A Managerial Perspective on Business Intelligence Tools as Decision Support Systems in Mind Box LLC

Oil and gas companies are increasingly using data analytics to improve drilling performance. This paper provides an example of using a business intelligence (BI) tool to analyze drilling data in the Permian Basin. The BI tool helped to improve operation decisions through the use of a visual report. A database, consisting of massive amounts of historical drilling data, is analyzed using the BI tool to better understand drilling performance and predict average operations performance in the area. A historical drilling database is created based on the bottomhole assembly (BHA) run data and analyzed by the BI tool to review the well performance, in addition to identifying any hazards and summarizing the optimum drilling system during the planning phase. With the help of the BI tool, the drilling database can be displayed in an interactive way to further understand the drilling performance in the area; e.g., the top performing drill bit, drilling system, downhole mud motor configuration, and estimated drilling time for the section of interest. As a result, engineers will find it easier to identify the potentially top performing wells along with drilling hazards in offset wells. The engineers can evaluate the well details and identify the best drilling practices to optimize drilling performance and eliminate downhole incidents. Using the BI tool helps reduce the data mining time and offers a fast, improved method for gaining technical insights into the drilling operation. These descriptive analytics help to simplify the complex data sets, which are valuable for uncovering patterns that offer data set understanding. With the visualization results, experts can focus on data diagnostics analytics to make suggestions for drilling operation improvements and corrections. Furthermore, these analytical data can be used as inputs for more advanced predictive (forecast drilling performance) or prescriptive analytics (drilling optimization) that deliver real-time insights for making improved business decisions.

This paper provides a comprehensive overview of the current trends in Business Intelligence (BI) tools within the finance sector, with a specific focus on the divergent landscapes of the USA and Africa. As the financial industry evolves, the integration of advanced BI tools has become imperative for informed decision-making and sustainable growth. This review examines key developments, challenges, and opportunities shaping the implementation of BI tools in both regions. In the USA, financial institutions are witnessing a surge in BI tool adoption, driven by the increasing need for real-time data analytics, regulatory compliance, and risk management. The integration of artificial intelligence and machine learning algorithms in BI tools is enabling predictive analytics, empowering financial professionals to make strategic decisions in a rapidly changing market environment. Additionally, the emergence of cloud-based BI solutions is enhancing scalability and accessibility, facilitating seamless collaboration and data-driven insights. Contrastingly, the African financial landscape is experiencing a gradual but promising shift towards BI tool utilization. The continent, characterized by diverse economies and regulatory frameworks, faces unique challenges such as infrastructure limitations and varying levels of technological readiness. However, the potential for BI tools to revolutionize financial operations, improve transparency, and facilitate financial inclusion is evident. Governments and financial institutions are investing in technology infrastructure and skill development to harness the transformative power of BI tools. This review underscores the importance of understanding regional nuances in the adoption of BI tools, recognizing that effective implementation requires tailored strategies. As both the USA and Africa navigate the evolving financial landscape, the convergence of technological advancements and strategic planning will undoubtedly play a pivotal role in shaping the future of BI tools in the finance sector

This study explores the integration of survey-based student feedback with Business Intelligence (BI) tools to optimize the student experience in higher education. As institutions increasingly aim to improve student satisfaction and engagement, leveraging survey data in combination with BI tools provides a data-driven approach to identifying key areas for enhancement. The research proposes a conceptual framework that facilitates the collection, analysis, and application of student feedback using BI technologies, enabling higher education institutions to make informed, real-time decisions. The study reveals that while students report high satisfaction with academic quality, there are notable gaps in support services, indicating a need for improvement. Furthermore, the use of BI tools uncovered trends and correlations in student feedback that traditional analysis methods overlooked, allowing for more proactive and personalized interventions. The findings suggest that the integration of BI tools not only improves institutional responsiveness but also leads to higher student satisfaction and retention rates. This paper concludes with recommendations for future research, emphasizing the importance of ethical data use and exploring the long-term impact of BI-driven interventions. Educational institutions are encouraged to invest in BI infrastructure and adopt data-driven decision-making to enhance student experiences and outcomes.

Business Intelligence (BI) is one of the fastest growing software sector and software vendors are rapidly developing multiple BI tools to support the growing data analysis needs of organisations. In order to be sustainable in a briskly changing turbulent environment, organisations need to have access to information about their operational performance. BI tools play a vital role in supporting the decision makers at different organisational levels. As these tools are becoming critical in decision making, it has become not only an information technology concern but also a management concern. Without proper governance it would be impractical to achieve the value that BI tools offer. Adopting a BI governance framework in organisations will lead to common principles and clear ownership over information. Additionally, appropriate alignment between corporate governance and BI governance can yield more benefits. This paper provides an insight into the importance and value of BI tools. Key functionalities of BI tools have also been highlighted. Different challenges in gaining true value from BI tools have been examined. Four phases of developing a BI governance framework have been illustrated. The alignment between BI governance and corporate governance has also been explored with a recommended model. Exploratory analysis of two organisations (Premier Healthcare Alliance & BellSouth Telecommunications) to identify how they have utilised BI tools and adopted BI governance has been briefly carried out. The paper posits that if the steps for developing a BI framework are adopted by organisations and the BI framework is aligned with the corporate framework, BI deployment and usage will be successful with reduced risk levels.

The act of applying game mechanics and dynamics (which have been traditionally used in video games) into business applications is being widely trialled in an effort to make conventional business software a bit more participative, fun and engaging. This new trend, named `gamification' has its believers and of course, its critics who still need convincing that the concept is an effective and beneficial business tool worthy of investment. The literature reveals that user engagement of business intelligence (BI) tools is much lower than expected and investors are failing to get a good return on their investment (ROI). So, a software prototype will be designed and developed to add gamification to a BI tool to determine its effect upon the user engagement levels of test participants. The experimental study will be evaluated using the comprehensive User Engagement Scale (UES) to see if there are improvements in areas such as; aesthetics, perceived usability, endurability, novelty, felt involvement and focused attention. The results of this unique study should demonstrate whether or not `gamifying' a BI tool has the potential to increase an individual's motivation to use BI software more often.

This study extends the existing literature of users' acceptance of business intelligence technologies by proposing a comprehensive model integrating personality five factors model (FFM) and technology acceptance model (TAM) to investigate the intention to use business intelligence (BI) tools to support business decision making. We expect that this study will present an example of the integration between FFM and TAM to provide a better understanding about the relationship between personality and intention to use within the context of BI.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

In an ever-evolving world, transport and logistics businesses are facing a variety of challenges. From changing customer expectations to competition from large e-commerce players, the industry is being pushed to its limits. The current study can make revolutionize the industry which has the great potentiality for business with BI. Fortunately, business intelligence can give transport and logistics companies the necessary insights they need to thrive. In this article, we have explored how business intelligence (BI) can help transport and logistics companies stay competitive in today's market. Study objectives include identifying ways to streamline transportation and logistics operations, reduce costs, and enhance overall efficiency. The study evaluates the BI technologies available, as well as how they can improve operations, reduce costs, and increase customer satisfaction. Business intelligence is a tool that has become invaluable in many industries, and transport and logistics are no exception. Business intelligence allows companies to collect data on their operations, analyze it, and gain insights into their operations in order to make better decisions. This study will explore how BI can be used in the industry to optimize operations, reduce costs, and improve customer service. The study findings indicate that the Business intelligence tools give transport and logistics companies the ability to see their operations in a new light, understand their customers better, and make informed decisions that drive to succeed. A data quality issue and high implementation costs are two major factors that pose challenges when using business intelligence (BI) tools, since accurate and reliable data is essential for generating insightful insights and making informed decisions.

Tracking and monitoring construction projects are essential tasks to make decisions on the projects. There are various techniques to collect and store data from the construction site. In the current construction sector, informatics play an important role in collecting and interpreting data from construction processes. In recent studies, various technologies such as RFID, UAV are used for surveying the construction site. However, there is a study about collecting and interpreting the collected data is still needed. In this study, the collected data is categorized by labeling with several dimensions (e.g. location, contractor) using business intelligence and data warehouse tools. Moreover, this technique allows us to construct relationships between activities and generate practical information. The major implication of this study for engineering managers is monitoring the public demands and construction processes within one framework.

Business intelligence techniques and tools are used more and more in all the domains of human activity, being able to bring to light new information from the volumes of data that are gathered from the activities carried on. In the educational domain, as in other fields of activity, business intelligence tools can be employed with success. The current paper aims to analyze the impact of learning activities, based on data available related to a number of courses that were carried on over a period of three years (October 2010 - September 2013). The large majority of the courses was carried on through a European funded project and was aimed to develop the entrepreneurship competences of the participants through relatively short term trainings (24 hours spread over several days - 20 hours theoretical activities and 4 hours practical activities). Related to these courses, the available database consists of information related to the participant's background and the courses followed by them, with the outcomes of the courses (for example businesses started after graduation). Among the analysis that can be performed based on the available database are: participants starting a business by age; participants starting a business by field of study / level of education; participants starting a business by region / county of residence/ place of residence; participants starting a business by status on the labor market. The conclusions of the study can be useful to different learning organizations that carry on entrepreneurship and other training courses in order to optimize their activities and better select the participants to maximize the impact of the learning programs they are offering.

PurposeHigher education institutions (HEIs) face a number of challenges in effectively managing and reporting on sustainability information, such as siloes of data and a limited distribution of information. Business intelligence (BI) can assist in addressing the challenges faced by organisations. The purpose of this study was to propose a BI framework for strategic sustainability information management (the Sustainable BI Framework) that can be used in HEIs.Design/methodology/approachThe research applied the design science research methodology whilst using a South African HEI as a case study. The problems with sustainability information management were identified, and a theoretical framework was proposed. In addition, a practical BI software tool was developed as proof of concept to address these problems and to assist with the management of strategic sustainability information in an HEI.FindingsThe proposed sustainability BI tool was evaluated through heuristic and usability evaluations with senior management. The results indicated that the usability of the BI tool was positively rated and that the framework can assist in overcoming the constraints that HEIs face in effectively managing sustainability information.Research limitations/implicationsThe research was limited to a single case. However, the theoretical framework was derived from and expanded on existing stakeholder theory, sustainability reporting theory and literature on BI dashboard development. The framework was implemented successfully in the Sustainable BI Tool prototype at the case study, and the results reveal in-depth information regarding information management for sustainability reporting in higher education.Practical implicationsThe Sustainable BI Tool is a solution that integrates data from multiple areas of sustainability and provides a single integrated view of the information to stakeholders. The information is provided through performance dashboards, which provide predictive capabilities to enable management to report on sustainability and determine if the institution is meeting its strategic goals. The lessons learnt can also assist other HEIs considering implementing BI for sustainability reporting.Social implicationsImproved sustainability reporting for HEIs provided by the BI framework can improve the environmental and social impact of the educational community.Originality/valueThis study provides the most comprehensive framework for guiding the design of a BI tool to assist in effectively managing sustainability information in HEIs.

This study proposes the use of Business Intelligence (BI) tools to support obtaining technical and economic indicators for the management of electrical energy in consumer units with photovoltaic generation added to storage systems, focusing on consumption, photovoltaic generation and storage of electrical energy in its operation. To this end, data was collected using telemetry devices connected to bidirectional electrical energy meters, allowing the acquisition of electrical quantities. The detailed analysis of the UCs energy profile over time provides valuable insights to optimize the use of electrical energy, reduce costs and promote sustainability, through automation and business intelligence resources. The availability of the expenses and revenues included in the UC's monthly billing makes it possible to analyze the values recorded in the invoices over a given period and allows the actual costs to be compared with the calculated projections, identifying possible deviations and verifying the accuracy of the estimates. Through BI tools, it is possible to identify trends and measure impacts for future projections, opening a range of innovative applications with regard to seasonality and optimization opportunities, contributing to end-user diagnosis through graphs, dashboards and customizable alarms . Furthermore, the platform creates a robust database for recording and analyzing the history of paid invoices, allowing the generation of important indicators for UC energy management.

This paper explores the design and implementation of compliance-focused financial reporting systems using a robust integration of SQL, Tableau, and Business Intelligence (BI) tools, specifically Excel. In the face of evolving regulatory mandates such as SOX (Sarbanes-Oxley Act), IFRS (International Financial Reporting Standards), and Basel III, organizations are under increasing pressure to maintain transparency, accuracy, and timeliness in their financial reporting. Leveraging a technical toolkit comprising structured query language (SQL), Tableau dashboards, and advanced Excel models, this study demonstrates how a streamlined data architecture can enable real-time, auditable, and regulator-ready financial reports. The system design begins with SQL-driven data modeling to ensure the integrity, traceability, and normalization of financial data from multiple source systems. SQL stored procedures and views are developed to automate data extraction, transformation, and loading (ETL), eliminating manual errors and enhancing audit readiness. Tableau is then used to create dynamic visualizations and compliance dashboards that allow stakeholders to monitor financial health, reconcile transactions, and detect anomalies. These dashboards integrate governance controls, flagging deviations from compliance thresholds in real time. Advanced Excel formulas and pivot tables complement this workflow by supporting in-depth variance analysis, control testing documentation, and regulatory submissions. A key contribution of this work is the development of a repeatable compliance-reporting framework that emphasizes version control, data lineage, and role-based access. By embedding risk metrics, policy thresholds, and audit trails within the reporting interface, the system minimizes regulatory risks while improving operational efficiency. Case studies from investment funds and corporate finance illustrate how this integrated architecture enhances both internal control effectiveness and external audit alignment. This technical synthesis provides a blueprint for finance professionals, auditors, and IT teams to collaborate in developing scalable, compliance-ready financial reporting systems that fulfill both operational and regulatory mandates in a digitized finance ecosystem. It highlights how bridging data engineering with financial analytics through SQL, Tableau, and Excel ensures not just compliance, but strategic foresight.

IntroductionThe growing competitiveness and the importance of data availability for organizations have created a demand for intelligent information systems capable of analyzing data to support strategy and decision-making. Organizations are generating more and more data due to new technologies associated with Industry 4.0 and Logistics 4.0, making it essential to transform this data into relevant information to streamline decision-making processes. This paper examines the influence of these technologies on gaining a competitive advantage, specifically in a logistics company, which is scarce in the literature.MethodsA case study was conducted in a Portuguese company using the Delphi method with 61 participants—employees who use the company’s integrated BI tool daily. The participants were presented with a questionnaire via the online platform Welphi, requiring qualitative responses to various statements based on the literature review and the results of semi-structured meetings with the company.ResultsThe study aimed to identify areas where employees believe more investment/ development is needed to optimize processes and improve the use of the BI tool in the future. The results indicate that BI is a crucial technology when aligned with a company’s objectives and needs, highlighting the necessity of top management’s involvement in optimizing the BI tool. Encouraging employees to use the BI tool emerged as a significant factor, underscoring the importance of leadership in innovative projects to achieve greater competitive advantage for the company.DiscussionThis study aims to understand the importance of Business Intelligence (BI) and how its functionalities should be adapted according to a company’s strategy and objectives to optimize decision-making processes. Thereby, the discussion focused on the essential role of BI technologies in leveraging the company’s competitive advantage.

In recent years, a wide range of Business Intelligence (BI) technologies have been applied to different areas in order to support the decision-making process. BI enables the extraction of knowledge from the data stored. The healthcare industry is no exception, and so BI applications have been under investigation across multiple units of different institutions. Thus, in this article, we intend to analyze some open-source/free BI tools on the market and their applicability in the clinical sphere, taking into consideration the general characteristics of the clinical environment. For this purpose, six BI tools were selected, analyzed, and tested in a practical environment. Then, a comparison metric and a ranking were defined for the tested applications in order to choose the one that best applies to the extraction of useful knowledge and clinical data in a healthcare environment. Finally, a pervasive BI platform was developed using a real case in order to prove the tool viability.