In today’s retail environment, organizations generate huge amounts of transactional data. However, many companies still use traditional reporting systems that are primarily concerned with historical analysis of sales. Such approaches provide limited insights into future customer behaviour and long term profitability. The paper introduces a Retail Analytics and Customer Intelligence Platform that uses a combination of machine learning techniques to integrate customer segmentation, Customer Lifetime Value (CLV) prediction and sales forecasting. The Online Retail dataset is pre-processed and transformed to generate RFM (Recency, Frequency, Monetary) features to understand customer purchasing behaviour. Machine learning algorithms such as Linear Regression, Random Forest and XG Boost are used to estimate customer lifetime value. XG Boost was the best model with an R2 score of 0.986, indicating good predictive power among these models. Customers are segmented into Low, Medium and High value segments to facilitate targeted marketing campaigns. There’s also a sales forecasting module that predicts revenue trends to help with business planning. Developed an interactive dashboard with Streamlit for data visualization and decision making. The proposed system helps retailers to increase customer retention, optimize marketing efforts, and improve accuracy of revenue prediction.

ABSTRACT Business Intelligence has become a strategic enabler of data-driven decision-making. However, empirical evidence on its performance implications in state-owned enterprises (SOEs) within emerging economies remains limited. This study examines the effect of Business Intelligence on firm performance and investigates the mediating roles of organizational learning and performance measurement capability, as well as the moderating role of board size. Grounded in the resource-based view, the study employs a panel data regression from 20 Jordann publicly listed SOEs. The results reveal that Business Intelligence has a positive and significant impact on firm performance. Organizational learning and performance measurement capability partially mediate this relationship, indicating that Business Intelligence contributes to performance when supported by effective learning processes and robust evaluation systems. Conversely, board size negatively moderates the Business Intelligence–performance relationship, suggesting that larger boards may weaken the strategic utilization of Business Intelligence due to coordination complexity. These findings advance the accounting information systems and management accounting literature by clarifying the mechanisms and boundary conditions through which Business Intelligence enhances organizational performance.

The Accreditation Review Commission on Education for the Physician Assistant requires programs to implement ongoing self-assessment processes using numerous measures to evaluate their effectiveness, entailing data collection, critical analysis, application of results, and the formulation of conclusions. Developing, implementing, and maintaining a robust ongoing self-assessment process is challenging for physician assistant/associate educators due to competing complexities, time limitations, overwhelming amounts of data, and a lack of training and/or adequate resources, which impact program success and faculty and staff stress. Recent technological advances, such as business intelligence (BI) software, may influence faculty and staff experiences in programmatic self-assessment. A survey was used to quantitatively and qualitatively gauge faculty and staff experiences and perceptions of using BI technology in the study program's self-assessment process. Survey items focused on data management, usability, task management, faculty and staff stress, and overall satisfaction. Faculty and staff reported positive perceptions overall. Favorable narratives emerged related to their experiences using BI tools for self-assessment activities within the study program. Respondents recommend these tools to other programs and offer valuable insight to ensure successful implementation. The findings of this single-program study suggest that the use of BI technology may contribute favorably to faculty and staff experiences in programmatic self-assessment. However, further studies are needed to correlate a relationship and explore generalizability.

Prevalence of pre-frailty and frailty and their association with postoperative hospital days in older adults referred for elective orthopaedic surgery.

The Sales Data Analysis Dashboard is designed to provide organizations with a comprehensive view of sales performance by transforming raw transactional data into meaningful business insights. In today’s competitive environment, companies depend on data-driven decision-making to improve operational efficiency and increase revenue growth. This project focuses on collecting, processing, and visualizing sales data using analytical techniques and interactive dashboards. The system integrates multiple datasets including sales transactions, product details, customer information, and regional sales records. Key performance indicators such as total revenue, monthly sales growth, top-performing products, and customer purchasing trends are analyzed and displayed through visual reports. The dashboard enables users to filter data based on product categories, regions, and time periods for better analysis. The developed system helps organizations identify market trends, monitor sales performance, optimize inventory planning, and support strategic business decisions. This project demonstrates the practical application of business intelligence and modern data analytics technologies for organizational growth. Keywords—Sales Data Analysis, Business Intelligence, Data Visualization, Interactive Dashboard, Data Analytics, Sales Performance, Revenue Analysis, KPI Monitoring, Customer Insights, Decision Support System.

Supply chain management constitutes one of the most critical and strategically consequential functions of modern retail business operations, encompassing the complete lifecycle of a product from procurement and production through warehousing, distribution, and final delivery to the end customer. In an era of growing market complexity, rapidly shifting consumer expectations, and increasing competitive pressure on profit margins, the ability to extract timely and accurate intelligence from supply chain transactional data has emerged as a defining competitive advantage for retail organizations. This paper presents a comprehensive supply chain analytics study applied to the widely used Sample Superstore dataset, a multi-dimensional transactional dataset representing four years of retail operations across the United States. The study implements an end-to-end data analytics pipeline encompassing raw data ingestion, systematic preprocessing and feature engineering using Python and its scientific ecosystem — including Pandas, NumPy, Matplotlib, and Seaborn — followed by in-depth exploratory data analysis (EDA) and the development of an interactive, multi-page business intelligence dashboard built using Microsoft Power BI. The preprocessing phase engineers five derived analytical features: Delivery Duration, Profit Margin Percentage, Discount Band, Order Year, and Order Month, significantly enriching the dataset’s analytical depth. The EDA reveals several critical findings: a strong and consistent negative correlation (r ≈ −0.22) between discount rate and profit margin that intensifies at discount levels above 40%; stark category-level profitability divergence with Technology generating approximately 17.4% profit margin versus Furniture at only 2.5%; significant regional profitability disparities with the West and East regions outperforming the Central and South; an on-time delivery rate of 81.75% indicating logistics execution gaps; and a 99.37% customer repeat rate indicating strong customer loyalty. The Power BI dashboard translates these findings into sixteen Key Performance Indicators (KPIs) across four pages covering sales performance, profitability analysis, customer behaviour, and logistics operations. Six actionable business recommendations are derived, including the implementation of a 20% discount cap policy, restructuring the Furniture category strategy, adopting a regionally differentiated discount framework, and targeting a 95% on-time delivery rate. The project demonstrates the transformative potential of applied supply chain analytics and establishes a reproducible, scalable framework that organizations can adapt to their own operational datasets.

ABSTRACTBackground Post–COVID-19 syndrome (PCS) is an emerging public health issue characterized by persistent symptoms, including anxiety. Anxiety is the most frequent mental health persistent symptom among patients with PCS, with global prevalence ranging from 6.5% to 63%. It negatively impacts people’s functioning, productivity, and daily routine beyond recovery. This study aimed to examine the prevalence, determinants, and health impact of anxiety symptoms among adults with post–COVID-19 in Qatar during 2022. Methods A cross-sectional study was conducted in Qatar between January and July 2022, utilizing data from the Primary Health Care Corporation’s Business Intelligence Unit. Out of 368 eligible individuals, 159 adults with confirmed PCS lasting more than 12 weeks were randomly selected and interviewed by telephone after providing consent. Anxiety levels were assessed using the Generalized Anxiety Disorder-7 (GAD-7) scale.Results Out of 159, the prevalence of anxiety symptoms was 50.3% (n = 80) based on GAD-7. Most of the participants were female, Qatari, married, and unemployed. Hospitalization and functional impairment were also higher in the anxiety group compared to those without anxiety. Depression, disability, and cognitive dysfunction were strongly associated with the post–COVID-19 anxiety group compared to the non-anxiety group, with P < 0.05.Conclusion Post–COVID-19 anxiety is prevalent among PCS patients in Qatar and linked to worse mental and physical health outcomes. Routine screening and integrated care approaches are essential to support recovery.

Purpose This study aims to develop a new theoretical framework to explain how geopolitical disintegration in the Middle East and Red Sea region generates transregional shocks that disrupt multinational enterprises (MNEs) and how artificial intelligence (AI) capabilities enhance firm-level strategic resilience. Design/methodology/approach This study introduces the transregional shock transmission (TST) theory and uses a multi-method quantitative design. It integrates panel data from 75 MNEs in the logistics, energy and shipping sectors with event-based conflict data and machine-coded news sentiment analysis for 2013–2024. A moderated mediation model is tested using structural equation modeling to examine the relationships between geopolitical instability, risk perception, strategic adaptation and AI capabilities. Findings Firms with advanced AI-driven risk intelligence systems demonstrate significantly higher resilience to geopolitical shocks. These firms adapt supply chains more rapidly, mitigate financial losses and restore operations more efficiently. AI capability moderates the adverse effects of geopolitical disintegration by enhancing real-time risk perception and adaptive decision-making. Research limitations/implications This study is limited by its cross-sectional design and reliance on self-reported data, which may constrain causal inference and introduce bias. Although the sample is diverse, generalizability remains limited. Future research should adopt longitudinal and multi-source approaches and further validate emerging constructs to deepen the understanding of leadership and ethical accountability in AI-mediated contexts. Practical implications Theoretically, this study extends crisis internationalization and geopolitical risk research by positioning AI as a dynamic capability shaping firm responses to regional instability. Practically, the findings suggest that MNEs should: invest in AI-enabled early warning and predictive analytics systems; integrate geopolitical risk metrics into strategic planning; decentralize critical supply chain nodes; and shift from reactive to proactive resilience models in fragile environments. Originality/value This paper advances a novel conceptual model, TST theory, that explains how localized political instability translates into global business disruption. By highlighting the strategic role of AI in navigating transregional shocks, it offers new insights into adaptive international business strategy amid escalating geopolitical fragmentation.

Purpose: In this review paper, a framework is provided for the alignment of the Vision 2030 plan in relation to the digital transformation of inventory planning and control in Saudi industry. This paper focuses on the use of ERP software, Oracle Fusion Cloud SCM and business intelligence platform, Power BI, in relation to the conversion of inventory control as a reactive accounting task into a proactive industrial strategy through ERP transactions, replenishment algorithms, master data management, and business intelligence dashboards. Design/methodology/approach: A narrative approach is used in conducting the literature review, where contemporary research in relation to ERP, business intelligence, supply chain analytics, inventory control, and digital transformation are reviewed with a focus on literature published during the 2020-2025 period. Findings: Through the use of the Oracle Fusion ERP system and Power BI business intelligence platform, it will be possible to develop a framework for converting inventory control to a proactive process by enhancing data discipline, standardizing processes, and improving control on inventory transactions. Practical Implications: Saudi industry can take advantage of the ERP-BI combination in order to manage stock outs, slow-moving inventories, high working capital, and other inefficiencies. Originality/value: This paper adds value in terms of an applied framework connecting Oracle Fusion and Power BI to inventory control and Vision 2030 objectives.

Digital green synergies: linking green cyber-physical systems, green supply chains integration, and green business intelligence to boost SMEs sustainability

Financial forecasting and analytics have emerged as indispensable tools in modern business environments characterized by uncertainty, volatility, and rapid technological advancement. This study explores the role of financial forecasting techniques and analytical tools in enhancing organizational decision-making and financial performance. The research integrates quantitative forecasting models such as time-series analysis, regression models, and variance analysis with advanced analytics to provide actionable insights. A structured empirical framework is developed to evaluate the relationship between forecasting accuracy and financial outcomes. Statistical techniques including hypothesis testing, regression, and ANOVA are employed to validate the proposed model. The findings suggest that organizations utilizing advanced financial analytics demonstrate significantly improved accuracy in forecasting and enhanced financial stability. The study contributes to both academic literature and practical application by offering a comprehensive framework for integrating analytics into financial planning processes. Keywords:Financial Forecasting, Financial Analytics, Regression Analysis, ANOVA, Predictive Modeling, Decision-Making, Time Series Analysis, Business Intelligence

Traditional business intelligence tools are used for the visualization of the data by the organizations, but the major problem is analysts have to make efforts to find the insights from this and also find the predictions on the basis of any one factor or metric; this makes the whole process time-consuming and difficult. This project proposes a framework focusing on decisionbased analytics using a transactional dataset and creating a platform using Python and Streamlit that will provide a platform with options for data visualization and provide automated insights and predictions based on multiple variables with the help of a real-world dataset. The prediction model created using the Random Forest algorithm achieved an R² score of 0.99 and the Mean Absolute Error (MAE) of 37.93, thus the prediction deviation is low. This study also highlights the importance of Artificial Intelligence by implementing it the system using Machine Learning, showing how AI can help in advancing the dashboards from the traditional dashboards. The dashboard created will help to demonstrate how this proposed idea will enhance the efficiency and reduce the effort required for manually analyzing and interpreting and be easily usable by anyone, whether from a technical or non-technical field. The proposed system dashboard is reliable, time saving and requires less efforts, less complex and efficient.

In the rapidly evolving e‐commerce landscape, retaining existing customers has become more cost‐effective and strategically important than acquiring new ones. This study proposes a data‐driven framework that integrates business intelligence (BI) tools, machine learning, and customer relationship management (CRM) decision support to improve predictive customer retention. The framework was developed using the publicly available Brazilian E‐Commerce Public Dataset (Olist), which contains more than 100,000 orders and includes transactional, payment, delivery, product, and customer‐review information. After SQL‐based integration and feature engineering, customer segmentation was performed using K‐means clustering on recency, frequency, monetary (RFM) variables, identifying three behavioral groups: loyal, at‐risk, and occasional customers. For churn prediction, Random Forest and XGBoost classifiers were trained on customer‐level behavioral, satisfaction, and service‐related features. XGBoost achieved the best overall performance, with accuracy = 0.81, precision = 0.79, recall = 0.83, F1 − score = 0.81, and AUC = 0.85, outperforming Random Forest (accuracy = 0.76, precision = 0.74, recall = 0.71, F1 − score = 0.72, and AUC = 0.76). The resulting segmentation and churn scores were then exposed through Power BI dashboards and mapped into a proof‐of‐concept CRM decision framework for retention planning. Unlike studies that treat BI, machine learning, or CRM in isolation, this research presents an end‐to‐end analytical pipeline that links data preparation, predictive modeling, dashboard‐based decision support, and scenario‐level CRM action design. The framework provides a reproducible basis for e‐commerce retention analytics and a practical foundation for future live deployment and A/B‐tested CRM validation.

Artificial intelligence (AI) is advancing rapidly, transforming biomedical research and health care through software applications ranging from diagnostics to drug discovery. Biobanking resides at a unique intersection of this technological transformation, serving both as a foundation for training new AI models and as a beneficiary of AI-driven optimization. High-quality, well-annotated biospecimens enable robust machine learning, while AI methods in turn support automation, quality control (QC), predictive analytics, and workflow efficiency for biobanking operations. Emerging applications include non-generative AI methods, which have been used to predict sample degradation, stratify populations, and assess tissue integrity. Generative AI and large language models expand these capabilities by enabling synthetic data generation, metadata extraction, natural language-based interaction with biobank systems for both operational needs and training. Furthermore, newer multiagent approaches now demonstrate how distributed AI frameworks can orchestrate end-to-end processes. Case examples highlight early successes in automated image-based QC, natural language processing for metadata extraction, and privacy-preserving synthetic datasets to enable secure data sharing. Looking ahead, AI promises to reshape biobanking as both an operational and scientific engine, with opportunities in business intelligence, workflow optimization, and personalized education. Challenges around data quality, interoperability, governance, and ethics remain, but the convergence of AI and biobanking points to a future where repositories evolve into intelligent, adaptive infrastructures that actively drive discovery, accelerate translational research, and advance precision medicine.

ABSTRACT This study explores the perceptions of accounting professionals in the United Arab Emirates regarding the importance of integrating data analytics into accounting education to enhance graduate employability. Using Q methodology, 97 professionals evaluated 35 statements across three themes: data analytics knowledge and awareness, curriculum content, and data-analytics skills and competencies. The analysis, conducted with KenQ Analysis Desktop Edition software, revealed a strong awareness among professionals of the critical role data analytics plays in modern accounting practice. The findings indicate broad support for embedding specific topics in accounting curricula, particularly data structure/data warehouses, data governance, business intelligence tools, data mining and predictive modeling, regression analysis, and Excel-based techniques such as formulas, filtering, sorting, and lookups. Respondents also emphasized practical competencies in capturing, disseminating, aggregating and integrating data, and applying descriptive, predictive, and prescriptive analytics. The study offers valuable guidance for accounting educators and curriculum designers by emphasizing the need to revise both the content and the delivery of accounting education to meet evolving industry demands. It highlights the necessity of equipping graduates with data-driven competencies that align with the accounting profession’s digital transformation. The study provides evidence-based insights into how accounting professionals prioritize data-analytics knowledge and awareness, curriculum content, and skills.

The rapid proliferation of learning analytics, business intelligence (BI), artificial intelligence (AI), and generative AI (GenAI) has significantly expanded universities’ ability to collect, integrate, analyze, and operationalize institutional data. However, despite advances in predictive analytics, dashboards, and AI-driven systems, the translation of analytical outputs into consistent and accountable institutional decision-making remains uneven. This systematic literature review synthesizes contemporary research on analytics-enabled decision-making in higher education with the aim of moving beyond dashboard-centric perspectives toward a socio-technical and computing-oriented understanding of how data are transformed into institutional actions and outcomes. Guided by the PRISMA framework, the review synthesizes evidence across four interconnected dimensions: data ecosystems and learning analytics foundations; analytics capability, BI adoption, and digital readiness; AI and advanced analytics for decision support; and human-in-the-loop (HITL) decision routines and institutional outcomes. The findings show that predictive performance and analytical sophistication alone do not guarantee decision value. Instead, effective analytics-enabled decision-making depends on interoperable data ecosystems, organizational analytics capability, governance mechanisms, explainability, and sustained human oversight. Based on these findings, this review contributes a computing-oriented decision-intelligence framework that conceptualizes analytics-enabled decision-making as an end-to-end socio-technical pipeline linking heterogeneous data acquisition, integration, feature construction, analytical modeling, explainability, human validation, governance, and feedback-based refinement. By integrating learning analytics, BI, AI, GenAI, and HITL mechanisms within a unified framework, the review clarifies how universities can move beyond dashboard-based reporting toward accountable, adaptive, and institutionally actionable decision-support infrastructures.

This paper proposes a novel technical architecture for university business software systems in the era of large language models focused on digital intelligence empowerment. The architecture addresses limitations in traditional university data governance frame-works by adopting a ”large platform + small applications” approach, implementing a design solution of “one support platform + three atomic component groups + one service portal.” The intelligent connection infrastructure platform serves as the architectural foundation, supporting the development of atomic data, atomic business, and atomic capability components. These components collaboratively deliver personalized services through an intelligent service portal. Based on this architecture, a complete software ecosystem featuring agile data governance and intelligent technology empowerment can be constructed. The expected application scenarios and analysis demonstrates that this architecture overcomes traditional data governance constraints while fully leveraging intelligent technology capabilities. It supports the sustainable evolution of intelligent business application ecosystems and delivers substantial technical infrastructure for the digital transformation and intelligence enhancement initiatives within higher education.

Efficient operation on the factory floor has been hindered by data latency and communication delays, leading to production downtime and associated costs. This research investigates the use of an IoT-enabled Andon system integrated with a vendor-neutral Business Intelligence (BI) visualisation layer, within a genset manufacturing business. Through a sociotechnical intervention, this work examines how integration of real-time monitoring, data visualisation, and visual management influences operational performance. The single-case study method confirms the effect of implementing an IoT-enabled Andon: a 55.55% reduction in unscheduled downtime and a 4% increase in Overall Equipment Effectiveness. The effectiveness of the Andon and Power BI system was due to ‘technology’ and, importantly, its empowerment of operators to make responsive and aligned production decisions. The research provides empirical validation of an integrated Industry 4.0 intervention through a sociotechnical lens. It highlights the synergy between digital tools and human decision-making, offering strategic directions for digital transformation.

STMamba-GC: Spatiotemporal Mamba with graph contrastive learning for next POI recommendation.

Predicting product demand is a critical challenge in retail and inventory management, as inaccurate forecasting often leads to overstocking, stockouts, and financial losses. This study presents a machine learning-based Inventory Demand Prediction System, titled INVEX, designed to forecast future product demand using historical sales data. The system leverages key features such as product category, selling price, stock levels, and past sales patterns to generate accurate demand predictions for retail businesses. Two regression algorithms — Linear Regression and Random Forest Regressor — are implemented and compared to evaluate their effectiveness in predicting product demand. The dataset consists of structured sales records generated based on realistic retail scenarios, including daily transactions and product-level demand variations. The models are evaluated using standard performance metrics such as R² Score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall prediction accuracy.Experimental results indicate that the Random Forest Regressor outperforms Linear Regression by effectively capturing non-linear demand patterns and seasonal variations in sales data. The system achieves a prediction accuracy of over 90%, demonstrating its capability to provide reliable and data-driven insights for inventory planning. The integration of a user-friendly interface enables store owners to manage products, generate bills, track sales history, and visualize predicted demand trends dynamically. The proposed INVEX system offers a scalable and intelligent solution for modern retail environments by automating inventory decisions and reducing human dependency. It assists business owners in maintaining optimal stock levels, minimizing wastage, and improving profitability through accurate demand forecasting. This system can be extended further with real-time data integration and advanced machine learning models for enhanced predictive performance. Keywords Machine Learning, Inventory Management, Demand Prediction, Linear Regression, Random Forest, Sales Forecasting, Retail Analytics, Data Analysis, Stock Optimization, Predictive Modeling, Business Intelligence, Inventory Optimization