Operational Efficiency Research Articles

Accepting Technology in Aviation Safety Risk Management: an extension of the technology acceptance model

Aviation safety is paramount, and advancements in technology play a pivotal role in mitigating risks and enhancing operational efficiency. The Technology Acceptance Model (TAM) has been widely utilised to understand the adoption of various technologies across industries. However, its application within the context of aviation risk assessment requires nuanced considerations due to the unique operational environment and stringent safety requirements. This paper critically reviews existing literature on TAM and its adaptations in aviation risk assessment, identifying limitations and gaps. Drawing from interdisciplinary insights in psychology, human factors, and aviation safety, this paper proposes an enhanced framework for TAM tailored specifically to the aviation industry. The proposed model integrates key constructs such as perceived usefulness of technology in this area, trust in technology, system complexity, and organizational factors to provide a comprehensive understanding of technology acceptance within aviation risk assessment practices. By enhancing the TAM framework, this paper aims to offer valuable insights for researchers, practitioners, and regulators involved in aviation safety management and technology integration efforts.

Sustainable Practices in Brazilian Ports: Aligning with SDGs 9, 13, and 14

Objective: This study aimed to analyse the disclosure of the Sustainable Development Goals (SDGs) in reports published by ports and terminals with the highest cargo movements. Theoretical Framework: The research is grounded in key concepts of sustainability and sustainable development, focusing on the Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure), SDG 13 (Climate Action), and SDG 14 (Life Below Water). Method: This exploratory and descriptive research employed content analysis of sustainability, integrated, and annual reports from Brazilian public ports (BPP) and private port terminals (PPT) to identify key sustainable practices aligned with SDGs 9, 13, and 14. Results and Discussion: The study highlighted the Brazilian port sector's focus on developing sustainable mechanisms, driven by technologies such as radars and cameras, and fostering investments in research and development, reinforcing a commitment to SDG 9. Likewise, initiatives targeting the reduction of Greenhouse Gas (GHG) emissions, enhancing operational efficiency, and supporting the energy transition were prominent, demonstrating alignment with SDG 13. However, fewer practices were observed for SDG 14. Research Implications: The findings have significant implications for both academia and the port sector. The clear focus on SDGs 9 and 13 within the Brazilian port industry underscores the sector's commitment to innovation and climate action. Conversely, the study revealed a substantial gap in practices aligned with SDG 14, despite the strong connection between ports and marine ecosystems.

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

AbstractAquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework

The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addressing critical supply chain issues, including inventory optimization, demand variability, and regulatory compliance. The proposed framework details essential components—data collection, processing, integration with existing systems, and real-time decision-making—and outlines critical success factors for effective implementation. By offering insights into the transformative potential of predictive analytics, this paper provides actionable recommendations for pharmaceutical companies seeking to build resilient and responsive supply chains. Future research directions are also proposed, emphasizing the need for adaptable predictive models and the ethical implications of AI applications in pharmaceuticals. Keywords: Pharmaceutical Supply Chain, AI-driven Predictive Analytics, Machine Learning, Demand Forecasting, Inventory Optimization, Regulatory Compliance.

Financial Ratio Analysis of Tata Motors Limited

This report presents a comprehensive financial ratio analysis of Tata Motors Limited, focusing on its performance in the automobile industry. The analysis aims to evaluate the company's financial health and operational efficiency through various financial ratios. The findings are based on data collected during a summer internship at Tata Motors, providing insights into the company's strengths and weaknesses in the competitive market.

Applications of integrating artificial intelligence and big data: A comprehensive analysis

Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

Impact of Cloud Computing in Finance Graphs

The financial sector, which serves as the backbone of the global economy, has undergone significant transformation with the adoption of cloud computing. This paper presents a detailed analysis of the connection between cloud computing and finance, drawing insights from an in-depth review of scholarly articles in the Scopus database. As financial institutions navigate evolving customer demands, regulatory challenges, and the need for innovation, cloud computing has emerged as a dominant force. It has ushered in a new era where competition is reshaped and operational efficiency is prioritized. The introduction highlights the critical role cloud computing plays in enhancing the financial landscape. Traditionally, financial institutions have been complex and resource-heavy systems, but they now face the pressing need for digital transformation. Cloud technology's scalability, flexibility, and cost-efficiency offer a guiding light for organisations aiming to reimagine their operations in this digital era. This paper examines not just the evolution but the profound shift in which cloud technology becomes a key driver in redesigning the way financial services are developed, delivered, and experienced. Keywords—cloud computing, AWS, machine learning, finance.

Automated Bell and LED Display System

Traditional school bell systems and manual scheduling methods often lead to inefficiencies, inaccuracies, and confusion, resulting in operational delays and unclear communication regarding class timings. To address these issues, this project introduces an automated school bell system with a digital display that provides real-time updates on current periods, upcoming classes, and accurate time. Our system utilizes an Arduino microcontroller to automate the ringing of classroom bells based on a pre-set schedule. The bell timing can be easily programmed and adjusted through a user interface, ensuring precise synchronization with the academic timetable. Additionally, an integrated LED display provides real-time visual cues, indicating the current period and upcoming period. This feature helps to minimize confusion and ensure that both students and faculty are consistently informed of schedule changes. This will enable easy management and scheduling updates, reducing the likelihood of errors and improving overall operational efficiency. By streamlining scheduling and enhancing communication clarity, this solution aims to create a more organized and efficient school environment.

Risk Identification on Screw Press Machines Using the Failure Mode and Effect Analysis (FMEA) Method on CV.” Rezeki Bersama

CV. Rezeki Bersama is a producer of crude coconut oil (CCO) based in Alus-alus Village, Teupah Selatan District, Simeulue Regency, Aceh. CV. Rezeki Bersama employs 15 people at various production stations, including press machines. In practice, this research uses the Failure Mode and Effect Analysis (FMEA) method to determine the damage that occurred at the press station of the joint rezeki factory. The FMEA approach is used to assess the likelihood of failure in a manufacturing process, determining its severity, frequency of occurrence, and detectability. This study focuses on collecting machine damage data from January 3 to February 3 2024 and evaluating it to determine the Risk Priority Number (RPN) value which will be used to indicate the priority of repairs needed. This research aims to provide recommendations for repair and prevention of damage in the future, as well as increasing the reliability and efficiency of the company's operations.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Enhancing Collaborative Leadership in Healthcare: Addressing Ego Conflicts for Organizational Harmony and Improved Patient Care

In today’s complex healthcare environment, fostering collaboration between medical professionals and administrative teams is essential for delivering high- quality patient care. However, non-clinical conflicts, often driven by differences in authority, communication gaps, and ego clashes, can disrupt organizational harmony. These conflicts between doctors, nursing heads, and corporate teams can negatively affect operational efficiency, care outcomes, and staff morale. This article explores the root causes of these conflicts, highlights successful global strategies for resolving them, and provides a comprehensive framework for promoting a culture of mutual respect and teamwork within healthcare settings. By implementing these approaches, healthcare organizations can create a cooperative environment that enhances both staff satisfaction and patient outcomes. Moreover, the article examines how these strategies can be applied globally to address similar challenges across diverse healthcare settings.

Developing innovative financial products for sustainable economic growth

In today’s competitive landscape, technology firms increasingly turn to advanced data analytics as a strategic tool for boosting revenue growth and operational efficiency. This review explores how leveraging sophisticated analytical techniques can enable these organizations to extract actionable insights from vast datasets, thereby enhancing decision-making processes and driving business performance. Advanced data analytics encompasses a range of methodologies, including predictive analytics, machine learning, and big data analysis, which collectively empower firms to identify trends, optimize resources, and tailor products to meet evolving customer demands. One of the primary benefits of utilizing advanced data analytics is the ability to enhance revenue growth through improved customer targeting and segmentation. By analyzing customer behaviors and preferences, technology firms can develop personalized marketing strategies that resonate with their target audiences, resulting in increased conversion rates and customer loyalty. Moreover, data analytics facilitates the identification of new market opportunities, enabling firms to innovate and expand their product offerings effectively. Operational efficiency is another critical area where advanced data analytics proves invaluable. By monitoring key performance indicators (KPIs) and operational metrics, technology firms can streamline their processes, reduce costs, and minimize waste. Predictive analytics, in particular, allows organizations to anticipate potential disruptions in their operations, enabling proactive measures to mitigate risks and maintain smooth workflows. This capability not only enhances productivity but also fosters a culture of continuous improvement. Additionally, advanced data analytics supports informed strategic planning by providing insights into market dynamics and competitive positioning. Firms can leverage these insights to make data-driven decisions, allocate resources effectively, and align their business strategies with market trends. In conclusion, advanced data analytics serves as a catalyst for revenue growth and operational efficiency in technology firms. By embracing these analytical techniques, organizations can harness the power of data to drive innovation, improve customer engagement, and achieve sustainable competitive advantage. As technology continues to evolve, the strategic implementation of advanced data analytics will be essential for firms seeking to thrive in an increasingly data-driven world. Keywords: Advanced Data Analytics, Revenue Growth, Operational Efficiency, Technology Firms, Predictive Analytics, Customer Targeting, Strategic Planning.

Optimizing shutdown and startup procedures in oil facilities: A strategic review of industry best practices

Shutdown and startup procedures in oil facilities are critical processes that significantly impact operational efficiency, safety, and environmental compliance. This strategic review examines industry best practices for optimizing these procedures to minimize downtime and enhance productivity. Effective shutdown and startup operations are essential not only for maintaining equipment integrity but also for ensuring the safety of personnel and compliance with regulatory standards. The complexities of oil facility operations necessitate a thorough understanding of the interdependencies between various systems and processes during these phases. This review identifies key factors that contribute to successful shutdown and startup procedures. First, comprehensive planning and preparation are vital. Establishing a detailed shutdown/startup plan that outlines specific tasks, timelines, and responsibilities helps mitigate risks associated with unplanned events. This includes conducting risk assessments and ensuring that all personnel are adequately trained to execute their roles effectively. Second, the implementation of advanced monitoring and control technologies is crucial. Utilizing automation and real-time data analytics allows for precise control over processes during shutdowns and startups, enabling quicker responses to anomalies. This technological integration can significantly reduce the time required for these operations while enhancing safety measures. Third, collaboration and communication among teams are essential components of successful operations. Encouraging a culture of open communication fosters teamwork and ensures that all stakeholders are aligned throughout the shutdown and startup phases. Regular debriefings and feedback sessions post-operations also play a vital role in continuous improvement. In conclusion, optimizing shutdown and startup procedures in oil facilities is paramount for enhancing operational efficiency and safety. By adopting industry best practices, including thorough planning, leveraging technology, and promoting effective communication, organizations can achieve significant improvements in their operational performance. This review serves as a resource for industry stakeholders aiming to refine their shutdown and startup strategies to achieve operational excellence. Keywords: Shutdown Procedures, Startup Procedures, Oil Facilities, Operational Efficiency, Safety, Environmental Compliance, Risk Assessment, Automation, Real-Time Data Analytics, Industry Best Practices.

The Intelligent Data Ecosystem: Uniting AI and Data Integration to Revolutionize Data Science

The Rise of Big Data: Today, the scale and domain of massive data management has prolonged to an unmatched state. In this paper, we will examine the process of data science developed over the recent past and argue that artificial intelligence (AI) and data integration converge to create intelligent data ecosystem reaching new heights. This paper combines AI's strength to automate analytics and data integration, which is able to harmonize different data sources and shows how businesses can benefit from a unified approach for innovation, operational efficiency, decision-making. We also cover some of the key use cases in the industry, and ways these technologies are gaining momentum and reshaping the future of data science.

Exploring advanced techniques in process automation and control: A generic framework for oil and gas industry applications

This paper explores advanced techniques in process automation and control, offering a generic framework tailored for applications within the oil and gas industry. The framework addresses the growing need for operational efficiency, safety, and sustainability in an industry characterized by complex, high-risk processes. The adoption of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Industrial Internet of Things (IIoT) has revolutionized process control systems, enabling real-time monitoring, predictive maintenance, and optimization of production assets. Advanced Process Control (APC) techniques, including model predictive control (MPC), multivariable control systems, and data-driven analytics, play a pivotal role in enhancing production, minimizing downtime, and reducing operational costs. This generic framework leverages the integration of AI and big data analytics for optimizing control strategies, ensuring accurate decision-making and improving operational responsiveness to changing conditions. Additionally, the paper examines how automated control systems mitigate risks associated with human error, while enhancing safety protocols by providing early warnings and real-time insights into equipment performance. Automation also supports the industry's shift towards more sustainable practices by improving energy efficiency, minimizing environmental impact, and enabling remote monitoring in hazardous locations. Through case studies and examples, the paper illustrates the successful implementation of these techniques in upstream, midstream, and downstream operations, highlighting the significant role of digital transformation in driving innovation. The proposed framework can be adapted to diverse oil and gas settings, serving as a foundation for developing scalable, flexible, and sustainable automation solutions. By outlining the key components and benefits of advanced process automation and control, this study contributes to the ongoing discourse on how technological advancements can reshape operational strategies in the oil and gas sector. Keywords: Process Automation, Control Systems, Oil And Gas Industry, Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Advanced Process Control (APC), Model Predictive Control (MPC), Predictive Maintenance, Digital Transformation, Sustainability.

Liquidity Management Practices and Financial Performance of Microfinance Banks in Nairobi City County, Kenya

Microfinance banks are financial institutions that provide loans, insurance, saving platforms and other financial products to various individuals. Thus, these institutions require sufficient liquid assets to enable them to meet their routine financial obligations. However, in Kenya, microfinance banks have been recording a declining trend regarding their financial performance over time, leading to grave concerns. Thus, this study aimed to assess cash management practices and their effect on Nairobi City County’s MFB's financial performance. The analysis and elucidation of the literature was facilitated by the examination of the Keynesian Liquidity theory. A descriptive research design with a regression analysis model was embraced in determining the predictor variable’s impact on the explained variable. Data review guide was then utilized to gather secondary data, which was obtained from the CBK and covered the years 2017 to 2022. According to the study, operational cash flow management and cash management significantly and favorably impacted the financial performance of Kenyan microfinance institutions. Nonetheless, the financial performance of Kenya's microfinance institutions was significantly impacted negatively by operational efficiency management. As per the study's findings, microfinance banks in Kenya carry out cash budget preparation, bank reconciliation, cash inflow and outflow recording, cash expense payment, and cash surplus investment. As a result, they manage their funds carefully. Furthermore, the stability and functioning of the financial system depend heavily on efficient cash flow management. It entails keeping an eye on money coming in from investments, sales, and outside funding sources as well as tracking money going out to pay for bills, obligations, and loan repayments. Microfinance banks are able to balance inflows and outflows, lower the risk of liquidity crises, and take advantage of growth possibilities by managing cash flow well. This allows microfinance institutions to make well-informed choices regarding debt management, expansion strategies, and investments—all of which are essential for overall financial success. To maximize the beneficial effects on financial performance, the study recommends adhering to the statutory minimum liquidity ratio. Since excessive liquidity and illiquidity reduce the institutions' profitability, microfinance banks should implement effective cash management procedures.

The Diverse Applications of Aerospace Drones: From Defense to Humanitarian and Commercial Uses

This research investigates the extensive applications of aerospace drones across defense, humanitarian, and commercial sectors, showcasing their transformative impact on modern industry. Aerospace drones have become pivotal tools in diverse settings—from defense and tactical operations to humanitarian aid and infrastructure management—offering operational efficiencies, cost savings, enhanced safety, and innovative solutions to complex challenges. This study explores defense applications such as military reconnaissance, where Vertical Take-Off and Landing (VTOL) drones enable effective surveillance in challenging terrains. In humanitarian settings, drones are utilized for disaster response in earthquake and flood zones, providing critical functions like search-and-rescue, real-time damage assessment, and medical supply delivery. Commercially, drones support long-range infrastructure inspections, smart agriculture, and environmental monitoring, driven by advancements in imaging and remote sensing. The project emphasizes the technical challenges and ethical considerations associated with drone deployment, particularly the need for robust AI and autonomous navigation systems in dynamic and unpredictable environments. Collaborations with international aerospace firms and humanitarian organizations have further refined drone integration strategies in both civilian and military contexts. The research concludes with a discussion on regulatory frameworks and environmental impacts, aiming to provide a comprehensive view of thefuture of aerospace drones and their role in addressing global challenges.

Comprehensive cost analysis of voice call recording storage with and without NAS and GSM gateway integration via unified communication server

This study is an extension of existing research, which previously demonstrated the effective integration of Network Attached Storage (NAS) with a GSM Gateway into Unified Communication (UC) Platform, thereby realizing remarkable enhancements in system scalability, data security, privacy, network and overall server performance. Although the technical and operational benefits of this integration were well understood and documented, however there was an important gap that is cost analysis. This paper addresses the gap by providing a comprehensive cost analysis of voice call archiving with and without NAS and GSM Gateway integration using UC. The analysis considers essential financial factors, including initial investment, ongoing operational costs, and long-term savings due to lower maintenance and scalability. While the findings reveal that the upfront costs of NAS and GSM Gateway integration are higher, these are offset by long-term operational efficiencies and system resilience, leading to substantial financial gains. The evaluation results offer organizations a strategic model to assess the cost-effectiveness of using this NAS-GSM Gateway integrated approach for voice data archiving in UC environments. This custom-costing solution provides a much-needed holistic perspective on the combined technical and financial benefits.

Strategy Implementation Practices and Performance of Commercial Banks in Nakuru County

Organizational performance entails recurring duties like establishing organizational goals, monitoring progress towards those objectives, and making modifications to enhance their accomplishment in a more efficient manner. Strategy implementation represents a significant phase within an organization; however, existing literature consistently underscores a notable failure rate in the implementation process across numerous organizations. The prevalence of poor performance in banks is attributed to issues such as inadequate coordination, suboptimal structural frameworks, and insufficient personnel proficiency in executing rebranding strategies. As a result, the goal of this study was to look into how Nakuru's commercial banks implemented their strategies and how well they did so. General Systems Theory, Contingency Theory, and Resource-Based Theory served as the study's guiding theories. The study targeted 108 workers from 24 commercial banks in Nakuru County using a descriptive survey research approach. A sample of 54 respondents was chosen using a stratified random selection procedure to assure representation. Structured questionnaires were used to obtain data, and they were chosen for their propensity to do so with little bias and mistake. Pilot research was done in Narok County commercial banks prior to the main investigation. The data was analysed using both descriptive and inferential statistics. Percentages, frequencies, measurements of central tendency (mean), and measures of dispersion (standard deviation) were examples of descriptive statistics. Correlations were used for inferential analysis of qualitative data. To analyse quantitative data, Version 25 of the Statistical Package for the Social Sciences (SPSS) was utilized. The study found that resources, organizational change, structure, and leadership significantly influence the performance of commercial banks in Nakuru CBD. Effective resource allocation, strategic change management, well-defined organizational structures, and strong leadership positively impact operational efficiency and overall performance. The study recommends investing in employee training, optimizing IT services, and aligning organizational structures with strategic goals. Further research should examine how organizational capabilities and market dynamics shape performance outcomes.

Utilizing Technology and Artificial Intelligence in Educational Administration to Enhance School Performance at Junior High School

This study aims to explore the use of technology and artificial intelligence (AI) in education administration to improve school performance. In the growing digital era, demands for efficiency and effectiveness in education administration are increasing, especially with the development of school information management systems (SIMS) and AI-based technologies. This research examines how these technologies can automate routine tasks such as class scheduling, student data management, as well as improve data-driven decision-making that is more accurate and efficient. The research method used in this study is descriptive qualitative, with the process of collecting data through in-depth interviews, observation, and document analysis directly from several schools that have adopted this technology. In analyzing the data, I used Miles and Hubberman's data triangulation technique in the form of data reduction, data analysis, and conclusion drawing. The results show that the implementation of technology and AI has improved operational efficiency, accelerated administrative processes, and facilitated the monitoring of student and staff performance. However, the study also found challenges in technology adoption, including limited infrastructure, resistance to change, and concerns regarding data privacy. To overcome these obstacles, a holistic strategy is needed, including the development of supportive policies, digital skills training for staff, and improvement of technology infrastructure in schools. This research concludes that although the application of technology and AI in education administration faces various barriers, the potential benefits are significant, especially in terms of improving the efficiency, transparency and overall quality of school performance.