Promoting it streams in engineering admissions using Business Intelligence

this study aimed at clarifying the concept of business intelligence and what is meant by administrative development, and to know the role of business intelligence in administrative development at King Abdulaziz University. Also, to explain the relationship between them, the extent of its application in the university, and providing solutions and proposals that contribute to that. The study adopted the descriptive approach and data was collected by reviewing the existing intellectual production in the field of study. Preparing a questionnaire as a tool and distributing it electronically, (46) who entered the study (administrative leaderships at King Abdulaziz University) responded to it. The most important result of this study is the existence of an interrelated relationship between business intelligence and administrative development. The perceptions of the study sample came to the extent of applying business intelligence in administrative development in an average way. Also, there is a great importance for training in business intelligence practices in administrative Development processes. This study recommends supporting and adopting senior management and making more effort to apply business intelligence in accomplishing administrative operations. As well as giving high importance to training on business intelligence and sending informational and awareness messages to leaders at the university. And the need to seek to keep abreast of developments of business intelligence techniques to carry out and update. Further experimental studies reflecting business intelligence applications in local organizations recommend this, to find out the level and constraints of applying business intelligence.

Developed countries are constantly evolving their information systems by assessing the use of information and incorporating data analysis for decision making and forecasting. Like, developing countries are incorporate information technology standards and business intelligence technologies into their analysis of data to help them make some informed decisions. However, developing countries still have a long way to go to fully realize the potential of business intelligence. Although the spread of information technology in developing countries is rapid, the did not come with a large development benefit of it. Therefore, this study aims to identify factors influencing the adoption of business intelligence in developing countries, such technologies to raise overall socio-economic standards in developing countries. The purpose of this study is how well business intelligence have been adopted, how useful they have been, and the potential facing developing countries in adopting business intelligence and other information. Keywords: Business Intelligence, developing countries.

In this paper we explore the role of business intelligence (BI) in helping to support the management of sustainability in contemporary firms. The concepts of sustainability and corporate social responsibility (CSR) are among the most important themes to have emerged in the last decade at the global level. We suggest that BI methods and tools have an important but not yet well studied role to play in helping organizations implement and monitor sustainable and socially responsible business practices. Using grounded theory, the main contribution of our study is to propose a conceptual model that seeks to support the process of definition and monitoring of socio-environmental indicators and the relationship between their management and business strategy.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

Inspired by the theme “Exploring the Information Frontier” of the ICIS 2015 conference, the Pre-ICIS Business Analytics Congress workshop sought forward-thinking research in the areas of data science, business intelligence, analytics, and decision support with a special focus on the state of business analytics from the perspectives of organizations, faculty, and students. The research track aimed to promote comprehensive research or research-in-progress on the role of business intelligence and analytics in the creation, spread, and use of information. This work has been summarized in this chapter.

This study examines the role that business intelligence (BI) and communication technologies play in how firms may achieve organizational sensing agility, decision making agility, and acting agility in different organizational and environmental contexts. Based on the information-processing view of organizations and dynamic capability theory, we suggest a configurational analytic framework that departs from the standard linear paradigm to examine how IT’s effect on agility is embedded in a configuration of organizational and environmental elements. In line with this approach, we use fuzzy-set qualitative comparative analysis (fsQCA) to analyze field survey data from diverse industries. Our findings suggest equifinal pathways to organizational agility and the specific boundary conditions of our middle-range theory that determine what role BI and communication technologies play in organizations’ achieving organizational agility. We discuss implications for theory and practice and discuss future research avenues.

Business intelligence process analyzed data and found certain insights so that managers, executives, or higher authorities of the enterprise can take appropriate decision. Business intelligence provides past, current, and future views of the business actions. Big data indicates big amount of data that is growing exponentially with time. Data analytics check the huge amount of data and indicates some insights. The objective of big data analytics is to discover information like hidden patterns, correlations, market performance, and preferences of customer, so organization can make business decision. This paper presents a new methodology for analysis of huge amount of data using an unsupervised technique of machine learning.

The term “Business intelligence” is described as a plan or a strategy where the operations like reporting, data analysis, data mining, event processing are performed to improve the production and growth of a business enterprise or a business entity. And on the other hand, the “Knowledge management” is explained as well-organized management of resources and information within a commercial organization it can be a business too. Almost all business will have limitations and challenges which can be also known as the business problems. One of the main business problem is demand, the business plans must work according to the demand of the consumers. Analyzing the demand would provide the solutions for queries like what is the business trend? What is the need of the users? What should be the improvement make in the production? Where is the current position of the enterprise? And who all will be the competitors? For the predictive analysis a dataset of bitcoin is taken. The major aim of the study is to implement the strategies to overcome the business problems mainly the demand prediction. And the objective is to find out the relevant issues and the remedies by using knowledge management and business intelligence to the common business problems. The dataset has columns called lowest price, highest price, open price, close price, trading volume and market capital. The research methodology used is predictive analysis using PCA and K-means clustering algorithm. By this dataset predictive plots are developed as achieved results for easy analysis by using research methodology. PCA and K-means are the algorithm used for accurate prediction. The importance of study is to predict the future sale, as it is very essential for a business enterprise to find future demand so that the organization can improve production.

This study analyzes the implementation of Business Intelligence (BI) in logistics process management through a case study of a transportation company in Maranhão, Brazil. Using a qualitative documentary approach, the research examines operational data extracted from the company’s logistics management system and visualized through Microsoft Power BI dashboards. The results demonstrate that the BI implementation improved operational visibility by enabling real-time cargo monitoring, delivery deadline tracking, and route prioritization. These features enhanced managerial decision-making by allowing logistics managers to identify delays, monitor delivery status, and optimize route planning more efficiently. The dashboards also facilitated communication between departments by providing a centralized visualization of operational indicators. Although quantitative performance metrics prior to implementation were not available, qualitative evidence from system reports and managerial validation indicates significant improvements in logistics monitoring and decision support. Beyond organizational benefits, the study highlights how the adoption of digital analytics tools in logistics can contribute to greater operational resilience and supply chain efficiency in regional economic contexts. The findings provide practical insights into the role of business intelligence in supporting logistics management and improving operational coordination in emerging economies.

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 the era of the information economic development of society, which is based on the digitalization of communication technologies and software for business administration algorithms, business analytics occupies an important place, a tool for obtaining high-quality, unique, and expensive information, maintaining the global competitive advantages of projects in the face of long-term force majeure risks of the viability of the economy. Since the middle of the last century, information technologies have been in constant development, gradually being introduced into all spheres and strata of society: production, accounting and analytical support, management, analysis, forecasts, risks. This paper considers the tools of digital business transformation and trends within the new knowledge paradigm based on a controlled understanding of the value properties of an information product. The successful use of business intelligence can indicate an increase in the success of the company and the country's competitiveness in the global market. Business analytics of companies is currently undergoing a stage of reengineering due to testing various programs, understanding, and analyzing the database, improving communication interaction at all levels of business processes, and making decisions in conditions of interactivity and transparency. The state of traceability and transparency of information, evolution and dynamics of conclusions and proposals over a long period of the company's activity in the sectoral and global aspect is of value. The features, characteristics, and functions of business intelligence (BI) systems as a tool for increasing the efficiency of the company's activities are analyzed. Business intelligence systems were considered as a tool for data analysis and processing, a comparative analysis of modern business intelligence systems and market trends of business intelligence systems was conducted, because of which the role of BI systems in increasing the efficiency of the company's activities was revealed. Keywords: business analytics, business data analysis, data integration, data presentation, IT sphere, company performance.

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.

Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.

The rise of social media has fundamentally changed the way information is produced, disseminated, and consumed in the digital age, which has profound economic and business effects. Among many different types of social media, social broadcasting networks such as Twitter in the U.S. and “Weibo” in China are particularly interesting from a business perspective. In the case of Twitter, the huge amounts of real-time data with extremely rich text, along with valuable structural information, makes Twitter a great platform to build Business Intelligence (BI) systems. We propose a framework of social-broadcasting-based BI systems that utilizes real-time information extracted from these data with text mining techniques. To demonstrate this framework, we designed and implemented a Twitter-based BI system that forecasts movie box office revenues during the opening weekend and forecasts daily revenue after 4 weeks. We found that incorporating information from Twitter could reduce the Mean Absolute Percentage Error (MAPE) by 44% for the opening weekend and by 36% for total revenue. For daily revenue forecasting, including Twitter information into a baseline model could reduce forecasting errors by 17.5% on average. On the basis of these results, we conclude that social-broadcasting-based BI systems have great potential and should be explored by both researchers and practitioners.

Possibility of Improving Efficiency within Business Intelligence Systems in Companies