Business Analytics Tools Research Articles

Business analytics and AGI in corporate management systems

The article proposes a classification of tools and stages of business analytics development, extending previously known examples of such classifications. As the highest point of business analytics tools development, AGI is considered. It is substantiated that corporate management systems are the most likely environment in which the first copies of AGI will be created. The process of corporate management systems evolution is considered in conjunction with the time shift between the development of ideas and the practical use of the corresponding class of systems. A forecast of time frames of AGI introduction in these systems is made.

Developing a Dynamic Interactive Strategic Asset Management Plan for Wastewater Facilities using Rapidly Emerging Business Analytics Tools and Techniques – A Case Study at City of Houston

Developing a Dynamic Interactive Strategic Asset Management Plan for Wastewater Facilities using Rapidly Emerging Business Analytics Tools and Techniques – A Case Study at City of Houston

Faeyee: allocation of constrained business resources for production of competitive products from cost accounting perspectives

Synopsis Faeyee Electronics was an electronics company that manufactured and sold smartphones including XS and XT types. The management of Faeyee wanted to pursue the maximum contribution margin as much as possible, especially since they were faced with limited resources. It was necessary to apply analytical tools and cost accounting concepts to study this case including cost-volume-profit analysis, learning curve analysis, regression analysis, definitions of competitive products, constrained non-linear optimization, and contribution margin. Research methodology The case uses business analytics tools and cost accounting concepts, including regression models and constrained optimization approaches, to study how to maximize business outcomes, such as contribution margin and profits when limited business resources are available. The company and individuals are disguised. Relevant courses and levels This case can be used in any junior-, senior-, masters- or MBA-level managerial accounting course. Students need to have at least one course of introduction to statistics or instructors review required statistics concepts or techniques before assigning this case. Students are exposed to the challenges of deriving learning curve models, using regression analysis to study collected data and allocating limited resources to maximize contribution margin.

Improvement of feedlot operations through statistical learning and business analytics tools

A decision-support, modeling tool is developed that can project future cattle growth patterns in a feedlot based on a low dimensionality dataset available at the start of the feeding process. This work adapts the predictive performance of two well-known statistical machine modeling tools, gradient boosting and random forest regression, to predict future cattle growth. Time series analysis techniques are then used to create an ensemble method that further improves prediction accuracy from individual modeling outputs. Hierarchical clustering techniques are used to leverage projected growth patterns to increase group homogeneity when assigning cattle to different feeding pens. Finally, a profit maximization method is developed that estimates the optimal time each individual cattle should remain in the system under different revenue and cost estimates.The purpose of this work is to incentivize the implementation of modern statistical learning tools in cattle management operations, especially within low-to-mid scale operations that traditionally rely on the expertise of its workers and have limited cattle and process information. Access to ‘off-the-shelf’ statistical learning tools, requiring minimal user-interaction, not only enhances prediction accuracy but helps automate operational decisions. This results in higher process efficiencies and improved standardization practices, while also helping identify profit opportunities. Finally, integrating these components into a single operating framework allows the tool to adapt to changes in data characteristics, which is especially important within non-standardized processes. We show the application of this tool through a case study implementation on a mid-scale operation in the northwestern state of Sonora, Mexico. From our case-study results, it was found that the modeling tool can satisfactorily predict growth patterns based on a low-dimensional set. It also can also capture historic decision-making when segmenting cattle into homogenous groups during their feeding process. Furthermore, it can help identify profit opportunities when estimating optimal cattle system times under varying market conditions.

Analysis of the Fit of Learning and Management Systems in Higher Education Institutions: A Case Study from Saudi Arabia

Higher Education Institutions (HEIs) are heavily investing in learning and management systems tosupport and enhance the quality of services provided to key stakeholders; namely, the students. However, the degreeof fit of such systems with the needs of students varies significantly from one higher education institution to another.Therefore, continuous assessment of this kind of fit has to be undertaken by HEIs to ensure that these systems arecontinuously adding business value and maintaining appropriate level of return on investments. The problematic issueis that there is a scarcity of business and Infromation Technology (IT) alignment evaluation research or tool that isdedicated to this purpose in this sector. This motivates this ongoing research to be undertaken devising a theoreticalframework as an artefact that incorporates set of business analytics tools to facilitate the evaluation of the businessvalue from IT. The Design Science Research is adopted as the research methodology in this research to guide thedevelopment process of this artefact. This paper, however, focuses mainly on providing background information aboutthe issue of business and IT alignment in general and in the higher education sector in particular. It then describes thealignment influencing factors that impact on the alignment between business and IT. Through a real life case studyfrom a higher education institution in Saudi Arabia, the paper aims to present the IT alignment issue in this sector, and also discuss some of its influencing factors to illustrate how they influence the degree of fit between business and IT.

Business intelligence serious game participatory development: lessons from ERPsim for big data

PurposeA major trend in enterprise resource planning software (ERP) is to embed business analytics tools within user-centered roles in enterprise software. This integration allows business users to get better and faster insight to action. As a consequence, it is imperative for business students to learn how to use these new tools to adequately prepare them for new expectations in the industry. The paper aims to discuss these issues.Design/methodology/approachIn this paper, the authors propose a new serious game, called ERPsim for big data, to enable the learner to acquire abilities at each level of the business analytics learning taxonomy. To maximize the pedagogical impact of the game, participatory design (PD) with professors as co-designers was used during game development.FindingsThis case study presents the PD approach and analyses the efficacy of the proposed new simulation.Originality/valueThe authors conclude by providing recommendations and lessons learned from this approach.

Framework for a Hospitality Big Data Warehouse

In order to increase the hotel's competitiveness, to maximize its revenue, to meliorate its online reputation and improve customer relationship, the information about the hotel's business has to be managed by adequate information systems (IS). Those IS should be capable of returning knowledge from a necessarily large quantity of information, anticipating and influencing the consumer's behaviour. One way to manage the information is to develop a Big Data Warehouse (BDW), which includes information from internal sources (e.g., Data Warehouse) and external sources (e.g., competitive set and customers' opinions). This paper presents a framework for a Hospitality Big Data Warehouse (HBDW). The framework includes a (1) Web crawler that periodically accesses targeted websites to automatically extract information from them, and a (2) data model to organize and consolidate the collected data into a HBDW. Additionally, the usefulness of this HBDW to the development of the business analytical tools is discussed, keeping in mind the implementation of the business intelligence (BI) concepts.

Visualization of Twitter Data in the Classroom

ABSTRACTThe expression “big data” is ubiquitous in the business world today, but few undergraduate business students have the opportunity to gain practical experience with how new business analytics tools can be used in decision making. This article describes a set of hands‐on labs that prepare students to incorporate streaming data analysis into group research projects. Splunk is used to help students analyze and visualize streaming social media data. An evaluation of student projects and student survey results show that this practical approach of training students to manipulate and visualize big data was largely successful in achieving instructional goals.

Quantitative 4D analysis using business analytics techniques

A Reservoir Data Warehouse was successfully created where key subsurface information used for geophysical reservoir monitoring has been transformed, integrated, and stored into a geospatially registered relational database. Quantitative 4D analysis was performed on the data, utilising the power of the parallel processing architecture of the high-performance analytical database, and combining traditional 4D analysis and business analytics tools. The 4D analysis successfully demonstrated quantitative relationships between the different data. At its simplest the Reservoir Data Warehouse enables a more sustainable and easily accessible solution for data integration and co-visualization, and simplification in the data management realm. At its most powerful the Reservoir Data Warehouse will enable the creation of new subsurface workflows where large amounts of data from disparate origins can be analysed jointly and presented in existing software solutions, or in newly developed cross-disciplinary software solutions.

Business Analytics: Opportunities for Small and Medium Enterprises

Business Analytics is a most trendy technology for business organization. In information technological world organizations used to predict the future events or decision making based on the past records or existing information. For that, Business Analytical Tools are very essential to guide and support for the executives. Not only large scale or multi-national companies, even small and medium size enterprises also can utilize these business analytics tools for their performance improvement. In this conceptual article discusses the different types of business analytics models which can be utilized for taking decision and it brief about the difference between business intelligence and business analytics. Also, this paper discusses about the various types of business analytics in the different domains. The overall purpose of the organization is to attain the goals and objectives and to make profit. It can be achieved if the company is used to analyze their business process through business analytics.

Call for Papers—Special Issue of Management Science: Business Analytics

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeManagement ScienceVol. 58, No. 9 Call for Papers—Special Issue of Management Science: Business AnalyticsSubmission deadline: September 16, 2012 Expected publication date: First Quarter 2014Dimitris Bertsimas, Eric Bradlow, Noah Gans, Alok GuptaDimitris Bertsimas, Eric Bradlow, Noah Gans, Alok GuptaPublished Online:1 Sep 2012https://doi.org/10.1287/mnsc.1120.1637"Call for Papers—Special Issue of Management Science: Business Analytics." Management Science, 58(9), p. 1777 Previous Back to Top FiguresReferencesRelatedInformation Volume 58, Issue 9September 2012Pages iv-1777 Article Information Metrics Downloaded 53 times in the past 12 months Information Published Online:September 01, 2012 Copyright © 2012, INFORMSCite asDimitris Bertsimas, Eric Bradlow, Noah Gans, Alok Gupta, (2012) Call for Papers—Special Issue of Management Science: Business Analytics. Management Science 58(9):1777-1777. https://doi.org/10.1287/mnsc.1120.1637 PDF download

Call for Papers—Special Issue of Management Science: Business Analytics

Organizations today have access to enormous data sets, and sophisticated business analytical tools are needed to harness the tremendous potential of these data to improve day-to-day decision making. To encourage further developments in this rapidly growing area of interdisciplinary research, Management Science will publish a special issue dedicated to business analytics. Applications of business analytics extend to nearly all managerial functions in an organization. Using microlevel data, mathematical models can become more realistic, be validated more easily, and match more closely true behavioral phenomena. Through business analytics, historical data can be used to discover relationships among important elements in an organization's environment, make more formal statistical inferences regarding the structure or strength of these relationships, and improve actions taken by the organization. Because these relationships are often not static, the entire cycle of identification, estimation, prediction, and decision making is repeated frequently and on an ongoing basis, leading to real-time and highly targeted data-driven decisions. We envision business analytics applied to many domains, including, but surely not limited to: digital market design and operation; network and social-graph analysis; pricing and revenue management; targeted marketing and customer relationship management; fraud and security; sports and entertainment; retailing to healthcare to financial services to many other industries. We seek novel modeling and empirical work which includes, among others, probability modeling, structural empirical models, and/or optimization methods. Submission: Please submit your manuscript online via http://mc.manuscriptcentral.com/ms You must select "Special Issue" as the Manuscript Type in Step 1 and select one of the issue's four coeditors in Step 5. Manuscripts will generally be assigned to one of the Associate Editors for this issue (see below; please recommend three in Step 4), but you may also recommend guest Associate Editors.

Call for Papers—Special Issue of Management Science: Business Analytics

Organizations today have access to enormous data sets, and sophisticated business analytical tools are needed to harness the tremendous potential of these data to improve day-to-day decision making...

Call for Papers—Special Issue of Management Science: Business Analytics

Organizations today have access to enormous data sets, and sophisticated business analytical tools are needed to harness the tremendous potential of these data to improve day-to-day decision making. To encourage further developments in this rapidly growing area of interdisciplinary research, Management Science will publish a special issue dedicated to business analytics. Applications of business analytics extend to nearly all managerial functions in an organization. Using microlevel data, mathematical models can become more realistic, be validated more easily, and match more closely true behavioral phenomena. Through business analytics, historical data can be used to discover relationships among important elements in an organization's environment, make more formal statistical inferences regarding the structure or strength of these relationships, and improve actions taken by the organization. Because these relationships are often not static, the entire cycle of identification, estimation, prediction, and d...

Nestlé Improves Its Financial Reporting with Management Science

Nestlé’s executive information system (EIS) department gathers data from the firm’s subsidiaries (reporting units) to provide top management with operational, financial, and strategic information. In 1996, the EIS department decided to improve its service by using business analytics tools based on management science (MS) techniques. It wanted to encourage analysts and controllers to make better use of the information supplied. We developed four OR modules: sensitivity analysis, forecasting, simulation, and optimization, and integrated them into a more global modeling scheme for evaluating the economic profitability of Nestlé’s projects and more generally evaluating the value of the Nestlé group and its multifocal businesses. Disseminating this approach within the Nestlé group through training and internal consulting has been a long and important process that has increased the number of managers accustomed to quantitative decision making and established new reporting protocols imposing the use of MS models.