabia, the Gulf Cooperation Council markets and similar emerging markets, and incorporates insights from trade-off theory, pecking order theory, agency theory, signaling theory and the theory of dynamic calibration. The evidence indicates that there is no one leverage ratio that is optimal in all circumstances. Sustainable debt capacity is affected by profitability, the visibility of cash flow, firm size, the tangibility of assets, the availability of growth opportunities, debt maturity, market access and governance quality. Moreover, evidence obtained from Saudi Arabia shows that both the maturity of debt and the size of the firm have an impact on the relationship between leverage and performance. Meanwhile, the expanding sukuk and bond market is offering new possibilities for increasing maturities and for diversifying the investor base. The review recommends a portfolio-based method of optimization according to which companies should first examine their debt capacity in the case of stressed cash flows and then choose the appropriate instruments and maturities on the basis of the lifetime of their assets, their liquidity needs, the flexibility of any covenants, and the expected conditions for refinancing. The key conclusion is that high-growth firms in Saudi Arabia should regard leverage as a dynamic range backed by liquidity buffers, maturity ladders, and governance mechanisms which are activated by certain events, rather than as a fixed target ratio.
Wellhead systems functioning under high pressure and high temperature involve a situation in which pressure containment, thermal expansion, sealing performance, materials compatibility, and observance of operational procedures all become closely interrelated. This review has gathered the most up-to-date literature published between 2020 and 2025 on the reliability, integrity and risk management of wellheads and of the other pressure-control barriers used in complex upstream operations. It examines the load paths from the casing and tubing all the way to the casing heads, tubing heads, spools, connectors, valves, seals and the Christmas-tree assemblies; the various types of degradation caused by pressure-temperature cycling, corrosion, erosion, fatigue, wellhead movement and the deterioration of the seals; and the increasing significance of continuous surveillance, digital twins and risk-based maintenance. A structured integrative review method was used, with clear eligibility criteria and in accordance with the PRISMA 2020 reporting framework, since a meta-analysis was not carried out as the body of evidence includes experiments, numerical models, field case studies, standards and review articles. The synthesis demonstrates that the reliability of HPHT systems is not mainly a question of the rating of any one component but rather results from the way in which design margins, transient loads, material-environment compatibility, the quality of installation and aging over time interact. The paper suggests a lifecycle integrity framework which connects qualification, barrier verification, condition monitoring, anomaly diagnosis, risk ranking, intervention planning and feedback. It concludes that in the future HPHT assurance should focus on representative full-envelope qualification, coupled thermo-mechanical models, health indicators for seals and connections, validated digital-twin decision support, and risk controls which remain effective even in the presence of uncertainty.
Mohammad Ismail Ansar Ahmed Ansari
Glob Acad J Econ Buss; 2026, 8(5):1379-1390
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.029
Heat exchanger networks (HENs) play a key role in energy recovery in refineries, gas-processing plants and integrated petrochemical complexes since they decide how much process heat is reused internally before outside heating and cooling utilities have to be bought. This review looks at the developments published between 2020 and 2025 and uses them to formulate an optimisation programme specific to oil and gas facilities that are operating in accordance with Vision 2030. The evidence shows that the most effective retrofit approaches no longer regard pinch targets, network topology, exchanger design, fouling, pressure drop, maintenance and operability as independent issues; on the contrary, recent research has progressively combined thermodynamic targeting with mathematical programming, detailed thermal-hydraulic models, asset-life planning, real-time performance monitoring and the selection of retrofit measures within budgetary limitations. Refinery case studies show that significant reductions in utility and operating costs can be obtained through both low-disruption measures and more extensive reconfiguration of the network, although the outcomes depend heavily on the existing topology, the stream temperatures, the current state of fouling and the execution constraints. For Saudi facilities, the best approach therefore is not a single, universal HEN design but rather a step-by-step decision framework: first diagnose the actual losses in heat recovery, set realistic targets, rank the various retrofit options according to their energy and carbon value, take into account the hydraulic and fouling penalties, and then maintain the savings by means of digital monitoring and coordinated maintenance. The review suggests an implementation framework for Vision 2030 that connects HEN optimisation to fuel displacement, carbon-intensity reduction, reliability and capital discipline. It also points out gaps in dynamic optimisation, the treatment of uncertainty, site-wide integration, carbon-aware objective functions and validation based on data from Saudi plants. The main manuscript contains 4,400 words (from the Abstract to the Conclusion; excluding the title, headings, keywords, tables, figure captions and references).
Syed Abid Ali
Glob Acad J Econ Buss; 2026, 8(5): 1391-1400
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.030
The underground aviation fuel hydrant systems have to tightly contain hydrocarbons, ensure that the fuel quality remains strict, and are difficult to reach because of the physical limitations involved. They are also influenced by the changes that take place when aircraft are refuelling. For these reasons, the method needed to monitor such networks is different from that applied to long-distance oil pipelines. This article looks at the research carried out between 2020 and 2025 on a number of topics including pipeline leak detection, structural health monitoring, fibre-optic sensing, acoustic emission, hydraulic analytics, sensor fusion, machine learning, and digital twins. On the basis of this research it proposes a framework tailored for airports in Saudi Arabia. The framework follows a step-by-step procedure: the pressure and flow data establish the hydraulic context, while acoustic, vibration, fibre-optic, hydrocarbon-sensitive and corrosion sensors each contribute separate evidence. Edge analytics are used to filter out operational noise, and then a fusion stage combines all the data to estimate the presence, location, severity and degree of degradation of any leak, as well as how confident the estimates are. A digital model links these results to the asset's history, inspection records, maintenance work and operational data. The framework places particular emphasis on the buried pipes underneath the aprons, the hydrant pits, the branch connections, the interfaces between the soil and concrete, access to the sensors, controlling false alarms, and distinguishing between the normal changes that occur during refuelling and abnormal leaks. The review also connects the framework to the EI 1560 best practices and to Saudi Vision 2030, an initiative which aims at achieving reliable, safe, environmentally responsible and digitally capable airports as air traffic increases. The key point is that Saudi airports should not depend on single-sensor leak detection; rather, a risk-based, multi-physics, data-fused approach is more reliable for providing early warnings, enabling condition-based maintenance, reducing product loss, and improving the predictive management of the hydrant network.
The increasing reliance on the ability to accurately decide where, when, and how to measure in industrial manufacturing, rather than on a indiscriminate expansion of inspection, is highlighted in this review. By drawing on evidence from the field of metrology, Quality 4.0, zero-defect manufacturing, automated visual inspection, digital twins, and risk-based inspection, a risk-based framework for precision measurement and inspection in Saudi industrial manufacturing is developed. The synthesis argues for linking three decision levels—product and process criticality, measurement capability and uncertainty, and inspection evidence with closed-loop corrective action. However, recent literature shows that the benefits of advanced sensing, on-machine measurement, machine vision, and data-driven prediction depend on metrological traceability, validated measurement systems, controlled decision rules, data integrity, and qualified quality staff. Studies carried out in Saudi Arabia also point out that organizational readiness, investment limitations, system integration, and capability building influence the adoption of Industry 4.0. The framework in question bases inspection intensity on consequence and detectability, ties acceptance decisions to uncertainty, and sees digital technologies as a way of producing evidence rather than as a replacement for metrological control. It also suggests that governance should be achieved through the use of risk registers, critical-to-quality characteristics, measurement system analysis, traceability records, escalation rules, and regular reviews. This method could allow for local manufacturing within the framework of Vision 2030 by allocating quality resources to the failure modes that have the greatest operational, safety, reliability, and reputational consequences, while at the same time making conformity decisions that are defensible. The research priorities are sector-specific validation in Saudi Arabia, the economic optimisation of inspection efforts, trustworthy artificial intelligence, interoperable digital calibration records, and long-term evidence on quality performance.
Ilyas Mohammad Siddiqui
Glob Acad J Econ Buss; 2026, 8(5): 1413-1424
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.032
Zero-day exploits pose a challenge to critical infrastructure, as defenders must detect malicious behaviour prior to the existence of a reliable signature, patch, or complete threat profile. This problem is particularly acute in Saudi Arabia where energy, water, transportation, telecommunications, healthcare, government, and smart-city services rely on tightly coupled information technology and operational technology. This review summarises recent evidence about three capabilities that determine whether an unknown exploit becomes a contained security event or a cascading service disruption: advanced detection, rapid containment, and coordinated incident response. The researchers undertook a structured integrative review of the DOI-verified literature, from 2020 to 2025, focusing on zero-day detection, industrial control systems, cyber-physical resilience, zero-trust containment, explainable artificial intelligence, federated learning, security orchestration, and recovery. Thirty sources were included in the thematic synthesis. The evidence suggests that no single detector is robust enough to detect unseen attacks. The combination of behaviour baselining, unsupervised or semi-supervised learning, explainable scoring, asset and process context and cross-domain telemetry leads to greater resilience. Detection must then link to safety-aware containment, including identity restriction, micro segmentation, endpoint or workload isolation, and carefully bounded operational-technology actions. Orchestration and automation enable rapid response, but control actions with high consequences require human authorisation and predefined fallback states. The review introduces a Saudi Zero-Day Cyber Resilience Framework that connects detection confidence, operational criticality, containment authority, response playbooks, validation of recovery, and post-incident learning. The framework shifts the focus from perfect prevention to measurable continuity, controlled degradation and faster restoration under uncertainty. It also discusses research directions in local industrial datasets, adversarial robust models, digital-twin validation, privacy-preserving collaboration, and resilience metrics applicable to the Saudi critical infrastructure.
Ishaq Siddiqui Mohammed
Glob Acad J Econ Buss; 2026, 8(5): 1425-1447
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.033
Industrial Control Systems (ICS) are increasingly connected to enterprise networks, cloud platforms, remote engineering services, and industrial Internet of Things devices. Saudi Arabia is a particularly relevant setting because NCA OTCC-1:2022 establishes minimum cybersecurity requirements for relevant government organizations and private-sector organizations owning, operating or hosting Critical National Infrastructures, while other organizations are encouraged to use the controls as guidance (NCA, 2022). This review synthesizes evidence published between 2020 and 2025 with emphasis on the ability to anticipate, withstand, detect and contain, recover from, and adapt after cyber incidents while preserving safety, availability, integrity, and deterministic operational requirements. Across the retained evidence set, recurring resilience practices include current asset and dependency visibility, segmentation, least privilege, controlled remote access, process-aware monitoring, disciplined engineering change control, safety-centered response, and verified restoration. These are evidence-supported findings from the reviewed literature and authoritative guidance. Building on that evidence, this study proposes a Saudi-oriented, asset-specific resilience framework that maps NCA OTCC/ECC requirements and complementary NIST and IEC guidance to SCADA, PLC, DCS, and HMI resilience functions, and extends the synthesis with recovery engineering, exercise-based validation, and a Proposed Saudi OT Cyber Resilience Measurement Set.
Muhammad Zeeshan Qureshi
Glob Acad J Econ Buss; 2026, 8(5): 1448-1458
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.034
Insurance finance functions continue to encounter challenges from fragmented policy, claims, broker, bank, and reinsurance data, even when core underwriting and claims platforms are digitally advanced. This review evaluates the potential for intelligent automation to transform receivables, payables, and reinsurance finance processes into a unified, controlled operating system instead of isolated efficiency initiatives. A structured integrative review was conducted, drawing on peer-reviewed research and credible regulatory or standard-setting publications from 2020 to 2025. Evidence was systematically coded by process scope, technology, control design, performance effects, implementation conditions, and insurance-specific accounting requirements. The findings show that robotic process automation is most effective for stable, rule-based activities, while document intelligence, machine learning, application programming interfaces, workflow orchestration, and process mining extend automation to unstructured documents, exception prediction, continuous reconciliation, and operational monitoring. Automation of receivables enhances cash application, ageing visibility, dispute routing, and collection prioritisation. Automation of payables improves invoice capture, matching, approval, duplicate detection, and payment control. Reinsurance automation delivers additional value through contract-data standardisation, bordereaux validation, ceded-premium calculation, recoverable tracking, settlement matching, and IFRS 17 data lineage. Realising these benefits depends on process redesign, master-data quality, segregation of duties, human review of material exceptions, cyber resilience, and accountable model governance. This paper presents an integrated target architecture, a staged maturity pathway, and a balanced performance framework encompassing speed, accuracy, liquidity, compliance, resilience, and stakeholder outcomes.
Saudi Arabia is expanding domestic food production while seeking higher industrial productivity, resource efficiency and resilience under Vision 2030. Olive and tomato processing are strategically relevant because both depend on seasonal agricultural inflows, time-sensitive quality preservation and capacity-constrained thermal, separation and packaging operations. This review develops a data-driven production planning framework for smart-factory capacity optimisation in Saudi olive and tomato-paste manufacturing. The study synthesises literature published from 2020 to 2025 on food-process control, digital twins, Industry 4.0, predictive maintenance, production scheduling and process analytical technologies, complemented by official Saudi agricultural and industrial evidence. The review distinguishes nominal equipment capacity from effective, quality-constrained and utility-constrained capacity, and shows why seasonal factories require planning models that jointly represent raw-material arrivals, product deterioration, changeovers, sanitation, equipment condition, energy and water availability, yield uncertainty and market demand. The proposed framework combines industrial sensing, manufacturing execution data, short-horizon forecasting, digital-twin simulation, finite-capacity scheduling and feedback from advanced process control. It recommends a rolling planning architecture in which forecasted olive and tomato arrivals are converted into feasible campaigns, bottlenecks are protected by predictive maintenance, and process set-points are coordinated with throughput and quality targets. For Saudi manufacturers, the framework links local agricultural scale with industrial localisation by prioritising interoperable data, measurable overall equipment effectiveness, seasonal scenario planning and staged investment rather than technology acquisition without an operating model. The review concludes that smart capacity optimisation can increase usable throughput, reduce avoidable losses and improve resource productivity, but only when planning, process control, maintenance and quality decisions are integrated around a common data model.
Muhammad Waseem Raza
Glob Acad J Econ Buss; 2026, 8(5): 1470-1480
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.036
The electricity system in Saudi Arabia is currently being expanded and is at the same time becoming more digitally monitored, although the high-voltage transformers, substations, transmission lines, switchgear and protection systems remain long-life assets whose failure can have severe implications for reliability and economic performance. This review presents a risk-based asset management framework aimed at improving the reliability of high-voltage power infrastructure within Saudi energy networks. Evidence published between 2020 and 2025 has been brought together covering the areas of transformer condition monitoring, health indices, dissolved-gas analysis, machine learning, predictive maintenance, life-cycle planning, risk assessment and the modernisation of the Saudi grid. The review applies a structured integrative method which stresses clear eligibility criteria, critical comparison and thematic synthesis rather than making up screening statistics. The findings indicate that asset decisions can be made more defensible if condition data is transformed into a health state and then combined with the probability and consequences of failure, with uncertainty taken into account, before being linked to specific actions such as inspection, maintenance, refurbishment, replacement or continued monitoring. The proposed framework thus includes five interconnected layers: asset evidence, condition state, risk quantification, intervention decisions and portfolio governance. Special attention is paid to the conditions under which the Saudi infrastructure operates, such as high ambient temperatures, exposure to dust, rapid growth in demand, the integration of renewable energy, the geographically dispersed nature of the infrastructure and the increasing reliance on digital monitoring. The framework contributes to improved reliability by directing limited maintenance resources towards those assets that have the highest risk trajectory rather than just those that are the oldest. It also points out the need for further research in the areas of fleet-scale data quality, uncertainty calibration, interpretable artificial intelligence, cybersecurity, climate stress modelling and local validation. The approach thus offers a practical basis for utility companies that wish to implement transparent, auditable and reliability-focused asset management in line with the requirements of Saudi energy transition.
Arshad Pasha Mohammed
Glob Acad J Econ Buss; 2026, 8(5): 1481-1491
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.037
Saudi Arabia’s digital-government agenda increasingly depends on decisions that must be fast, consistent, explainable and adaptable rather than merely digitised. This review examines how artificial intelligence, business rules and case management can be combined into an intelligent decision-automation architecture suitable for public services operating under Vision 2030. The paper synthesises peer-reviewed literature published from 2020 to 2025 across five intersecting streams: artificial intelligence in public administration, Saudi digital transformation, explainable and responsible artificial intelligence, hybrid decision management, and data-driven case management. The evidence indicates that artificial intelligence is strongest when used for prediction, classification, prioritisation and recommendation; business rules are strongest when policy, eligibility, thresholds and legal constraints require deterministic control; and case management is strongest when services involve incomplete information, exceptions, appeals and human judgement. Treating these capabilities as substitutes creates avoidable governance and operational risks. The review therefore proposes a layered framework in which trusted data and case context feed a decision-orchestration layer that coordinates artificial intelligence models, executable rules and case logic under human oversight. A seven-stage lifecycle—sense, validate, decide, explain, authorise, execute and learn—connects model outputs to accountable service actions and continuous improvement. The framework emphasises versioned policies, traceable evidence, appealability, bias monitoring, privacy controls and public-value metrics. The study contributes a practical architecture and governance model for Saudi public entities seeking to automate high-volume decisions without sacrificing legal compliance, institutional accountability or citizen trust.
As energy, water, transport, telecommunications, healthcare, finance and government services migrate to cloud platforms, industrial internet connectivity, remote operations and data-driven automation, Saudi Arabia’s critical national infrastructure is becoming more digitally integrated. The integration enhances operational capability, but also provides routes for ransomware and advanced persistent threats (APTs) to move from information technology to operational technology, disrupt essential services, manipulate trusted identities and exploit supplier relationships. This review integrates recent peer-reviewed evidence on cyber resilience against these threats and interprets it for the Saudi critical-infrastructure context. The choice of a structured integrative review is due to the evidence being across the fields of cyber resilience, ransomware, APT behaviour, industrial control systems, zero trust, situational awareness, risk governance and recovery engineering. The synthesis suggests that prevention alone cannot deliver resilient infrastructure. It calls for service-centric governance, separation of business and operational environments, identity-based access control, threat-informed monitoring, secure and tested recovery, supplier assurance, and continuous adaptation after exercises or incidents. Ransomware and APTs differ in speed and goals, but both take advantage of weak identity, lack of asset visibility, trusted administrative pathways, and poor recovery design. The paper therefore proposes a Critical Infrastructure Cyber-Resilience Loop that incorporates govern, anticipate, withstand, detect, contain, recover and adapt functions around minimum viable service delivery. The review provides a practical framework for Saudi operators and policymakers, and also highlights research gaps in OT-specific resilience measurement, cross-sector dependency modelling, recovery assurance, and empirical evaluation of national cybersecurity controls.
Fuel retail stations in Saudi Arabia are positioned at the intersection of energy efficiency, distributed renewable energy, transport electrification, and service-sector modernization. This review develops a strategic framework for transforming conventional stations into energy-efficient mobility and service hubs aligned with Saudi Vision 2030. A structured integrative review synthesizes thirty verified peer-reviewed studies spanning Saudi energy policy, hot-climate building efficiency, photovoltaic performance, electric-vehicle charging, battery storage, and digital energy management. The synthesis indicates that transformation should follow an efficiency-first sequence: establish measured baselines, reduce avoidable cooling and auxiliary loads through weather- and season-responsive operation, deploy climate-adapted photovoltaic generation, integrate EV charging according to traffic demand and electrical hosting capacity, add storage only where it resolves a defined constraint, and coordinate assets through interoperable digital energy management. The review highlights that Saudi climatic conditions make cooling efficiency, seasonal thermostat control, photovoltaic soiling, thermal derating, and maintenance central to lifecycle performance. The framework therefore treats local weather as an operating input: during milder periods, particularly where central and eastern locations experience cooler winter conditions, convenience-retail HVAC should automatically reduce unnecessary cooling rather than continue summer-style operation. It also finds that unmanaged fast charging can create avoidable grid and transformer stress, whereas controlled charging, storage, and forecasting can improve flexibility. Two station archetypes and decision tables, together with two graphical frameworks, translate the evidence into implementation logic. Key research gaps concern station-specific benchmarks, monitored solar-canopy performance, charger utilization, battery economics, and field validation of digital controls. The paper concludes that scalable transformation requires outcome-based regulation, measurement and verification, interoperability, and staged portfolio learning rather than isolated technology deployment.
Muhammad Khurram Toor
Glob Acad J Econ Buss; 2026, 8(5): 1514-1525
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.040
Generative artificial intelligence is causing visual communication to take on an interactive character in which both people and computational systems jointly define, develop, test, and enhance creative possibilities. This development is significant for Saudi organisations since visual communication plays a key role in expressing brand identity, narrating service stories, influencing the customer experience, and gaining institutional legitimacy, even though relevant evidence is still dispersed across various fields such as management, marketing, information systems, advertising, and design research. This review gathers thirty peer-reviewed journal studies that were published between 2020 and 2025 and offers an integrative framework for human-AI collaboration in the areas of visual communication and creative strategy. By means of a structured integrative review and thematic synthesis, the study examines the roles of collaboration, the creative outcomes, the organisational capabilities, the governance requirements, and the implications within the Saudi context. The synthesis identifies five recurring mechanisms: humans providing strategic framing, AI carrying out generative expansion, humans taking the lead in curation, validation being based on organisational and cultural criteria, and learning through documented feedback. Even though generative systems can widen the range of ideas, accelerate visualisation, and boost productivity, these advantages do not automatically lead to communication that is distinctive or strategically coherent. Over-reliance on machine suggestions can reduce diversity, while poor delegation, low AI literacy, and insufficient validation can cause errors to be amplified or the brand message to be diluted. The proposed Generative Human-AI Visual Strategy framework regards human judgment as the final authority and views generative AI as a collaborative partner with well-defined boundaries. For Saudi organisations, effective implementation therefore demands involvement from leadership, the capacity to carry out bilingual and cultural interpretation, the introduction of clear review stages, controls over provenance, the development of the necessary capabilities, and feedback loops that transform experimentation into organisational learning.
Saudi Arabia is expanding domestic food production while seeking higher industrial productivity, resource efficiency and resilience under Vision 2030. Olive and tomato processing are strategically relevant because both depend on seasonal agricultural inflows, time-sensitive quality preservation and capacity-constrained thermal, separation and packaging operations. This review develops a data-driven production planning framework for smart-factory capacity optimisation in Saudi olive and tomato-paste manufacturing. The study synthesises literature published from 2020 to 2025 on food-process control, digital twins, Industry 4.0, predictive maintenance, production scheduling and process analytical technologies, complemented by official Saudi agricultural and industrial evidence. The review distinguishes nominal equipment capacity from effective, quality-constrained and utility-constrained capacity, and shows why seasonal factories require planning models that jointly represent raw-material arrivals, product deterioration, changeovers, sanitation, equipment condition, energy and water availability, yield uncertainty and market demand. The proposed framework combines industrial sensing, manufacturing execution data, short-horizon forecasting, digital-twin simulation, finite-capacity scheduling and feedback from advanced process control. It recommends a rolling planning architecture in which forecasted olive and tomato arrivals are converted into feasible campaigns, bottlenecks are protected by predictive maintenance, and process set-points are coordinated with throughput and quality targets. For Saudi manufacturers, the framework links local agricultural scale with industrial localisation by prioritising interoperable data, measurable overall equipment effectiveness, seasonal scenario planning and staged investment rather than technology acquisition without an operating model. The review concludes that smart capacity optimisation can increase usable throughput, reduce avoidable losses and improve resource productivity, but only when planning, process control, maintenance and quality decisions are integrated around a common data model.
Vibin James
Glob Acad J Econ Buss; 2026, 8(5): 1537-1547
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.042
Enterprise networks are evolving from relatively static connectivity fabrics into distributed computing systems that span campuses, data centers, clouds, edge nodes, remote users, software-defined overlays, and programmable transport. This heterogeneity makes manual configuration and reactive operations increasingly inadequate. This review synthesizes recent research on artificial intelligence for network automation and develops an AI-powered intelligent network automation framework for future-ready enterprise computing systems. The review integrates evidence from machine learning for networking, intent-based networking, zero-touch management, network telemetry, network digital twins, programmable data planes, anomaly detection, federated learning, and explainable AI. The proposed framework organizes automation as a governed closed loop that translates business intent into machine-verifiable objectives, collects multi-layer telemetry, constructs contextual network state, applies predictive and decision models, validates actions in a digital-twin or policy sandbox, executes changes through software-defined and programmable infrastructure, and continuously verifies outcomes. The synthesis shows that effective autonomy depends less on a single algorithm than on the coordination of data quality, model lifecycle management, policy constraints, observability, security, and human oversight. Key research gaps concern cross-domain state representation, safe reinforcement learning, explainability, concept drift, trustworthy intent translation, multi-vendor interoperability, energy-aware optimization, and methods for measuring automation risk. The review contributes a practical architecture, an evaluation logic, and research directions for enterprises seeking higher resilience, agility, and computing efficiency without surrendering operational accountability.
Puthan Veettil Abdulla Mohd Kayoom
Glob Acad J Econ Buss; 2026, 8(5): 1548-1558
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.043
Large Saudi corporations operate with substantial capital commitments, complex supplier and customer networks, exposure to commodity and interest-rate shocks, and increasingly digital finance functions. These conditions make cash flow optimization and liquidity risk reduction a dynamic decision problem rather than a static ratio-management exercise. This review synthesizes recent research on corporate cash holdings, working capital, trade credit, financial forecasting, machine learning, explainable artificial intelligence, and Gulf-region liquidity behavior to develop an integrated view of intelligent financial decision models. Evidence indicates that advanced forecasting can improve the timing and granularity of liquidity estimates, but prediction alone does not determine an economically optimal action. Effective models must connect forecasts to working-capital levers, liquidity buffers, financing choices, scenario constraints, and governance controls. Saudi and Gulf evidence further shows that cash policies respond to financial constraints, macroeconomic weakness, policy uncertainty, geopolitical risk, oil-price volatility, Shariah-related financing structures, and sector characteristics. The review therefore proposes a layered decision architecture that combines transaction-level data, probabilistic cash forecasting, optimization, stress testing, explainability, and managerial override. The synthesis argues that the most useful corporate treasury systems should optimize expected liquidity while preserving resilience under adverse scenarios and should measure forecast value by decision outcomes rather than prediction accuracy alone. Research priorities include Saudi-specific benchmarking, causal evaluation of treasury interventions, multi-entity liquidity optimization, interpretable reinforcement or prescriptive learning, and governance standards for model risk. The resulting framework offers a practical research agenda for large Saudi corporations seeking to reduce idle cash, shorten the cash conversion cycle, improve funding decisions, and protect operational continuity without creating opaque automated financial control.
The massive tourism initiatives of Saudi Arabia are having a significant impact on the way the country develops geographically, but at the same time their large scale mean that infrastructure, environmental, operational and governance risks are concentrated in destinations whose long-term value relies on reliable services and good ecological conditions. This study presents a regional development approach based on risk considerations which links tourism planning with the resilience of critical infrastructure, the vulnerability of destinations and the governance of megaprojects. In order to achieve this, a structured critical integrative review was carried out using 30 peer-reviewed studies published between 2020 and 2025, those being chosen because of their direct relevance to Saudi or Gulf planning, tourism resilience, infrastructure systems, sustainability or the delivery of megaprojects. The findings indicate that risk should be seen as a development condition which varies from one location to another rather than as a factor that is only considered at the project level. Destinations situated along the coast, in the desert with heritage sites, in the mountains, in metropolitan areas and those focused on pilgrimage face different combinations of risks relating to heat, flooding, water supply, the environment, mobility, supply chains and demand, while common dependencies on power, water, transport, digital networks and emergency services lead to the possibility of cascading disruptions. The review also points out a gap in governance between optimisation at the project level and the overall performance of the regional system. In order to bridge this gap, the paper puts forward a six-stage planning framework that incorporates hazard and vulnerability assessment, mapping of infrastructure interdependencies, scenario stress testing, portfolio sequencing, resilience safeguards and adaptive monitoring. This framework transforms resilience from a concept associated with recovery into a development criterion that determines where, when and how tourism capacity is added. This method offers a practical means of coordinating destination growth, infrastructure safety, environmental limits and the creation of regional value in accordance with Vision 2030.
Nura S. Akilu
Glob Acad J Econ Buss; 2026, 8(5): 1571-1583
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.045
This paper provides the first empirical test of the unfolding Resilience Sequence, a theoretical framework that explains how entrepreneurial disposition to produce evolves into Entrepreneurial Acumen within institutional voids. While prior research has examined isolated mechanisms such as bricolage and effectuation, limited attention has been given to the full sequential process through which sustained productive capacity unfolds under persistent constraint. Addressing this gap, the paper employs a qualitative single case design using deductive pattern matching to examine the entrepreneurial journey of Muhammad Abubakar, founder of L&Z Integrated Farms, Nigeria's leading agribusiness venture. Drawing on multiple biographical sources, the analysis operationalises nine constructs: Perceived Feasibility, Enacting Activity, Bilateral Attribution, Relationship Building, Interdependent Exertion, Collective Imagination, Generated Outcomes, Prototype Emergence, and Entrepreneurial Acumen, into observable textual indicators. Findings reveal strong empirical support for all nine propositions, with constructs appearing in the exact predicted sequential order. The results demonstrate that sustained entrepreneurial production is not a singular capacity but a cumulative, dialectical progression of cognitive, relational, and behavioural stages. The study contributes to entrepreneurship theory by validating a process based model of regenerative production, advancing deductive pattern matching in narrative research, and offering practical insights for entrepreneurs, policymakers, and educators operating in resource constrained environments.
Nura S. Akilu
Glob Acad J Econ Buss; 2026, 8(5): 1584-1597
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.046
This paper provides the first empirical test of the Executing Resilience Sequence, a theoretical framework that explains how entrepreneurial disposition to excel evolves into Managerial Acumen within institutional voids. While prior research has examined isolated mechanisms such as bricolage and effectuation, limited attention has been given to the full sequential process through which sustained managerial capacity for performance elevation unfolds under persistent constraint. Addressing this gap, the paper employs a qualitative single case design using deductive pattern matching to examine the entrepreneurial journey of Sijibomi Ogundele, founder of Sujimoto Construction Limited, one of Nigeria's leading luxury real estate development companies. Drawing on multiple biographical sources, the analysis operationalizes nine constructs: Survival Focused, Efficiency and Effectiveness, Recognition Driven, Creativity and Innovation, Prestige Oriented, Change and Leadership, Perpetuity Minded, Prudence and Strategy, and Managerial Acumen, into observable textual indicators. Findings reveal strong empirical support for all nine propositions, with constructs appearing in the exact predicted sequential order. The results demonstrate that sustained managerial excellence is not a singular capacity but a cumulative, dialectical progression of cognitive, operational, and governance stages. The study contributes to entrepreneurship theory by validating a process-based model of performance elevation and governance formation, advancing deductive pattern matching in narrative research, and offering practical insights for entrepreneurs, policymakers, and educators operating in resource-constrained environments.
Ali Abdulameer Flaifel, Hakem Mohsen Mohamed
Glob Acad J Econ Buss; 2026, 8(5): 1598-1607
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.047
This research aims to assess the effectiveness of using the book value and market value frameworks to estimate the implied required rate of return (Ke), examining their subsequent impact on determining the fair value of common stocks and comparing their effects. Traditional valuation models, primarily based on the Capital Asset Pricing Model (CAPM) and market-based metrics, often suffer from increased volatility and conflicting prices in public stock markets. This distorted environment negatively impacts the reliability of cost of equity estimates. To address this vulnerability, this research shifts its analytical focus to a data-driven framework for a group of 10 Iraqi industrial companies over the period 2015-2024. This framework applies the best solutions and models to support the implied rate of return (RM), specifically using the return on investment (ROI) of the companies in the research sample and comparing it to the same implied ROI using earnings per share (EPS) for the same companies over the same period (book value and earnings). The goal is to determine the best approach for estimating the required rate of return and its role in the fair value of the stock, while also controlling for the degree of systematic risk, company size, and earnings quality to understand the direct impact of the two models. Their impact on the accuracy and comparability of fair value. Through periods of macroeconomic distress or systemic market shocks, the first model framework provides a more stable and reversible required rate of return compared to the second model. Consequently, derived fair values exhibit a tighter convergence with long-term intrinsic values, effectively reducing valuation errors resulting from market movement. The analysis highlights a critical gap in the valuation literature by demonstrating that accounting metrics are not merely retrospective reporting requirements, but rather structural anchors capable of stabilizing equity valuations. Its most important findings have profound implications for institutional investors, financial analysts, and regulatory bodies seeking robust valuation methodologies less susceptible to anomalies in temporary market movements.
In Saudi Arabia, artificial intelligence is being introduced into public services, business operations and the digital platforms that deal with citizens, thereby increasing the demand for a kind of governance that allows innovation without sacrificing privacy, accountability or cyber resilience. This review brings together the scholarly work published between 2020 and 2025 in order to examine how responsible AI can be put into practice across the whole platform lifecycle rather than simply remaining as a set of general ethical principles. The analysis is grounded in four areas of evidence: organisational AI governance, data practices that ensure privacy, the use of trustworthy and explainable AI, and adversarial cybersecurity. It argues that these different areas are generally handled through separate compliance pathways, even though a failure in one can affect the others poor data provenance can result in unfair or insecure models; opaque models can make it hard to carry out audits or to investigate incidents; and a cyber breach can wipe out the privacy and accountability safeguards. A structured integrative review of 30 scholarly sources, verified by their DOI numbers, is used to develop a governance framework tailored to the Saudi context for application in both corporate and government platforms. The proposed framework connects the six lifecycle stages use-case intake, data stewardship, model assurance, secure release, continuous monitoring and controlled retirement with cross-cutting elements such as accountability, human oversight, third-party assurance and evidence retention. The review concludes that responsible AI is most credible when governance is expressed in the form of testable controls, clearly defined decision rights and continuously updated evidence of assurance. For organisations in Saudi Arabia, the resulting approach offers a practical means of achieving fast digital transformation while incorporating privacy-by-design and resilience-by-design, and at the same time remaining adaptable to sectoral risks and changing national requirements.
Hasan Zoboun
Glob Acad J Econ Buss; 2026, 8(5):1620-1627
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.049
Team leadership influences construction performance by shaping how project participants interpret priorities, coordinate work, resolve disagreement and respond to emerging risks. Technical plans and contractual controls remain essential, but their implementation depends on decisions made across temporary teams and organisational boundaries. This paper presents a critical narrative review of leadership and project-management literature, supported by a proposed framework for construction practice. It examines transformational, transactional, participative and shared leadership, connecting observable behaviours to communication, trust, accountability, learning and adaptation. The analysis treats project performance as multidimensional, encompassing cost, schedule, quality, safety and stakeholder outcomes. Selected construction studies support relationships between leadership and project success, while broader project research helps explain possible team mechanisms. However, survey-based associations and statistical mediation do not establish the size of a causal leadership effect. A performance-monitoring framework is therefore proposed to connect leadership interventions with independently recorded project outcomes. The paper concludes that effective team leadership requires both supportive relationships and disciplined delivery controls, adjusted to project phase, task uncertainty and decision authority. Future longitudinal research should test these relationships across comparable construction projects and distinguish leadership effects from resources, procurement arrangements and external disruption.
Saudi Arabia's Vision 2030 has increased the strategic importance of diversified and sustainable financing for enterprises participating in infrastructure, energy, industrial, technology, tourism and service-sector expansion. At the same time, debt financing exposes firms to interconnected credit, liquidity, refinancing, interest-rate, cash-flow, project and concentration risks. Conventional ratio analysis and management forecasting remain essential, but periodic and predominantly historical approaches may not fully exploit the financial, operational and market data now generated by enterprise resource planning, treasury, banking and project-management systems. Artificial intelligence (AI), machine learning (ML), predictive analytics and explainable artificial intelligence (XAI) can extend established finance practice by identifying nonlinear risk patterns, improving early-warning capability and supporting forward-looking debt-capacity assessment. This paper develops an AI-enabled financial risk management framework for debt financing decisions in Saudi enterprises. It uses an integrative structured review of recent peer-reviewed literature on corporate default prediction, credit risk, financial distress, loss given default, explainable AI and financial risk analytics, combined with official Saudi sources on financial-sector and debt-market development. The resulting framework comprises six layers: integrated data, AI-supported risk identification, predictive debt-capacity analysis, scenario and stress testing, explainable decision intelligence, and governance with continuous monitoring. The framework treats AI as an augmentation of professional judgement rather than an autonomous financing authority. It proposes practical metrics, a governance workflow and implementation priorities for CFOs, FP&A teams, treasury functions, risk managers and boards. The study contributes by connecting AI-based risk analytics specifically to corporate debt financing in the Saudi Vision 2030 context while explicitly addressing explainability, data governance, model risk and human accountability.
Abdullah Alijefri
Glob Acad J Econ Buss; 2026, 8(5):1641-1651
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.051
The expansion of modern retail destinations in Saudi Arabia has increased the complexity of tenant and lease administration, particularly where rental arrangements include turnover-based components linked to tenant sales. Conventional tenant sales auditing depends heavily on periodic declarations, manual reconciliation, sampling, and retrospective verification. These practices can be difficult to scale across multi-mall portfolios and may delay the identification of abnormal sales patterns. This study proposes an intelligent tenant sales auditing framework that combines machine learning-based anomaly detection, retail sales forecasting, tenant benchmarking, contractual data analysis, and risk-based audit prioritisation. The proposed framework integrates tenant point-of-sale information, declared sales, historical performance, footfall indicators, lease conditions, payment records, and tenant-category benchmarks. A simulated evaluation demonstrates how the framework can classify tenants according to anomaly risk and support targeted audit intervention. The study does not claim that simulated results represent actual Saudi mall performance; they illustrate the analytical logic and potential operational value of the proposed approach. The framework contributes to research at the intersection of artificial intelligence, retail analytics, and commercial real estate management by shifting tenant sales auditing from periodic and reactive verification toward continuous, data-driven monitoring. The proposed model can support Saudi retail property operators in improving sales transparency, strengthening lease governance, prioritising audit resources, and developing more intelligent turnover-based lease management practices.
Mohammed Abdul Lateef
Glob Acad J Econ Buss; 2026, 8(5): 1652-1661
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.052
Saudi Arabia has exceptional solar resources, rapidly growing electricity demand and a national commitment to energy diversification. The performance of photovoltaic systems, however, is affected by high module temperature, dust deposition, irradiance variability, equipment degradation, communication gaps and maintenance quality. Internet of Things monitoring can improve solar-energy harvesting by connecting field sensors, smart meters, inverters, weather stations and maintenance systems to a governed operational platform. This integrative review evaluates how IoT monitoring supports real-time performance assessment, fault detection, soiling management, predictive maintenance, energy forecasting and quality assurance in Saudi photovoltaic installations. It examines system architecture, sensor selection, communications, data quality, cybersecurity, analytics and human decision-making. The paper proposes a framework that converts trustworthy measurements into normalized performance indicators, diagnostic alerts, controlled work orders and verified energy outcomes. It emphasizes that connectivity alone does not optimize generation; benefits depend on calibrated instruments, appropriate baselines, reliable communications, interpretable analytics and timely field action. Black-box optimization without local validation can conceal sensor error or environmental effects. For Saudi Arabia, the recommended approach prioritizes temperature and soiling correction, geographically appropriate communication, secure integration with SCADA, and phased deployment across utility, industrial and distributed systems. The framework supports Vision 2030 through renewable-energy productivity, efficient operations, local technical capability and evidence-based asset management.
Preoperative anxiety in children remains a persistent perioperative problem because it can impair cooperation during anaesthesia induction, increase distress for families, and contribute to postoperative pain, emergence agitation and maladaptive behavioural change. This review compares parental presence during induction with distraction-based strategies, including video games, tablets, audiovisual preparation, virtual reality, therapeutic play, music and tailored cartoon video. A structured literature search was undertaken across major biomedical databases and reference lists for English-language studies published from 2020 to 2025. Priority was given to randomized trials, systematic reviews, network analyses and clinically relevant observational studies involving children undergoing surgery or anaesthesia. Recent evidence suggests that parental presence is valued by many families and may reduce anxiety when parents are calm, prepared and supported, yet its effects are inconsistent when used alone. Distraction techniques show a more consistent anxiolytic signal, especially when active, developmentally matched and continued from holding area to induction. The strongest clinical approach is not a rigid choice between family presence and distraction, but a stepped preventive bundle that screens child and parent anxiety, offers parental preparation, selects a child-centred distraction method, preserves safety, and audits outcomes. The review concludes that distraction should be treated as a core non-pharmacological intervention, while parental presence should be individualized rather than used as a universal policy. Future work should standardize anxiety measurement, compare combined strategies, include parental outcomes, and test feasibility across resource-variable settings.
Hassan Ali Khan Mohammed
Glob Acad J Econ Buss; 2026, 8(5): 1670-1681
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.054
Saudi Arabia is making the transition from an electricity system based mainly on hydrocarbons to one in which solar photovoltaic and wind power make up a much larger share, and this is leading to a demand for simulation tools that are able to account for variability, converter-related dynamics, storage, demand flexibility, and network constraints all within a single, coherent decision-making process. This review brings together peer-reviewed research carried out between 2020 and 2025 on the integration of renewable energy, smart-grid modelling, forecasting, hybrid systems, storage, inverter control, hosting capacity, and applications specific to Saudi Arabia. The findings indicate that no single type of simulation is adequate; quasi-static time-series and techno-economic models are useful for planning and for determining capacity sizes; probabilistic methods are able to take into account uncertainties relating to weather and demand; phasor-domain dynamic models are suitable for studies on electromechanical stability; electromagnetic-transient models are required when inverter controls and interactions with weak grids are the main factors; and data-driven or digital-twin approaches can enhance forecasting and enable online updating of the models. Studies specific to Saudi Arabia show that there is a considerable resource potential but also highlight sensitivity to high ambient temperatures, dust, the uneven distribution of wind resources across the geographical area, demand that is dominated by cooling needs, the economics of storage, and the operating assumptions made in the optimisation process. The review presents an integrated, multi-timescale framework that connects resource forecasting, component models, network constraints, energy management, uncertainty analysis, and validation. It contends that future research into smart grids in Saudi Arabia should go beyond separate optimal-sizing exercises and instead focus on reproducible, grid-aware, uncertainty-aware simulations that have been validated under a range of seasonal and disturbance conditions. The key areas that should receive priority include the assessment of grid-forming inverters, probabilistic hosting capacity, hybrid storage, dynamic demand response, high-resolution resource forecasting, and digital-twin-assisted operation. Pursuing these avenues will help to strengthen the technical credibility of the decisions regarding the deployment of renewable energy while at the same time supporting reliability, flexibility, and economic efficiency.
In asset-intensive manufacturing, spare-parts demand is irregular, equipment condition changes over time, and conventional reorder rules may not exploit enterprise and machine data. This article examines how artificial intelligence can connect demand forecasting, asset-health information and inventory decisions within a predictive decision-support framework for manufacturing in Saudi Arabia. A structured narrative review synthesises 29 journal publications from 2020 to 2025 covering spare-parts forecasting, predictive maintenance, digital twins, inventory optimisation, reinforcement learning and explainable artificial intelligence. The evidence is organised by decision problem, data foundation, analytical method, operational output and boundary of application; Saudi policy material provides context, while Project Management Institute guidance informs a separate lifecycle-governance adaptation. The review is not presented as an exhaustive systematic search or meta-analysis. The synthesis supports evaluating forecasts through the inventory decisions they inform and shows how demand estimates and equipment condition information can be connected to replenishment and maintenance decisions. Building on this evidence, the paper proposes a five-layer framework linking industrial data, context and feature engineering, predictive services, constrained inventory optimisation, and governed execution and learning. Proposed governance controls include review checkpoints, assigned decision rights, bounded automation and auditable feedback. The framework is conceptual and has not been validated in Saudi manufacturing plants. It provides a staged pilot pathway from data readiness and criticality segmentation to condition-informed forecasting and closed-loop optimisation, while requiring plant specific assessment of technical compatibility, organisational readiness and economic value.
Inclusive digital transformation depends on whether mobile services remain usable for people whose abilities, devices, languages, literacy, and connectivity differ from the assumptions embedded in mainstream interfaces. This review develops a Saudi Vision 2030 framework for accessible mobile applications by synthesizing research published from 2020 to 2025 on disability, mobile accessibility, assistive technologies, usability, digital inclusion, and public-service adoption. A structured integrative review was used because the evidence spans human-computer interaction, digital health, software quality, and Saudi e-government research rather than a single experimental tradition. Findings show that accessibility is strongest when semantic structure, Arabic and right-to-left behavior, multimodal interaction, responsive performance, and recovery from errors are treated as one service-quality system. Evidence concerning people with visual, hearing, motor, cognitive, and age-related needs further indicates that compliance checks alone cannot establish inclusion; task-based testing with representative users and assistive technologies remains necessary [1-8]. The review proposes a closed-loop architecture in which user evidence, technical telemetry, reusable components, and governance continuously refine service standards. It also presents a staged implementation roadmap linking inclusive design to equitable access, trusted service completion, digital participation, and institutional learning. The central implication is that accessible mobile delivery should be governed as shared digital infrastructure rather than as a late-stage interface correction.
Abdul Rauf
Glob Acad J Econ Buss; 2026, 8(5): 1708-1716
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.057
Large-scale catering operations in Saudi Arabia are expanding while major catering, remote-site catering, energy, logistics and industrial programmes increase the scale and complexity of occupational risk. Conventional food safety management remains essential, yet lagging indicators, periodic inspections and rule-based controls often identify deterioration only after exposure has accumulated. This structured review develops an risk-based digital food safety and HACCP governance framework suited to Saudi operating conditions. It integrates leading indicators from records, inspections, sensors, equipment systems, food-service workforce records, environmental monitoring and food-safety incident narratives; applies transparent risk models; and routes predictions through human review and proportionate controls. The synthesis shows that digital food-safety analytics can improve prioritisation, early-warning capability and learning across dispersed sites, but only when data quality, privacy, food-service workforce participation, model drift, cybersecurity and accountability are governed explicitly. The proposed framework therefore treats digital food-safety analytics as a decision-support layer within an ISO 22000-aligned management system, not as an autonomous replacement for competent safety judgement. Its principal contribution is a risk-based operating model linking data governance, prediction, verification, intervention and continuous assurance to Saudi Vision 2030 objectives.
Fazal Ahmed Khan
Glob Acad J Econ Buss; 2026, 8(5): 1717-1730
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.058
Saudi Arabia’s Vision 2030 is accelerating the delivery of large-scale urban, tourism, transport, industrial and infrastructure projects while simultaneously strengthening national ambitions for environmental protection, resource efficiency and waste diversion. The scale of this development creates a strategic challenge: construction and demolition waste cannot be managed only as an end-of-pipe disposal problem. It must be addressed through a circular economy model that prevents waste, preserves material value, enables reuse and high-value recycling, and creates dependable markets for secondary construction materials. This paper develops a Saudi-contextualised pathway for integrating circular economy principles with construction waste management in mega-projects. A structured narrative review was undertaken using recent peer-reviewed literature, Saudi policy and regulatory sources, international circular-economy standards, and contemporary research on digital construction and material passports. The review identifies persistent barriers including fragmented responsibilities, limited lifecycle awareness, insufficient source segregation, inconsistent quality assurance for recovered materials, weak secondary-material demand, data gaps, and inadequate contractual incentives. In response, the paper proposes an integrated Circular Construction and Waste Management Framework organised around six linked stages: prevention and demand reduction; circular design; circular procurement; construction-stage segregation and control; recovery and quality assurance; and reintegration of secondary materials into new projects. Digital traceability, governance, market development and performance measurement operate across all stages. The framework introduces practical indicators for material efficiency, waste intensity, source segregation, diversion, reuse, recycled content and traceable recovery. Saudi Arabia’s reported rise in national waste diversion from 5% in 2018 to 18% in 2025, alongside a national 90% diversion target by 2040, demonstrates the wider policy direction in which construction circularity must operate. The study concludes that mega-projects can become demand anchors for circular material markets when client specifications, design decisions, contractor controls, recycling infrastructure and digital information systems are aligned. Empirical validation across active Saudi projects is recommended to quantify sector-specific benchmarks and test the proposed indicators.
Abdul Waheed
Glob Acad J Econ Buss; 2026, 8(5): 1731-1741
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.059
Saudi Arabia’s insurance sector is expanding while simultaneously undergoing regulatory, technological and organizational transformation. Under Vision 2030, digital infrastructure, data and artificial intelligence have become explicit enablers of insurance-sector development, creating a strong policy context for redesigning underwriting. This paper examines how digital transformation can improve risk selection, pricing adequacy and operational efficiency in Saudi insurance underwriting while preserving governance, fairness and professional accountability. An integrative review approach is used to synthesize recent peer-reviewed insurance, artificial-intelligence and financial-technology literature with official Saudi policy and market evidence. The analysis identifies five mutually dependent transformation capabilities: integrated and high-quality data, advanced analytics and artificial intelligence, digitally orchestrated workflows, model governance and explainability, and underwriting talent capable of combining actuarial judgment with digital tools. The paper argues that the greatest value does not arise from replacing underwriters, but from redesigning underwriting as a human-in-the-loop decision system in which routine cases can be automated, complex risks are escalated, and pricing decisions are supported by transparent analytics. Saudi market evidence demonstrates rapid premium growth and an increasingly explicit regulatory focus on technology, data and artificial intelligence. However, implementation risks include fragmented data, algorithmic bias, privacy and cybersecurity concerns, legacy-system integration, model drift and inadequate change management. A Saudi Underwriting Digital Transformation Framework is therefore proposed, linking digital foundations, analytics, workflow redesign, governance safeguards and measurable performance outcomes. The framework provides a research and managerial agenda for insurers seeking to improve technical profitability, customer responsiveness and market resilience while aligning digital underwriting with the objectives of Vision 2030.
Smart electrical distribution networks are becoming more observable, automated, and data intensive, yet maintenance practice often remains dominated by periodic inspection or intervention after alarms and failures. This review develops a predictive maintenance framework that connects artificial intelligence and data analytics with the operational realities of distribution transformers, on-load tap changers, switchgear, circuit breakers, feeders, cables, substations, smart meters, and distributed-energy interfaces. The evidence base is restricted to peer-reviewed work published from 2020 to 2025 and is synthesized through a structured integrative review focused on data provenance, condition indicators, learning tasks, validation practice, interpretability, uncertainty, deployment constraints, and maintenance decision linkage. The review shows that useful predictive maintenance cannot be reduced to selecting a high-performing classifier. Reliable deployment requires a chain of asset-aware sensing, data-quality control, representation learning, anomaly detection, fault diagnosis, health forecasting, uncertainty estimation, consequence assessment, and human-governed maintenance orchestration. Deep learning and ensemble methods improve representation of nonlinear and temporal degradation, while digital twins can provide synchronized context, scenario testing, and traceability; however, scarce fault labels, changing operating regimes, class imbalance, fragmented semantics, cyber risk, and weak external validation remain major obstacles. A layered framework is therefore proposed in which analytical outputs are converted into risk-calibrated maintenance actions rather than isolated predictions. The framework emphasizes evidence quality, cross-asset generalization, explainable reasoning, model monitoring, and feedback from completed work orders. It provides a research and implementation architecture for moving smart distribution maintenance from condition visibility toward dependable, auditable, and economically meaningful predictive decision support.
Saudi Arabia's infrastructure expansion requires assurance systems that match the speed, complexity, and sustainability expectations of Vision 2030. This review examines how automated inspection, digitally enabled calibration, and metrological traceability can be integrated into a coherent quality architecture for infrastructure delivery. The literature indicates that computer vision, uncrewed aerial vehicles, laser scanning, structural health monitoring and digital twins can accelerate defect detection and condition assessment. Still, their decisions remain trustworthy only when the underlying measurements, sensor states, and uncertainty information are traceable. Digital calibration certificates provide a machine-readable mechanism to carry calibration status, uncertainty, and provenance into automated workflows. In contrast, BIM and digital twins provide the asset context needed to convert measurements into engineering decisions. Evidence from construction and asset-management research further shows that automation is not intrinsically sustainable: benefits depend on interoperability, cybersecurity, competence, data governance, risk-based human oversight, and lifecycle use of verified data. The review develops an integrated traceability-to-decision framework for Saudi infrastructure projects, linking calibrated sensing, automated inspection, digital evidence, engineering acceptance and lifecycle feedback. It argues that traceability should be treated as digital infrastructure rather than a laboratory back-office function. It proposes research and policy priorities for scalable implementation across giga-projects, transport, utilities and public assets.
Tauseef Muhammad Rafiq
Glob Acad J Econ Buss; 2026, 8(5): 1765-1775
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.062
Digital twins are increasingly used as operational interfaces that connect physical assets, engineering models, and instantaneous data. However, their environmental and safety benefits remain fragmented in oil and gas and construction research. This review develops an integrative framework for Saudi Arabian facilities and projects, where rapid infrastructure delivery, challenging climate, and high-risk operations require proactive risk management. The review systematically synthesizes peer-reviewed literature from 2020 to 2025, using Scopus, Web of Science, IEEE Xplore, and other databases. It compares digital-twin architectures, sensing strategies, predictive analytics, construction safety applications, process-integrity uses, environmental monitoring, and Saudi-specific implementation factors. Evidence shows that digital twins can improve risk visibility by integrating asset context with live data, support scenario-based decisions before intervention, and link environmental indicators to work controls. Realizing these benefits depends on synchronization quality, model validation, data interoperability, cybersecurity, human oversight, and lifecycle governance. The literature is uneven: construction studies often focus on localized safety or monitoring, while oil and gas studies emphasize asset integrity and operational optimization, with few integrated environmental-safety frameworks. This paper proposes a layered, closed-loop framework that integrates physical sensing, BIM and engineering data, physics and AI models, environmental and safety intelligence, predictive decision support, governance, and continuous learning. In Saudi Arabia, the framework is designed to support auditable, human-supervised decisions for sustainable project delivery and resilient operations, rather than serve as an autonomous control system.
Muhammad Hamza Muhammad Amin
Glob Acad J Econ Buss; 2026, 8(5): 1776-1785
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.063
Saudi enterprises with significant debt obligations encounter a complex treasury challenge: liquidity must sustain daily operations while guaranteeing debt-service capacity amid oscillating interest rates, payment delays, supply disruptions, and evolving refinancing conditions. This integrative review explores the combined governance of treasury and working capital risk management in debt-financed Saudi enterprises, moving beyond the traditional separation of these finance functions. Recent research on working capital, corporate liquidity, debt maturity, hedging, supply-chain finance, and Saudi corporate finance is synthesized using a risk-transmission framework. The findings indicate that cash conversion policy is most effective when managed as a resilience portfolio rather than through simple cycle minimization. Inventory buffers, receivable terms, supplier payments, committed liquidity, debt tenor, interest-rate exposure, covenant headroom, and cash reserves interact in nonlinear ways; enhancing one metric may undermine another if treasury decisions are made in isolation from operations. Saudi-specific evidence further demonstrates that financing constraints and macro-financial conditions influence the reliance on internal cash for investment. Additionally, recent studies highlight the significance of debt maturity, Sharia-compliant capital structures, and precautionary cash holdings. This paper develops an integrated treasury-working-capital risk architecture for Saudi firms, identifies control indicators for boards and treasury committees, and outlines research priorities related to covenant risk, cash-flow-at-risk, supply-chain finance, sukuk-linked liquidity, and sector heterogeneity. The central conclusion is that debt-financed enterprises should prioritize liquidity reliability, rather than solely accounting liquidity, by aligning operating-cycle cash, committed funding, and debt-service obligations within a unified, stress-tested governance framework.
Electrical and utility procurement is an economically consequential governance function because award decisions influence transaction costs, supplier incentives, capital efficiency, schedule reliability, lifecycle expenditure, and the development of domestic productive capability. This paper develops a governance-based Tendering Excellence Framework (TEF) for power, substation, transmission, distribution, and related utility projects. A structured conceptual synthesis integrates transaction cost economics, strategic procurement, value-for-money guidance, project and risk governance, asset management, contractor-selection research, and Saudi procurement requirements. The resulting six-stage model links procurement strategy, tender design, controlled bid management, technical-commercial-risk evaluation, governed award, and post-award learning. Its principal contribution is to connect mandatory compliance gates with a weighted scorecard and an economic-value logic that distinguishes acquisition price from total evaluated cost, risk exposure, lifecycle value, and localization impact. The TEF therefore positions tendering as a mechanism for reducing information asymmetry, limiting opportunism and costly contract adaptation, improving capital allocation, and strengthening supplier accountability. In the Saudi Vision 2030 context, the framework provides a practical route for aligning infrastructure procurement with institutional effectiveness, private-sector development, domestic capability formation, and reliable delivery. The model is conceptual and its weights are illustrative; empirical validation should test whether stronger TEF maturity predicts lower procurement-cycle cost, fewer claims, improved schedule and cost performance, and higher verified local-content outcomes.
Mohammed Shebin
Glob Acad J Econ Buss; 2026, 8(5): 1796-1806
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.065
Saudi Arabia’s giga-project programme brings together major capital investment, numerous interfaces, multiple contractors, imported materials, off-site production, and regulatory dependencies within compressed delivery periods. As a result, conventional project-control and construction supply-chain practices lose effectiveness when they operate as separate reporting activities. This article gives a critical examination of how integrated project control and construction supply-chain technologies can be used to enhance cost, schedule, and risk management in Saudi giga-projects. It uses a structured integrative review method to combine recent peer-reviewed research on earned value management, 4D/5D building information modelling, sensing-based progress monitoring, digital twins, artificial intelligence, blockchain, and digital supply-chain platforms with studies that are specific to Saudi conditions concerning delay, cost overruns, BIM adoption, and project governance. The synthesis indicates that technology generates value primarily when the data structures, control accounts, procurement packages, logistics milestones and risk registers are aligned with a common work breakdown and decision schedule. BIM and digital twins enhance the visibility of spatiotemporal information; sensing and analytics improve the verification and prediction of progress; blockchain can improve the traceability of transactions; and supply-chain visibility allows for earlier responses to interruptions in materials, fabrication and logistics. Yet, merely introducing technology in isolation can lead to the digital replication of fragmented processes. The review thus proposes an integrated control-to-supply chain framework that links baseline, actual, forecast and risk information across the owner, programme manager, contractor, supplier and logistics levels. The paper concludes that the success of Saudi giga-projects depends less on acquiring individual technologies and more on establishing interoperable data governance, following disciplined project-control procedures, obtaining reliable field evidence, and having contractual incentives to promote transparent information sharing between organisations.
Mohammad Ahsan
Glob Acad J Econ Buss; 2026, 8(5): 1807-1817
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.066
Purpose: This review examines how credit risk governance and risk-based internal audit can jointly strengthen the quality, resilience and developmental contribution of debt financing in Saudi Islamic banks. The central argument is that effective oversight requires more than separate board, risk and Shariah compliance arrangements: it requires a traceable chain connecting financing decisions, forward-looking risk signals, independent assurance and corrective action. Design/Methodology: A structured critical integrative review synthesises 30 DOI-identifiable journal articles published during 2020–2025, selected for their relevance to Islamic banking, credit risk, governance, assurance and the Saudi or comparable regional context. Findings: Evidence favours a differentiated approach. Board and Shariah oversight are important, but their effects vary across institutional settings; independent audit is useful only when its remit, expertise, access and follow-up are credible. The proposed framework links risk appetite, transaction-level controls, portfolio surveillance, audit prioritisation and accountable remediation. Originality: Rather than treating governance and audit as parallel functions, this paper specifies their interfaces, evidential requirements and failure points across the financing life cycle. Implications: The framework offers Saudi banks a practicable basis for risk-sensitive financing expansion consistent with Vision 2030, without assuming that any single governance structure guarantees lower defaults. The review does not report original bank-level estimates; its propositions require future validation.
Amaan Ullah Sharif Mohammed
Glob Acad J Econ Buss; 2026, 8(5): 1818-1828
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.067
Saudi Arabia's Vision 2030 construction programme is increasing the scale and complexity of tendering across infrastructure, buildings, tourism, transport and giga-project supply chains. Conventional bid evaluation remains vulnerable to inconsistent scope interpretation, fragmented tender documents, manual arithmetic checks, evaluator subjectivity and weak traceability between technical and commercial decisions. This review examines how artificial intelligence can support tender normalization and bid evaluation without displacing professional judgement or procurement accountability. Evidence published from 2020 to 2025 was synthesised across construction procurement, contractor selection, construction supply-chain digitalisation, natural language processing, explainable machine learning, supplier selection and AI-assisted bidding. The literature indicates that the principal opportunity is not autonomous award selection but the creation of a governed decision-support layer that converts heterogeneous tender submissions into a common comparison basis. Natural language processing can extract qualifications, exclusions, scope assumptions, programme commitments and contractual risks; rules and structured analytics can reconcile quantities, currencies, unit rates, provisional sums and arithmetic differences; and explainable models can support technical, commercial, sustainability and supplier-risk scoring. However, automation also creates risks of hidden bias, data leakage, inconsistent criteria, overreliance, algorithmic collusion and weak legal defensibility. This review therefore proposes a six-stage framework comprising data ingestion, document intelligence, tender normalization, multi-criteria evaluation, human governance and award recommendation, with project-performance feedback used to recalibrate future models. For Saudi Vision 2030 projects, the framework offers a route to faster, auditable procurement at scale while preserving fairness, local-content considerations, competition and accountable human approval.
Purpose: Saudi enterprises increasingly manage employment records, payroll calculations and workforce operations through different applications, yet connecting these functions reliably is more demanding than moving them to cloud infrastructure. This review examines the architectural, organisational and assurance conditions under which integration can improve service quality while protecting employees and meeting national obligations. Method: A structured integrative review critically synthesises 30 DOI-identified publications dated 2020–2025, spanning digital human resource management, cloud enterprise systems, attendance technologies, software architecture and Saudi adoption evidence. Papers were grouped by the integration problem they illuminate; no primary organisational data were collected. Findings: The literature indicates that common data definitions, governed events, transparent calculation rules and reconciliation controls deserve priority over indiscriminate platform consolidation. Cloud scalability alone cannot correct inconsistent employment records, disputed attendance or fragmented accountability. Contribution: An original five-layer reference architecture and an assurance-led implementation cycle connect employee experience, operational processing and auditable compliance. Implications: Enterprises should assess success through payroll accuracy, exception resolution, accessibility and recoverability alongside infrastructure availability. The framework is a conceptual proposal requiring field validation, not a demonstrated national deployment.
Salah Ali Ibrahim Ramadan
Glob Acad J Econ Buss; 2026, 8(5): 1840-1850
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.069
Saudi Arabia’s shift towards prevention, integrated care and digital health creates an opportunity to use artificial intelligence at population scale rather than only for isolated diagnosis. This review examines how AI-enabled population health analytics can combine clinical, administrative, insurance, pharmacy, laboratory and patient-generated data to predict chronic disease risk and preventable hospital admissions. Thirty peer-reviewed studies published from 2020 to 2025 were synthesised across Saudi health-system transformation, digital maturity, chronic disease prediction, electronic records, remote monitoring, risk stratification, readmission prediction, explainable AI and avoidable hospitalisation. The evidence shows that machine-learning models can identify elevated risk for diabetes, cardiovascular disease, sepsis, emergency use and readmission, but predictive performance alone does not create prevention. Population benefit depends on reliable longitudinal data, clinically meaningful prediction horizons, transparent risk thresholds, human review and direct links to primary-care outreach, screening, medication review, case management and remote monitoring. Claims and encounter data provide breadth, while laboratory values, vital signs, clinical notes and patient-reported information improve clinical specificity. The review proposes a six-layer Population Health Intelligence Framework comprising multi-source data integration, longitudinal patient resolution, AI risk modelling, clinical validation, preventive care orchestration and outcome-based model learning. Preventable admissions should be treated as a system outcome requiring both accurate risk detection and timely access to effective primary care. For Saudi Arabia, the framework aligns predictive analytics with Vision 2030 goals for prevention, efficiency and patient-centred care while emphasising interoperability, privacy, fairness, explainability and accountable clinical governance.
Application programming interfaces have now turned into operational dependencies rather than just being used for peripheral integration in digital government. In Saudi Arabia, the expansion of platform connectivity as part of Vision 2030 is leading to greater consequences from contract defects, authorization errors, inconsistent data exchange, latent service dependencies, and performance regressions. This review presents an advanced framework for API testing and integration validation aimed at ensuring the reliability of digital government systems by drawing on recent evidence from 2020 to 2025 in the areas of REST API testing, microservice assurance, reliability engineering, cybersecurity, and research into Saudi digital government. It finds a consistent gap between endpoint-level functional testing and the more comprehensive assurance needed for public services that involve identity, data, payment, notification, legacy systems, and inter-agency components. The framework proposed therefore includes five interconnected controls: contract baselining, intelligent risk-weighted test generation, integration-chain validation, resilience and security verification, and an evidence-based reliability gate backed by operational feedback. The synthesis contends that test adequacy should be evaluated on the basis of evidence from the service chain rather than simply counting response codes, and that API quality must take into account traceability, awareness of dependencies, recoverability, privacy, and the impact on citizens. Two implementation tables and two conceptual figures are provided to convert the literature into a model suitable for deployment by Saudi government organisations. The framework offers a coherent architecture based on a review of the literature that links technical testing evidence with governance decisions, thus enabling the continuous validation of rapidly changing digital services while maintaining interoperability, service continuity, and public trust.
The move towards a cloud-based digital transformation has taken enterprise reliability from being merely a concern of the back office to making it a strategic capability. In Saudi Arabia this transition is supported by Vision 2030, the quick adoption of cloud technology, national investment in artificial intelligence, and the increasing reliance by both public and private organisations on digital services that are always available. Although traditional IT service management is still useful for controlling processes, its reliance on manual triage, monitoring based on thresholds, fragmented telemetry, and human-led diagnosis is becoming more and more strained as a result of distributed cloud architectures. This study brings together research that was published between 2020 and 2025 on the use of artificial intelligence in IT operations, cloud failure prediction, log and trace analytics, incident intelligence, root-cause analysis, automated remediation, and intelligent service desks. The objective is to see how these capabilities can be combined into a governable operating model for businesses in Saudi Arabia rather than being implemented as separate algorithms. The evidence shows that AI can enhance early detection of failures, the classification of incidents, prioritisation, causal diagnosis, knowledge retrieval, and certain remediation tasks, but its performance is very much dependent on the quality of the telemetry, the service topology, controls against model drift, explainability, and the limits set on autonomous action. The paper puts forward a framework for AI-enabled Cloud Operations and Intelligent IT Service Management based on five layers: observability, context, intelligence, orchestration, and governance. It also defines a closed-loop assurance lifecycle in which machine-generated recommendations are continuously assessed in relation to service-level, risk, security, and business outcomes. The review concludes that the most significant contribution of Vision 2030 will be achieved through the controlled enhancement of human operations, not through uncontrolled automation, and it identifies key areas such as Arabic-aware service intelligence, cross-enterprise benchmarking, sovereign data controls, and measurable human-AI accountability.
Mohammed Vaquar Ahmed Haseeb
Glob Acad J Econ Buss; 2026, 8(5): 1875-1886
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.072
The reliability of cloud services is now increasingly determined by how safely organizations can modify their software, rather than just by the redundancy of their infrastructure. Although continuous integration and continuous delivery pipelines have shortened the time between releases, problems such as static rules, fragmented telemetry, manual approval hurdles, and a lack of feedback between deployment and operations can cause failures to become worse as cloud systems become more distributed. This study looks at the way in which artificial intelligence can be incorporated into DevOps and into automated CI/CD pipelines in order to enhance reliable cloud service delivery in Saudi Arabia. A structured integrative review of peer-reviewed research published between 2020 and 2025 was carried out in the fields of software engineering, AI-assisted testing, AIOps, observability, cloud automation, and literature on Saudi cloud adoption. Thirty studies, verified by their DOIs, were analysed using five analytical dimensions: pipeline stage, AI function, reliability mechanism, governance requirement, and relevance to Saudi enterprise adoption. The evidence shows that AI provides the most significant improvement in reliability when it functions as a bounded decision-making layer based on high-quality telemetry, rather than as an unbounded substitute for engineering judgment. Together, predictive detection of build failures, intelligent regression testing, deployment-risk scoring, anomaly detection, root-cause localisation, and feedback-driven rollback can collectively reduce the risk resulting from frequent releases. Nevertheless, the benefits in terms of reliability depend on the use of traceable data, policy controls, progressive delivery, explainable decisions, and human authority for important changes. The review puts forward a closed-loop reference model that connects code changes, automated verification, risk assessment, controlled release, runtime observability, and learning. For Saudi enterprises, the model stresses the importance of trust, service-level assurance, governance, organizational readiness, and the adoption of locally appropriate cloud operating practices. The paper ends with specific implementation priorities and a research programme aimed at validating the approach on a production scale.
Waqas Ahmad
Glob Acad J Econ Buss; 2026, 8(5): 1887-1898
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.073
Mission critical data centres require continuous power, stable thermal conditions, reliable sensing and disciplined maintenance. As Saudi Arabia ramps up its cloud, AI and digital service infrastructure under Vision 2030, the traditional approach of calendar based maintenance is increasingly inadequate for the operation of such tightly coupled, high availability facilities. This paper reviews the application of AI to enhance predictive maintenance and thus the resilience of mission critical data centre infrastructure in Saudi Arabia. A structured integrative review of the literature was undertaken (2020 to 2025) with a focus on data centre power and cooling assets, batteries, sensors, storage, digital twins, anomaly detection, fault diagnosis, remaining useful life estimation, transfer learning, explainability and maintenance prioritisation. The synthesis indicates that the greatest resilience value is achieved when the predictions are tied to asset criticality, redundancy state, intervention lead time, uncertainty and the existence of controlled operational workflows rather than simply as a standalone measure of prediction accuracy. The strongest evidence base is for battery health assessment, cooling system fault diagnosis, sensor validation, interpretable component failure prediction, transfer learning with limited fault data and digital twin enabled monitoring. The key remaining barriers are sparse failure labels, concept drift, site heterogeneity, imbalanced data, uncertain generalisability and poor integration of the analytical output into engineering governance. A proposed Saudi implementation framework would start with telemetry and asset criticality governance, move through a series of well defined use cases and model assurance and finish with a closed loop maintenance learning process. The paper concludes that AI enabled predictive maintenance can deliver a significant contribution to resilient digital infrastructure when it is deployed as an accountable engineering decision support capability with explicit human oversight, validation, cybersecurity and post intervention verification.
Saudi Arabia is aiming to develop an electricity system that is more reliant on renewables, more digital, and more distributed, and as a result the importance of having a resilient distribution grid is growing. This study looks at how distribution network planning, testing and asset management can be combined in order to minimise service interruptions, manage lifecycle risks and deal with variable renewable generation, storage and flexible demand. The research carried out between 2020 and 2025 is organised around four closely related functions: risk-informed planning, verification and testing, condition-based asset management, and adaptive operation. It has been found that resilience cannot be achieved through the use of a single technology; rather, it arises as a result of coordinated decisions regarding topology, distributed energy resources, storage, protection, automation, equipment health, maintenance and restoration. Studies specific to Saudi Arabia show that expanding renewable energy capacity can improve adequacy, while probabilistic planning of storage can reduce losses and delay the need for network reinforcement; but these advantages depend on reliable models of uncertainty, protection coordination, the quality of the data and the state of the assets. The review suggests an integrated resilience lifecycle in which the assumptions made during planning are checked by means of studies and field tests, operating events update the asset health and the probabilities of failure, and decisions relating to maintenance and investment are then fed back into future network plans. Special attention is paid to the effects of extreme heat, dust, the intermittency of renewable energy, cybersecurity issues and the ageing of key transformer assets. The paper ends with a research programme that focuses on data from Saudi distribution systems, climate-conditioned failure models, hardware-in-the-loop validation, interoperable asset-health indices and the economic valuation of resilience. The framework thus provides utilities with a practical basis for linking their investments in resilience to their reliability, sustainability and Vision 2030 goals.
Saudi Arabia is speeding up the digital transformation of its urban infrastructure as it works to achieve safer cities, greater efficiency, and objectives set out in Vision 2030. The Internet of Things (IoT) is important since it connects distributed sensors, connected assets, communication networks, edge and cloud platforms, and analytics to operational control. This study looks at how smart-city infrastructure enabled by IoT can improve urban safety and operational efficiency in Saudi Arabia, without assuming that the simple deployment of technology will lead to public value. A structured integrative review of 30 peer-reviewed studies published between 2020 and 2025 was carried out covering the topics of Saudi smart-city development, IoT architectures, edge computing, digital twins, mobility, water, energy, cybersecurity, governance, and citizen participation. The analysis reveals two main pathways for impact. The first is a safety pathway in which sensing, situational awareness, anomaly detection, incident coordination, and robust communications reduce the time taken to detect and respond to incidents. The second is an efficiency pathway in which continuous monitoring, predictive analytics, adaptive control, and asset intelligence cut down delays, minimise resource losses, lower energy consumption, and prevent unnecessary maintenance. It is also found that interoperability, cybersecurity, data governance, institutional coordination, and the establishment of measurable performance benchmarks are essential prerequisites for turning IoT capabilities into real outcomes at the city level. The paper suggests a layered Saudi IoT smart-city framework that links physical assets to decision-making intelligence and operational action, along with an outcome model that connects technical capabilities to safety, efficiency, resilience, and quality of life. The review concludes that the best alignment with Vision 2030 will be achieved when IoT investments are designed around verifiable urban problems, an interoperable architecture, risk-based governance, and outcome-oriented indicators rather than on the number of devices or on isolated pilot projects.
Ahmed Helmi
Glob Acad J Econ Buss; 2026, 8(5): 1922-1933
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.076
Fleet management based on connectivity is now evolving from simple location tracking towards becoming a cyber-physical mobility capability in which vehicles, roadside infrastructure, cloud platforms and operational teams constantly share data. This article looks at how Internet of Things technologies can help to achieve smart and sustainable fleet management in Saudi Arabia, with a focus on real-time visibility, predictive maintenance, route and energy optimisation, safety, interoperability, cybersecurity and environmental performance. A structured review of 30 peer-reviewed studies verified by DOI was carried out between 2020 and 2025. The evidence was organised using thematic synthesis rather than statistical aggregation since the body of literature includes a wide variety of fleet types, communication technologies, optimisation models and evaluation scenarios. The synthesis indicates that efficient fleet platforms rely on five interacting layers: reliable vehicle sensing, low-latency connectivity, edge-cloud data processing, decision intelligence, and governance that leads to measurable sustainability outcomes. The greatest operational benefits occur when these layers function as a closed loop so that telemetry data can be turned into actions such as changing routes, scheduling maintenance, giving driver feedback and making decisions about charging. Yet having good technical connectivity by itself does not ensure sustainable mobility; the benefits depend on the quality of the data, cybersecurity, interoperability, the electrification of the fleet, the readiness of the infrastructure and co-ordination at an institutional level. For Saudi Arabia, the literature recommends a step-by-step architecture that combines current telematics with 5G and vehicle-to-everything services, scalable edge-cloud analytics, common data models and performance governance. The review also suggests an implementation framework tailored to Saudi conditions and a number of indicators relating to operational performance, the environment, safety and digital resilience which are appropriate for fleet operators and mobility authorities.
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Dr. Sumathi Kumaraswamy
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Assistant Professor, Department of Management Studies, Deenbandhu Chhotu Ram University Murthal, Sonepat, Haryana, India Email: jitenderkbatra@gmail.com
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