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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 present business competition, organizations are emphasizing on optimal decision support systems (DSS) to enhance its growth-oriented decision support system process. To meet these requirements, the enterprise solutions facilitate certain robust and productive business intelligence (BI) applications. On the other hand, to retain market, organizations are emphasizing on service innovation discovery and its redesign (SIDRD) paradigm using BI applications. To accomplish SIDRD, DSS-oriented BI applications require huge feedback datasets to process where the data could be retrieved from certain feedback channel or data warehouses (DWs). The data warehouses encompass datasets from varied resources, and to ensure optimal data extraction and retrieval, these DWs need robust data mining schemes compatible with multidimensional data model (MDDM). On the other hand, to provide most precise and accurate DSS applications, the data security and its privacy preservation are inevitable. Considering all these requirements, in this paper, a robust privacy-preserved mining model called Commutative RSA (CRSA) has been developed and implemented with C5.0 decision tree algorithm to achieve SIDRD objectives with BI utilities. The developed paradigm has exhibited optimal performance for optimized accuracy, overheads, and secure rule set generation for BI. The performance for the developed SIDRD has been analyzed in terms of coverage accuracy and F1 scores, and the overall system has performed optimal.

Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study clarifies the definition of business analytics combined with its functionality and the relation between business analytics and business intelligence. Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed.Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Scopus and Web of Science. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information.Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system that enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills.Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.

Business Intelligence (BI) applications allow their users to query, understand, and analyze existing data within their organizations in order to acquire useful knowledge, thus making better decisions. The core of BI applications is a data warehouse (DW) which integrates several heterogeneous data sources, mainly structured transactional databases. However, a new trend has emerged, since users of BI applications no longer limit their analysis to structured databases, and they also need unstructured sources to empower their analysis (i.e. data from the Web, company-internal reports, emails, etc.). Therefore, the ability to manage this available information is crucial for the success of the decision making process. Unfortunately, this new useful data, although easily available for companies, are often found in unstructured data (such as web pages, internal reports, emails, and so on) which make it unuseful for feeding the DW beneath BI applications. To shed light on this scenario, in this paper, we propose a novel model for using ontologies to integrate Question Answering (QA) systems in the DW. The great benefit of using QA is that specific pieces of data (i.e. answers) that will empower the analysis are detected, obtained and returned from unstructured data in order to feed the DW.

Most general-purpose distributed storage systems are not designed with near data processing (NDP) in mind. They do not respect semantic data boundaries when writing data, for example splitting a record across servers. This reduces NDP effectiveness by requiring data collation before computation. While semantic data awareness and NDP functions can be retroactively added to existing distributed storage, it is often complex and difficult to accomplish in practice. We propose sharing storage system layout information with data writers so they can adjust data layouts to prevent data alignment issues regardless of the underlying architectures. By doing so, we can simplify NDP implementation by reducing the need for data reassembly, and reduce the need for complex storage system or application extensions. We demonstrate a hinting mechanism on both HDFS with computational block storage and an erasure coded MinIO deployment, reducing data movement by up to 99% when querying CSV data with NDP co-located with the stored data. This was accomplished purely with client side data alignment, no modifications to the server side write paths, and no inter-node collation of data.

A study on the influence of leadership orchestration on e-readiness accession in higher education in the Republic of Kenya concluded in 2018 showed that there is a correlation between the leadership performance on selected leadership indices and the institution’s digital age readiness. Does this correlation also apply to adoption of evidence-based decision-making and, thus, application of data analytics, visualization and business intelligence? Cross-sectional survey design was employed. Quantitative data was collected through simple random sampling using an online questionnaire in nine purposively selected universities. Seven key questions formulated around process and data were posed to the participants. Qualitative data was obtained using focus group discussion and interviews. Pearson Moment Co-efficient of Correlation and bivariate regression were done. Content analysis was applied on the qualitative data. Significant correlation was observed with six out of seven items. It is concluded that institutional perspective on process performance and evidence-based decision-making is correlated with leadership orchestration. Effective adoption and application of data analytics, visualization and business intelligence in the digital age is dependent on institutional leadership. Recommendation is made to re-orient leadership in higher education to evidence-based decision-making and data-driven culture in order to harness the power of data analytics, visualization and business intelligence to improve the quality and productivity of higher education in Kenya.

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.

This research proposes a systematic literature review (SLR) of the application of big data, analytics, business intelligence, and artificial intelligence to company management and strategic control. Thus, this paper attempts to answer the following research questions: 1) How is the literature on the application of big data, analytics, business intelligence, and artificial intelligence to management and strategic control developed in the business, management and accounting fields? 2) On which aspects of this application does the literature focus? 3) What are the implications that arise for companies? In this paper, we used a longitudinal study of research documents in the form of last-decade literature collected from Scopus database as the leading source for the international scenario. After, we selected business, management, and accounting areas, and screened the titles and abstracts of the research documents, we based the final result on 60 scientific documents as sources relevant to the aim of this SLR. The findings highlight four main topic clusters. We specifically explain smart technologies' usefulness for each analyzed business function, and, while adopting a critical perspective, we point out the interesting current streams of research resulting from the application of new sources of technology. We conclude by proposing valuable insights gleaned from the study. Thus, our results are useful for both the academic and the professional community.

Currently, the application of Business Intelligence systems in enterprises has become popular. However, research on applying Business Intelligence in the education field is still incomplete. Theoretical and experimental studies for applying the BI system in higher education institutions (HEIs) in a developing country like Vietnam are scarce. In this paper, we aim to study the application of Business Intelligence in Vietnamese HEIs. Based on previous framework studies and the current situation in VietNam, we proposed BI framework customizations for HEIs to create a suitable common framework for the autonomous and non-public HEIs in Vietnam. Besides that, the data warehouse will be stored on a cloud service, and inbuilt data science tools are utilized for analytical and forecasting reports.KeywordsBusiness intelligenceData scienceEducationFrameworkData warehouse

Business Intelligence (BI) combines tools, that supported by technology, allow to use the data generated by an organization in order to extract information that supports decision making. This paper exposes the application of BI in Higher Education Institutions focusing on administrative and academic management processes. The review links BI with its tools balanced scorecard (BSC), data mining (DM) and data warehouse (DW). Among the results, we found a relationship between the used tool and the type of process, identifying three categories: administrative management, educational quality assurance and academic performance.

For a large company the speed of processing and distribution of data is very important. However, at Company XYZ, this has not yet been implemented in its entirety. This hinders the progress of the company. To be able to solve these problems, a storage system is needed that can access data more quickly and effectively by using a data warehouse system. This study discusses the design and construction of a data warehouse using the Kimball method. The data warehouse schema is created using SQL Server Management Studio and Microsoft SQL Server Integration Service. The analysis results from the data warehouse are displayed in the form of a dashboard using Microsoft PowerBI and a web-based application. Judging from the dashboard display that has been made, the production environment at PT Bio Farma is stable so it does not interfere with production quality. The data is entered into a web-based application, with the percentage of application feasibility of 84% after testing.

The paper proposes a new approach of data warehouse minimization by fuzzy-based ETL filter for ETL processes in business intelligence (BI) systems and discusses possibilities to tune up the process. First part introduces common company systems and possible data sources in the company. Second part states the problem with interpreting information in BI systems and explains a data representation in the BI systems. Third part of the paper defines a rule base and input and output values of the expert system. Last part of the paper proposes a two ways to minimize a data - modification border of the fuzzy set and omitting useless combinations of the linguistic variables and modifiers.

Business intelligence (BI) institutionalization has become a growing research area within the information systems (IS) discipline because of the decision-making iteration in businesses. Studies on BI application in improving decision support are not new. However, research on BI institutionalization seems sparse. BI institutionalization may positively contribute to a managerial role in using BI application repetitively for the decision-making iteration in businesses. This article aims to carry out an integrative literature review and report consolidated views of the body of knowledge. The study adopted a qualitative content analysis to generate themes about BI routinization in the decision-making iteration. Eighty-eight research articles were selected for the study. However, 57 articles were finally included for review. The findings suggest information management capability as the key necessity for BI application and its alignment with the organizational standard for BI institutionalization.

This poster presentation is to introduce both the initial objectives and activities of the new EUNIS Task Force group about the application of Business Intelligence (BI) in European Higher Education Institutions (HEI), BITF@EUNIS. Founded during the 2011 EUNIS Congress in Dublin, notable activities started as a result of a “Birds of a Feather” meeting held in Vila Real (Portugal) during the 2012 EUNIS Congress, when a group of about 19 BI enthusiasts from 9 European countries gathered to identify common topics of interest for a BI Task Force. Currently, the active members of the BI Task Force (BITF) include people from the University of Osnabrueck (Germany), the University Institute of Lisbon (Portugal), the Italian consortia AlmaLaurea and CINECA, and the Spanish Office for University Cooperation (OCU). As a starting point for further services, this core team initiated the first BITF-project to survey the occurrence and maturity of BI applications across European HEIs. Details of this survey are subject to another paper submission to EUNIS-2013. To summarize, it aims for a continuously updated collection of maturity information, enabling participants to perform a self-assessment and benchmark of its BI maturity level against other HEI, both actually and over time. Thus, it is to deliver the first cornerstone for the overall goals of the BI Task Force @EUNIS, namely, to promote the creation of a European platform to exchange and share knowledge and experiences on BI in HE institutions. Aside the maturity survey data, which of course are to be organized, processed and presented by typical BI technologies, the "BITF knowledge base" might collect and disseminate examples of BI application components or case studies as reference, e.g. data models, definitions of key measures or dimension hierarchies for analyses, or dashboard designs. Furthermore, guidelines for BI project and runtime organization are conceivable. These are just proposals for contents, which are to be defined and committed by BITF participants, next to be continued during the EUNIS 2013 conference. However, the BITF will not itself fill in the contents, in fact, it defines its responsibility only in developing and facilitating an online platform for the exchange of experiences in the sense of traditional knowledge management systems. Therefore, the directions and success are determined by its intended users, i.e. European HE institutions. With this poster, the authors want to inform about this project and invite EUNIS members for active participation in the BITF initiative.

© 2015 IEEE. Business intelligence applications are more focused on structured data and support decision makers by providing meaningful information from extracted data mainly coming from day-to-day operational databases and structured external data sources. However, the volume of unstructured data is growing very fast and data analysts need to consider this kind of data especially when analysing external data such as customers' reviews and posts in social media networks and web blogs. As external data is derived from a variety of sources, it is essential to determine the reputation of the source and provide flexibility to the analysts so that they can take into account the trust value of each source in their analysis. Another important consideration is the semantics of extracted textual data from which meaningful information is derived. Ontology is utilized in order to obtain the semantics of textual data. We do not intend to develop ontology from scratch, but rather use the existing available ontologies which are collected in an ontology repository. By using ontology, entities in the extracted textual data are linked with corresponding concepts. Useful knowledge can be inferred and used in the analysis process. In summary, we propose to use the notion of trust to evaluate data sources, and use ontology to enrich textual data. The trusted external data covered global environment, the Voice of the Market, and the Voice of the Customer can be collected and store it in the current data warehouse.