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Journal articles on the topic 'Business intelligence Planning'

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Author: Grafiati

Published: 10 December 2022

Last updated: 30 January 2023

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1

Lin, Wei-Syun, I.-Chou Hung, Meng-Hsuan Chia, Chan-Chien Yu, and Kuo-Hua Wu. "Intelligence Itinerary Planning and Novel Tourism Business." Advanced Science Letters 21, no. 5 (May 1, 2015): 1117–21. http://dx.doi.org/10.1166/asl.2015.6045.

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2

Cristescu, Marian Pompiliu. "Business Intelligence Integrated Solutions." Land Forces Academy Review 22, no. 4 (December 1, 2017): 270–75. http://dx.doi.org/10.1515/raft-2017-0036.

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Abstract A Business Intelligence solution concerns the simple, real-time access to complete information about the business shown in a relevant format of the report, graphic or dashboard type in order help the taking of strategic decisions regarding the direction in which the company goes. Business Intelligence does not produce data, but uses the data produced by the company’s applications. BI solutions extract their data from ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), HCM (Human Capital Management), and Retail, eCommerce or other databases used in the company.

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3

James, Roger. "Business intelligence and capacity planning: web-based solutions." British Journal of Hospital Medicine 71, no. 7 (July 2010): 395–98. http://dx.doi.org/10.12968/hmed.2010.71.7.48998.

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4

Rapoport, Elizabeth, and Anna Hult. "The travelling business of sustainable urbanism: International consultants as norm-setters." Environment and Planning A: Economy and Space 49, no. 8 (January 9, 2017): 1779–96. http://dx.doi.org/10.1177/0308518x16686069.

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This article examines the international travels of ideas about sustainable urban planning and design through a focus on private sector architecture, planning and engineering consultants. These consultants, who we refer to as the global intelligence corps (GIC), package up their expertise in urban sustainability as a marketable commodity, and apply it on projects around the world. In doing so, the global intelligence corps shape norms about what constitutes ‘good’ sustainable urban planning, and contribute to the development of an internationalised travelling model of sustainable urbanism. This article draws on a broad study of the industry (GIC) in sustainable urban planning and design, and two in-depth case studies of Swedish global intelligence corps firms working on Chinese Eco-city projects. Analysis of this material illustrates how the global intelligence corps’s work shapes a traveling model of sustainable urbanism, and how this in turn creates and reinforces particular norms in urban planning practice.

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5

Siskos, Evangelos, Konstantia Darvidou, N. P. Lytvynenko, and M. P. Khmara. "INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO BUSINESS PROCESSES." Actual Problems of International Relations, no. 147 (2021): 65–73. http://dx.doi.org/10.17721/apmv.2021.147.1.65-73.

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AI performs many tasks, including - creating and working in symbolic computing systems, creating and working in systems with fuzzy logic, cognitive psychology, understanding natural language, creating expert systems, computer linguistics, automation of various vocabulary and lexical cards, behavior planning (search and proposal of the most optimal way to achieve the goal, based on this situation), machine translation, creation and management of intelligent robots and groups of mobile robots, etc.

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6

Babbar, Sunil, and Arun Rai. "Competitive intelligence for international business." Long Range Planning 26, no. 3 (June 1993): 103–13. http://dx.doi.org/10.1016/0024-6301(93)90012-5.

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7

Nofal, Muhmmad I., and Zawiyah M. Yusof. "Integration of Business Intelligence and Enterprise Resource Planning within Organizations." Procedia Technology 11 (2013): 658–65. http://dx.doi.org/10.1016/j.protcy.2013.12.242.

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8

Orgaz, Gema Bello, David F. Barrero, María D. R-Moreno, and David Camacho. "Acquisition of business intelligence from human experience in route planning." Enterprise Information Systems 9, no. 3 (March 14, 2013): 303–23. http://dx.doi.org/10.1080/17517575.2012.759279.

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9

Tavera Romero, Carlos Andrés, Jesús Hamilton Ortiz, Osamah Ibrahim Khalaf, and Andrea Ríos Prado. "Business Intelligence: Business Evolution after Industry 4.0." Sustainability 13, no. 18 (September 7, 2021): 10026. http://dx.doi.org/10.3390/su131810026.

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Industry 4.0 is a set of technologies that companies require to promote innovation strategies and obtain a rapid response in dynamic markets. It focuses mainly on interconnectivity, digital technology, predictive analytics and machine learning to revolutionize the way companies operate and develop. Therefore, this article proposes and motivates the implementation of Industry 4.0 in organizations. Studying the state of the art and reviewing the current situation of business intelligence (BI) technology, the way it has positively impacted organizations at the economic and business level in terms of decision-making and some success stories implemented in different business, academic, social and governmental environments. Moreover, it addresses the future expected for Industry 4.0 primarily in BI and how companies should face this revolution. This article provides knowledge contribution about the current state and positive consequences of Industry 4.0, and high development in technology when implemented in the organization and the harmonization between production and intelligent digital technology.

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Pawar, Badrinarayan Shankar, and Ramesh Sharda. "Obtaining business intelligence on the internet." Long Range Planning 30, no. 1 (February 1997): 110–21. http://dx.doi.org/10.1016/s0024-6301(96)00100-8.

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11

Ansari, Nazeen, and Gopakumaran Thampi. "Investigating the Synergistic Relationship between Enterprise Resource Planning and Business Intelligence." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (October 30, 2014): 5090–93. http://dx.doi.org/10.24297/ijct.v13i10.2331.

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This study aims to discuss the synergistic relationship between enterprise resource planning (ERP) and business intelligence (BI). It focuses on the benefits obtained from ERP and BI framework integration. The purpose of this study is to identify the software engineering challenges associated with ERPâ€“BI integration. It also discusses methods for ERPâ€“BI integration and shift toward open source and standard-based integration technologies.

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12

Nofal, Muhmmad Islam Mahmoud, and Zawiyah Mohammad Yusof. "Conceptual model of enterprise resource planning and business intelligence systems usage." International Journal of Business Information Systems 21, no. 2 (2016): 178. http://dx.doi.org/10.1504/ijbis.2016.074260.

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13

Marrella, Andrea. "Automated Planning for Business Process Management." Journal on Data Semantics 8, no. 2 (November 1, 2018): 79–98. http://dx.doi.org/10.1007/s13740-018-0096-0.

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14

Lucić, Danijela. "Open-Source Information – the Basis for Business Intelligence (BI)." Informatologia 55, no. 3-4 (December 23, 2022): 219–31. http://dx.doi.org/10.32914/i.55.3-4.2.

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The paper discusses the relationship between business intelligence (BI) and intelligence discipline based on information from open sources, better known as OSINT (open-source intelligence). The importance of the intelligence methodology – the intelligence cycle – is emphasized, which presupposes planning, systematic collection of information, its processing and analysis, and dissemination to end users. The goal is, ultimately, to make a business decision based on the knowledge. The development of BI and OSINT is discussed as well as the ideas that emerged in the 1970s when the importance of intelligence logic in the sphere of economy, and in society in general, was pointed out. Given the lack of empirical examples, the paper points out at the normative level that successful business is possible by relying on business intelligence (BI) and open-source information (OSINT), assuming the possession of analytical capacities, above all human and technological, which are capable to extrapolate the information necessary for a quality business decision from the immeasurable amount of data.

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15

Sutherland, John W. "Intelligence-driven strategic planning and positioning." Technological Forecasting and Social Change 34, no. 3 (November 1988): 279–303. http://dx.doi.org/10.1016/0040-1625(88)90072-8.

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16

Kim, Huong Trang. "Do managers’ emotional intelligence matter for SMEs’ business practices?" Economics and Business Letters 10, no. 3 (August 2, 2021): 200–207. http://dx.doi.org/10.17811/ebl.10.3.2021.200-207.

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An under-researched question in the current literature is to what extent managers’ emotional intelligence (EQ) play a role in driving business practices. To explore this question, we carried out a survey with 320 textile and garment SMEs in Vietnam. We find that one standard deviation increase in EQ of managers is related to 3.87% increase in business practices adoption. Notably, EQ levels of managers have different impact on adoption of each business practices. These effects on marketing practices are strongest, while EQ level of managers has modest effect on financial planning practices.

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17

Ellis, David. "Business Intelligence Enhances Strategic, Long-Range Planning in the Commercial Aerospace Industry." International Journal of Business Intelligence Research 2, no. 4 (October 2011): 17–28. http://dx.doi.org/10.4018/jbir.2011100102.

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The world’s largest aircraft manufacturers like Boeing and Airbus have traditionally been dominant in the commercial aerospace industry, but due to the rise of several smaller commercial aircraft companies and in spite of air travel increasing each year, it will be paramount for Boeing and Airbus to thoroughly understand past and current market conditions and be able to combine their understanding with the proper analytical tools to anticipate the market demands of the future if they are to remain the world leaders in their industry. This paper presents a discussion of industry factors such as airline routes, past passenger demands in different regions of the world and the sizes and types of aircraft that were required to support those demands, and more importantly, how analysis of that information is integral to the projection of future demands within the commercial aerospace market which will facilitate Boeing and Airbus positioning themselves to provide their airline customers with the right product at the right time.

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18

DANILCZUK, Wojciech, and Arkadiusz GOLA. "COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY." Applied Computer Science 16, no. 3 (September 30, 2020): 42–55. http://dx.doi.org/10.35784/acs-2020-20.

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Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in small and medium enterprises.

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19

Miranda, Eka, Firmansyah Firmansyah, and Davies Ezra Emerald. "Desain Business Intelligence untuk Manajemen Rumah Sakit." JURNAL SISTEM INFORMASI BISNIS 11, no. 1 (May 27, 2021): 62–69. http://dx.doi.org/10.21456/vol11iss1pp62-69.

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Organizational management, as well as hospital management, could not work precisely without defining the performance indicators to control all business process. This situation encourages the need for information and data analysis availability. BI includes applications, infrastructure, tools and practices that enable organizations to access and analyze data and information to improve and optimize the decisions and organization performance. BI has the potential to improve the quality, efficiency and effectiveness of hospital health services as well. The objective of this study was to design business intelligence prototype for the hospital. BI design was carried out with a Business Intelligence Roadmap approach which has 6 main stages, namely: (1) Justification, (2) Planning, (3) Business Analysis, (4) Design, (5) Construction and (6) Deployment. Data were collected from hospital activities includes registration, Electronic Medical Record (EMR) in the Imaging, Laboratory, Pharmacy, Operating Theater and Medical Check-Up departments activities. Designing BI was preceded by identifying technical and non-technical needs, then continued by designing BI itself. BI roadmap approach was used for this propose. Technical requirements for designing BI include hardware and software infrastructure readiness, while non-technical requirements include Business Analysis which consists of Project Requirements Definition, Data Analysis, Application Prototyping and Metadata repository Analysis. Designing BI itself includes: Designing a multidimensional database and designing ETL. The user interfaces for BI was shown in the Performance Dashboard, which allows organizations to track all aspects of their daily business activities and performance.

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20

González-Ferrer, Arturo, Juan Fernández-Olivares, and Luis Castillo. "From business process models to hierarchical task network planning domains." Knowledge Engineering Review 28, no. 2 (March 4, 2013): 175–93. http://dx.doi.org/10.1017/s0269888912000410.

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AbstractHierarchical Task Network (HTN) planning paradigm has been widely used during the last decade to model and solve planning and scheduling (P&S) problems, and it has proved to be very useful in the planning and coordination of human tasks. At the same time, Business Process Management (BPM) tools are being increasingly used in the modeling of organizations’ business practices and processes, but their life cycle has shown to have some shortages (as the possibility to obtain context-dependent plan instances). In this paper we present a methodology and software framework to translate Business Process Models into HTN P&S domains, in order to cover some of these deficiencies.

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21

Muntean, Mihaela. "Business Intelligence Issues for Sustainability Projects." Sustainability 10, no. 2 (January 29, 2018): 335. http://dx.doi.org/10.3390/su10020335.

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22

Hoffmann, J., I. Weber, and F. M. Kraft. "SAP Speaks PDDL: Exploiting a Software-Engineering Model for Planning in Business Process Management." Journal of Artificial Intelligence Research 44 (July 31, 2012): 587–632. http://dx.doi.org/10.1613/jair.3636.

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Planning is concerned with the automated solution of action sequencing problems described in declarative languages giving the action preconditions and effects. One important application area for such technology is the creation of new processes in Business Process Management (BPM), which is essential in an ever more dynamic business environment. A major obstacle for the application of Planning in this area lies in the modeling. Obtaining a suitable model to plan with -- ideally a description in PDDL, the most commonly used planning language -- is often prohibitively complicated and/or costly. Our core observation in this work is that this problem can be ameliorated by leveraging synergies with model-based software development. Our application at SAP, one of the leading vendors of enterprise software, demonstrates that even one-to-one model re-use is possible. The model in question is called Status and Action Management (SAM). It describes the behavior of Business Objects (BO), i.e., large-scale data structures, at a level of abstraction corresponding to the language of business experts. SAM covers more than 400 kinds of BOs, each of which is described in terms of a set of status variables and how their values are required for, and affected by, processing steps (actions) that are atomic from a business perspective. SAM was developed by SAP as part of a major model-based software engineering effort. We show herein that one can use this same model for planning, thus obtaining a BPM planning application that incurs no modeling overhead at all. We compile SAM into a variant of PDDL, and adapt an off-the-shelf planner to solve this kind of problem. Thanks to the resulting technology, business experts may create new processes simply by specifying the desired behavior in terms of status variable value changes: effectively, by describing the process in their own language.

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23

Destiandi, Niki, and Aditiya Hermawan. "Business Intelligent Method For Academic Dashboard." bit-Tech 1, no. 2 (December 17, 2018): 11–20. http://dx.doi.org/10.32877/bt.v1i2.42.

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Business Intelligence Lifecycle is a method for developing effective business intelligence (BI) decision support applications such as the Academic Dashboard. There are six steps in the BI life cycle from the beginning to implementation such as Justification, Planning, Business Analysis, Design, Construction, and Deployment, where each step is developed to be more detailed in accordance with BI's environmental needs (L. T. Moss). Management of tertiary institutions in Indonesia requires appropriate and fast academic reports that make it possible to make strategic decisions and in order to improve the quality of education. Academic evaluations can be presented with the dashboard being easy for decision making. The dashboard is a page that displays graphics as a KPI from an organization and provides everything needed to make key research results [4]. Problems that occur there are a lot of academic data that is stored but when turning it into a report at the time of evaluation academic activities are difficult and require a long time and require monitoring, evaluation and measurement tools that can measure the performance of universities. The Business Intelligence Lifecycle can be used to provide information to produce high resolution by adding KPI components.

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Ozhiganov, Edward N., Alexander A. Chursin, and Ekaterina S. Kaplun. "BUSINESS INTELLIGENCE (BI) APPLICATIONS AND STRATEGIC CAMPAIGN PERFORMANCE MANAGEMENT." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3, no. 12 (2021): 16–20. http://dx.doi.org/10.36871/ek.up.p.r.2021.12.03.003.

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Today's business environment is more complex and dynamic than ever before, with companies operating under the influence of globalization, volatile markets, regulatory changes and technological ad-vances, while more information technology (IT) solutions are being offered to manage the complexity of busi-ness systems. The development of IT support goes along several interdependent directions: product life cy-cles are linked through digital networks, sensor technologies are embedded in the physical production envi-ronment, a system of semi- and unstructured data bases is used, etc. The article shows that the implementation of these areas in business intelligence systems is promising from two points of view: firstly, the integration of data on technical processes and business results allows more targeted planning and man-agement of business processes at the operational and tactical levels, and second, it allows you to get a new understanding of them, which is used at the level of strategic management of campaign performance and strategic decision-making.

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Marcella, Jr., Albert J., and James V. Rauff. "Utilizing Expert Systems To Evaluate Disaster Recovery Planning." Journal of Applied Business Research (JABR) 11, no. 1 (September 21, 2011): 30. http://dx.doi.org/10.19030/jabr.v11i1.5889.

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The application of the techniques of artificial intelligence to the task of disaster recovery plan auditing is discussed. A prototype expert system for DRP auditing is described and evaluated. We conclude that automated DRP auditing is feasible.

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Kaushik, Dr Priyanka. "Role and Application of Artificial Intelligence in Business Analytics: A Critical Evaluation." International Journal for Global Academic & Scientific Research 1, no. 3 (October 13, 2022): 01–11. http://dx.doi.org/10.55938/ijgasr.v1i3.15.

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The commercial adoption of artificial intelligence in business analytics tools across multiple industries is being driven by the rising volume and complexity of company data. Business organizations are being helped by the widespread application of artificial intelligence and machine learning in business intelligence to glean meaningful insights from sizable and complicated datasets and provide business recommendations that are clear to any business user. Within the industry of information technology, the business analytics is used to refer the usage of computing to gain the insights from data. Such data can be acquired from the internal sources of company like from its enterprise resource planning application, warehouse and mart data, providers of third-party data, or from other public sources. Sample of 198 respondents from different business sectors were to know the role, application and impact of artificial intelligence in business analytics. It is found that there is a significant role of artificial intelligence in business analytics. This paper provides a rigorous examination of the literature in an effort to illustrate the value-creating procedures and to elucidate how organizations may employ AI technologies in their operations. In this study, the forms of AI use in the organizational setting, first- and second-order impacts, and usage typologies are highlighted along with the significant enablers and inhibitors of AI adoption and use. Our analysis synthesizes the current literature.

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Paradza, Dignity, and Olawande Daramola. "Business Intelligence and Business Value in Organisations: A Systematic Literature Review." Sustainability 13, no. 20 (October 15, 2021): 11382. http://dx.doi.org/10.3390/su132011382.

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Organisations must derive adequate business value (BV) from Business Intelligence (BI) adoption to retain their profitability and long-term sustainability. Yet, the nuances that define the realisation of BV from BI are still not understood by many organisations that have adopted BI. This paper aims to foster a deeper understanding of the relationship between Business Intelligence (BI) and business value (BV) by focusing on the theories that have been used, the critical factors of BV derivation, the inhibitors of BV, and the different forms of BV. To do this, a systematic literature review (SLR) methodology was adopted. Articles were retrieved from three scholarly databases, namely Google Scholar, Scopus, and Science Direct, based on relevant search strings. Inclusion and exclusion criteria were applied to select ninety-three (93) papers as the primary studies. We found that the most used theoretical frameworks in studies on BI and BV are the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Technology-Organisation-Environment (TOE), and Contingency Theory (CON). The most acknowledged critical factors of BV are skilled human capital, BI Infrastructure, data quality, BI application and usage/data culture, BI alignment with organisational goals, and top management support. The most acclaimed inhibitors of BV are data quality and handling, data security and protection, lack of BI Infrastructure, and lack of skilled human resource capital, while customer intelligence is the most acknowledged form of BV. So far, many theories that are relevant to BI and BV, critical factors, inhibitors, and forms of BV were marginally mentioned in the literature, requiring more investigations. The study reveals opportunities for future research that can be explored to gain a deeper understanding of the issues of BV derivation from BI. It also offers useful insights for adopters of BI, BI researchers, and BI practitioners.

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Kašparová, Petra. "New Approaches to the Application of Business Intelligence in the Strategic Management Process." ACC Journal 28, no. 2 (September 2022): 33–43. http://dx.doi.org/10.15240/tul/004/2022-2-003.

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This article aims to find the optimal links and ways to use the outputs of business intelligence in strategic company management effectively. A total of 41 tools supporting individual phases of the strategic decision-making process were selected within the literature search. They were divided into six successive stages: mission and vision determination, environment analysis, setting long-term goals, strategy formulation, implementation, and, finally, monitoring. The particular techniques were subsequently assigned to the utilization rate of business intelligence within their application. The resulting model connects the outcomes obtained from the evaluation with the scheme of the strategic planning process inspired by literary review. The most appropriate application of business intelligence takes place in the phases dealing with analyzing the company situation, such as environmental analysis and strategy monitoring. According to the obtained results, the involvement of business intelligence outputs should not be overlooked even in the stages devoted to setting long-term goals and formulating strategy.

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29

Barba, Irene. "Constraint-based planning and scheduling techniques for the optimized management of business processes." AI Communications 26, no. 2 (2013): 251–53. http://dx.doi.org/10.3233/aic-130554.

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30

Mashingaidze, Sivave. "A tautology of ancient leadership intelligence: An interpretive auto-ethnographic research." Corporate Ownership and Control 13, no. 1 (2015): 351–55. http://dx.doi.org/10.22495/cocv13i1c3p2.

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The main purpose of the article was to look into how business and management could extract from ancient data base of leadership intelligence for solutions. The article cherry picked a few great historical leaders who won wars using their leadership intelligence. An Interpretive auto-ethnography methodology was used and strategic intelligence qualities such as Changing the mood, Boldness of vision, Doing the planning, Leading from the front, Bringing people with you and finally Likeability Factor was explored from these leaders. The results was that all the above mentioned strategic intelligence qualities were quintessential for these historical leaders to achieve their objectives hence business and management today can learn and tap from these qualities for a competitive strategy.

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Tiara, Khanna, Siti Nurhayati, and Novita Heriyani. "Optimalisasi Business Intelligence Pada Widuri Berbasis Mediawiki Dalam Monitoring Rekapitulasi Data." Creative Information Technology Journal 4, no. 1 (December 1, 2016): 72. http://dx.doi.org/10.24076/citec.2016v4i1.96.

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Seiring perkembangan zaman dan teknologi pada Perguruan Tinggi yang mengalami peningkatan sangat pesat. Peranan modal intelektual menjadi sangat penting dan strategis dalam pengelolaan program akademik untuk mencapai keunggulan kompetitif. Business Intelligence sebagai elemen penting dalam Perguruan Tinggi difungsikan untuk membantu manajerial mengelola modal intelektual dalam proses pengambilan keputusan guna meningkatkan kinerja Perguruan Tinggi. Penelitian ini bertujuan untuk mengimplementasikan Business Intelligence demi mendukung pengelolaan program akademik kearah yang lebih kompetitif melalui sistem perencanaan, pengukuran dan meningkatkan kinerja berbasis modal intelektual. Salah satu implementasi yang menggunakan sistem Business Intelligence dalam Perguruan Tinggi Raharja ialah website Widuri. Penggunaan Business Intelligence pada Widuri berbasis pada teknologi informasi yang bertujuan untuk mendesain, mengukur, mengelola dan mengembangkan modal intelektual, antara lain berupa pengetahuan, skill dan keahlian sumberdaya manusia Perguruan Tinggi Raharja. Penggunaan indikator kinerja Perguruan Tinggi Raharja dianalisis untuk memenuhi standar mutu Perguruan Tinggi yang kompetitif. Sehingga dengan penggunaan Business Intelligence pada Widuri mempermudah pengguna dan Admin mengetahui data secara akurat.As the development of the times and the technology at the College is currently undergoing a very rapid increase. The role of intellectual capital is becoming very important and strategic in the management of academic programs to achieve competitive advantage. Business Intelligence as an important element in the College functioned to help manage intellectual capital in the managerial decision-making process in order to improve the performance of colleges. This research aims to implement Business Intelligence to support the management of academic program towards a more competitive through a system of planning, measuring and improving performance-based intellectual capital. One implementation uses a system of Business Intelligence in the College website is Widuri Raharja. The use of Business Intelligence in information technology-based Thistle aims to design, measure, manage and develop intellectual capital, inter alia in the form of knowledge, skill and expertise of human resources College Raharja. The use of indicator performance College Raharja analyzed to meet quality standards at competitive Colleges. So with the use of Business Intelligence at Thistle make it easier users and Admins know data accurately.

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Rao, M. Venkata Krishna, Ch Suresh, K. Kamakshaiah, and M. Ravikanth. "Prototype Analysis for Business Intelligence Utilization in Data Mining Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 29, 2017): 30. http://dx.doi.org/10.23956/ijarcsse.v7i7.93.

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Tremendous increase of high availability of more disparate data sources than ever before have raised difficulties in simplifying frequent utility report across multiple transaction systems apart from an integration of large historical data. It is main focusing concept in data exploration with high transactional data systems in real time data processing. This problem mainly occurs in data warehouses and other data storage proceedings in Business Intelligence (BI) for knowledge management and business resource planning. In this phenomenon, BI consists software construction of data warehouse query processing in report generation of high utility data mining in transactional data systems. The growth of a huge voluminous data in the real world is posing challenges to the research and business community for effective data analysis and predictions. In this paper, we analyze different data mining techniques and methods for Business Intelligence in data analysis of transactional databases. For that, we discuss what the key issues are by performing in-depth analysis of business data which includes database applications in transaction data source system analysis. We also discuss different integrated techniques in data analysis in business operational process for feasible solutions in business intelligence

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Roth, Jan A., Nicole Goebel, Thomas Sakoparnig, Simon Neubauer, Eleonore Kuenzel-Pawlik, Martin Gerber, Andreas F. Widmer, et al. "Secondary use of routine data in hospitals: description of a scalable analytical platform based on a business intelligence system." JAMIA Open 1, no. 2 (September 20, 2018): 172–77. http://dx.doi.org/10.1093/jamiaopen/ooy039.

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Abstract We describe a scalable platform for research-oriented analyses of routine data in hospitals, which evolved from a state-of-the-art business intelligence architecture for enterprise resource planning. This platform involves an in-memory database management system for data modeling and analytics and a high-performance cluster for more computing-intensive analytical tasks. Setting up platforms for research-oriented analyses is a highly dynamic, time-consuming, and costly process. In some health care institutions, effective research platforms may be derived from existing business intelligence systems.

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Nascimento, Alexandre Moreira, Vinicius Veloso De Melo, Anna Carolina Muller Queiroz, Thomas Brashear-Alejandro, and Fernando de Souza Meirelles. "Artificial intelligence applied to small businesses: the use of automatic feature engineering and machine learning for more accurate planning." Revista de Contabilidade e Organizações 14 (October 14, 2020): e171481. http://dx.doi.org/10.11606/issn.1982-6486.rco.2020.171481.

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The purpose of this study is to develop a predictive model that increases the accuracy of business operational planning using data from a small business. By using Machine Learning (ML) techniques feature expansion, resampling, and combination techniques, it was possible to address several existing limitations in the available research. Then, the use of the novel technique of feature engineering allowed us to increase the accuracy of the model by finding 10 new features derived from the original ones and constructed automatically through the nonlinear relationships found between them. Finally, we built a rule-based classifier to predict the store's revenue with high accuracy. The results show the proposed approach open new possibilities for ML research applied to small and medium businesses.

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Bany Mohammad, Ashraf, Manaf Al-Okaily, Mohammad Al-Majali, and Ra’ed Masa’deh. "Business Intelligence and Analytics (BIA) Usage in the Banking Industry Sector: An Application of the TOE Framework." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 4 (October 17, 2022): 189. http://dx.doi.org/10.3390/joitmc8040189.

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This study aims to examine the factors that influence business intelligence and analytics (BIA) usage in the banking sector. Based on a comprehensive literature review, a theoretical model was developed to explore the impact of three key factors on business intelligence and analytics adoption and usage in the banking sector, namely technological, organizational, and environmental factors. The study used the Statistical Package for the Social Sciences (SPSS) to analyze data collected from 120 employees of Jordan Arab bank. The results revealed the critical impact of not only the existence of data and technology infrastructure but also the importance and availability of management and human resources support and capabilities. This study suggests that, more importantly, successful planning for business intelligence and analytics should go beyond the technology aspects to gain the full benefits of such technology, especially in the banking sector. Yet, we argue that more research needs to be conducted, especially in the context of developing countries, to fully understand how banking sectors can successfully implement and utilize business intelligence and analytics.

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Caserio, Carlo. "IT Governance in Enterprise Resource Planning and Business Intelligence Systems Environment: a Conceptual Framework." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 12, no. 1 (March 18, 2017): 3041–49. http://dx.doi.org/10.24297/ijmit.v12i1.5915.

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A large number of studies has been carried out on IT governance during the last decade, with the aim to support companies in implementing IT governance frameworks aligned with the business strategy. However, companies which create value and develop competitive advantage through an intensive use of Enterprise Resource Planning (ERP) and Business Intelligence Systems (BIS), should implement an IT governance framework tailored on Critical Success Factors (CSFs) of both systems. To organise the main studies on IT governance frameworks, as well as to support companies in designing (or reviewing) their IT governance framework by considering the CSFs of ERP and BIS, a conceptual framework is introduced and discussed. The insights arising from this study are supported by the literature and suggest that an IT governance framework should be designed by considering the ERP and BIS critical success factors, especially in companies whose success heavily depends on such systems.

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Koupaei, Mehrdad Nouri, and Alireza Arshadi Khamseh. "An integrated enterprise resources planning framework for supply chain management using business intelligence tools." International Journal of Business Performance and Supply Chain Modelling 9, no. 3 (2017): 202. http://dx.doi.org/10.1504/ijbpscm.2017.089035.

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Koupaei, Mehrdad Nouri, and Alireza Arshadi Khamseh. "An integrated enterprise resources planning framework for supply chain management using business intelligence tools." International Journal of Business Performance and Supply Chain Modelling 9, no. 3 (2017): 202. http://dx.doi.org/10.1504/ijbpscm.2017.10010001.

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Nouri Koupaei, Mehrdad, and Mohammad Mehdi Movahedi. "An investigation on the effects of business intelligence and enterprise resources planning on TQM." Uncertain Supply Chain Management 2, no. 3 (2014): 191–98. http://dx.doi.org/10.5267/j.uscm.2014.4.001.

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Cates, James E., Sam S. Gill, and Natalie Zeituny. "The Ladder of Business Intelligence (LOBI): a framework for enterprise IT planning and architecture." International Journal of Business Information Systems 1, no. 1/2 (2005): 220. http://dx.doi.org/10.1504/ijbis.2005.007408.

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Mashingaidze, Sivave. "Descriptive business intelligence analysis: Cutting edge strategic asset for SMEs, is it really worth it?" Journal of Governance and Regulation 3, no. 2 (2014): 70–83. http://dx.doi.org/10.22495/jgr_v3_i4_p7.

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The purpose of this article is to provide a framework for understanding and adoption of Business Intelligence by (SMEs) within the Zimbabwean economy. The article explores every facet of Business Intelligence, including internal and external BI as cutting edge strategic asset. A descriptive research methodology has been adopted. The article revealed some BI critical success factors for better BI implementation. Findings revealed that organizations which have the greatest success with BI travel an evolutionary path, starting with basic data and analytical tools and transitioning to increasingly more sophisticated capabilities until BI becomes an intrinsic part of their business culture and ROI is realized. Findings are useful for managers, policy makers, business analysts, and IT specialists in dealing with planning and implementation of BI systems in SMEs.

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Lasminiasih, Lasminiasih, Rooswhan Budhi Utomo, and Sigit Nurdianto. "Analisis Pengaruh Permodalan, Pemasaran, Teknologi & Inovasi, Kecerdasan Spiritual dan Perencanaan Strategi Terhadap Kinerja Usaha Kecil Menengah (UKM) Mebel di Klaten Jawa Tengah." BISMA (Bisnis dan Manajemen) 11, no. 1 (October 31, 2018): 47. http://dx.doi.org/10.26740/bisma.v11n1.p47-66.

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Small medium enterprises (SMEs) constitute a business activity that is able to expand employment and provide economic services broadly to society and can play a role in the process of Equalization and the increase in people's income, encouraging the growth of economy and play a role in realizing stability nationwide. The purpose of this research is to know the influence of the capital, marketing, technology and innovation, spiritual intelligence and planning strategy on performance of small medium-sized enterprise (SMEs) furniture in Klaten, Central Java. The object of the research done in the village of Dayan, Ceper, Klaten, Central Java. The methods used in this research are multiple linear regression by disseminating a questionnaire in directly. The results showed that capital, marketing, technology and innovation, spiritual intelligence and planning strategy of the influence on the performance of small and medium businesses (SMEs) furniture in the Klaten.

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Gebhardt, Gary F., Francis J. Farrelly, and Jodie Conduit. "Market Intelligence Dissemination Practices." Journal of Marketing 83, no. 3 (February 21, 2019): 72–90. http://dx.doi.org/10.1177/0022242919830958.

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Market intelligence is a cornerstone of the marketing concept and essential to market-focused strategic planning and implementation. Although the importance of market intelligence is widely accepted, how managers can ensure the organization-wide generation, dissemination, and responsiveness to market intelligence remains a persistent challenge. In this article, the authors investigate market intelligence dissemination practices and their resulting managerial responses. Using qualitative methods, the authors identify five market intelligence dissemination practices that either update and reinforce organization members’ existing schemas (mental models) of the market or create new, shared schemas of the market. Specifically, they find that the creation, existence, or absence of organizationally shared market schemas is crucial in explaining the effectiveness of different market intelligence dissemination practices. Thus, in addition to being experts on market intelligence, intelligence directors must be authorities on organizational learning and ways to create shared meaning structures that enable disseminated intelligence to be understood and used within their organizations. The authors conclude with suggestions for practitioners on how to manage intelligence dissemination across their organizations more effectively and efficiently.

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Thi Thu Huong Dinh, Thi Thu Huong Dinh, Thai Thuy Lam Ha Thi Thu Huong Dinh, and Hongjuan Yang Thai Thuy Lam Ha. "Predicting Credit Accessibility Based on Social Capital and Artificial Intelligence." 電腦學刊 33, no. 5 (October 2022): 039–47. http://dx.doi.org/10.53106/199115992022103305004.

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<p>Social capital is a significant factor that determines the success of a business, company or individual. The paper presents a new method to predict the aquaculture household’s ability to access to credit, based on social capital and artificial intelligence. Social capital data is collected through surveys of aquaculture households, and then replying on artificial intelligence algorithms to build predictive models. The prediction results show that our proposed method can accurately predict households accessing the formal credit market. This is the basis for the planning and development of aquaculture in potential areas. </p> <p> </p>

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Klepac, Goran. "Customer Profiling in Complex Analytical Environments Using Swarm Intelligence Algorithms." International Journal of Swarm Intelligence Research 7, no. 3 (July 2016): 43–70. http://dx.doi.org/10.4018/ijsir.2016070103.

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Customer profiling is always an interesting task from perspective of business. It became even bigger challenge in situation of complex analytical environment. Complex analytical environment can be caused by multiple modality of output variable as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of neural network. Presented methodology has practical value for decision support in business, where information about customer profiles which prefers to buy some product or group products are valuable information for campaign planning and customer portfolio management.

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Villegas-Ch, William, Xavier Palacios-Pacheco, and Sergio Luján-Mora. "A Business Intelligence Framework for Analyzing Educational Data." Sustainability 12, no. 14 (July 17, 2020): 5745. http://dx.doi.org/10.3390/su12145745.

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Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university.

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Barba, Irene, Andrés Jiménez-Ramírez, Manfred Reichert, Carmelo Del Valle, and Barbara Weber. "Flexible runtime support of business processes under rolling planning horizons." Expert Systems with Applications 177 (September 2021): 114857. http://dx.doi.org/10.1016/j.eswa.2021.114857.

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Trisnawarman, Dedi, and Muhammad Choirul Imam. "BUSINESS INTELLIGENCE FRAMEWORK FOR PERFORMANCE MEASUREMENT IN HIGHER EDUCATION STUDY PROGRAMS." Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan 4, no. 2 (October 29, 2020): 249. http://dx.doi.org/10.24912/jmstkik.v4i2.8877.

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Business Intelligence (BI) is an online application and realtime, needed by large and modern organizations to increase competitive advantage in the global competitive environment. Higher Education (HE) is a current organization that houses excellent resources, with the number of students reaching up to tens of thousands, thus requiring an application that can be used as a tool to achieve the organization's business goals. This study aims to build a BI model and framework aimed at developing decision-making applications for measuring the business performance of HE. The method used in this research is the BI development method which is derived from the software engineering development method and adapted to the Study Program Accreditation Instrument (IAPS) 4.0. The case study used is the Information Systems Study Program at Tarumanagara University. The BI development methods are: Business Case Assessment, Enterprise Infrastructure Evaluation, Project Planning, Project Requirements, Data Analysis, Prototyping, Meta Data Analysis, Database Design, ETL Design, Meta Data Design, ETL Development, Application Development, Data Mining, Meta Data Repository Development, Implementation, Release Evaluation. The results of this study are a development stage model and framework that can be used to build BI applications for study program performance measurement.ABSTRAK Business Intelligence (BI) adalah aplikasi daringdan terkini, dibutuhkan oleh organisasi besar dan modern untuk meningkatkan keunggulan kompetitif dalam persaingan global. Perguruan tinggi adalah organisasi saat ini yang memiliki sumber daya yang sangat baik, dengan jumlah mahasiswa mencapai puluhan ribu, sehingga membutuhkan aplikasi yang dapat digunakan sebagai alat untuk mencapai tujuan bisnis organisasi. Penelitian ini bertujuan untuk membangun model dan framework BI yang bertujuan untuk mengembangkan aplikasi pengambilan keputusan untuk mengukur kinerja bisnis PT. Metode yang digunakan dalam penelitian ini adalah metode pengembangan BI yang bersumber dari metode pengembangan rekayasa perangkat lunak dan disesuaikan dengan Instrumen Akreditasi Program Studi (IAPS) 4.0. Studi kasus yang digunakan adalah Program Studi Sistem Informasi Universitas Tarumanagara. Metode pengembangan BI adalah: Business Case Assessment, Enterprise Infrastructure Evaluation, Project Planning, Project Requirements, Data Analysis, Prototyping, Metadata Analysis, Database Design, ETL Design, Metadata Design, ETL Development, Application Development, Data Mining, Metadata Pengembangan Repositori, Implementasi, Evaluasi Rilis. Hasil dari penelitian ini berupa model tahapan pengembangan dan kerangka kerja yang dapat digunakan untuk membangun aplikasi BI untuk pengukuran kinerja program studi. Kata Kunci: Kerangka kerja Business Intelligence; Pengukuran Performa; Perguruan Tinggi

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Aldossari, Showimy, and Umi Asma Mokhtar. "A model to adopt Enterprise Resource Planning (ERP) and Business Intelligence (BI) among saudi SMEs." International Journal of Innovation 8, no. 2 (August 31, 2020): 305–47. http://dx.doi.org/10.5585/iji.v8i2.17395.

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Objective of the study: This paper touched upon the major issues that are related to intention towards the use of ERPBI in the Saudi private sector – Saudi Arabia is an example of a developing countryMethodology / approach: This paper used a qualitative approach and the sample of the study comprised of 30 experts from the Saudi private sector, who were involved in ERPBI.Originality / Relevance: According to prior studies in literature, there is no specific model that supports and explains ERPBI, and as such, this increases the chances of ineffective adoption and use of ERPBI. This study found that ERPBI adoption models are lacking, and those that exist are inapplicable as they exclude several significant factors for successful adoption.Main results: The findings indicated that for the promotion of the ERPBI model, the entire significant factors have to be considered and incorporated. Those factors are: system quality, service quality, and information quality factors, change management, effective communication, training factors, clear vision and planning, competitive pressure, and government role.Theoretical / methodological contributions: This study’s results open door for future research based on the gap in theories applied and the factors investigated in the context of developing nations. Social / management contributions: Shedding light on the factors and their relationship with ERPBI use success would assist top management in minimizing losses through system use for enhanced cash flow, and improved performance of employees and that can eventually bring about the overall performance of the organization.

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Dokhanchi, Atieh, and Eslam Nazemi. "BISC." International Journal of Enterprise Information Systems 11, no. 2 (April 2015): 90–106. http://dx.doi.org/10.4018/ijeis.2015040105.

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Despite the importance and the strategic role of Business Intelligence (BI) in organizations and its key impact on successfully executing corporate strategies, in most cases, strategic planning and performance management projects are done independently from BI projects, therefore a holistic and an integrated framework has been proposed in this paper for aligning Business Intelligence initiatives with corporate strategies. This framework as an interdisciplinary work borrows the approach of the Enterprise Architecture frameworks and its structured logical thinking about the organization and was refined through gathering and analyzing expert's opinions. This paper also uses a case study in one bank with the aim of clarifying the application of the proposed framework.

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