Toward a Model Undergraduate Curriculum for the Emerging Business Intelligence and Analytics Discipline

Business intelligence (BI) combined with business analytics (BA) is an increasingly prominent strategic objective for many organizations. As a pedagogical subject, BI/BA is still in its infancy, and, in order for this to mature, we need to develop an undergraduate model BI/BA curriculum. BI/BA as an academic domain is emerging as a hybrid of disciplines, including information systems, statistics, management science, artificial intelligence, computer science, and business practice/theory. Based on IS 2010’s model curriculum constructs (Topi et al., 2010), we explore two curricular options: a BI/BA concentration in a typical IS major and a comprehensive, integrated BI/BA undergraduate major. In support, we present evidence of industry need for BI/BA, review the current state of BI/BA education, and compare anticipated requirements for BI/BA curricula with the IS 2010 model curriculum. For this initial phase of curricular design, we postulate a preliminary set of knowledge areas relevant for BI/BA pedagogy in a multi-disciplinary framework. Then we discuss avenues for integrating these knowledge areas to develop professionally prepared BI/BA specializations at the undergraduate level. We also examine implications for both AACSB and ABET accreditation and describe the next phase of applying the IS 2010 concept structure to BI/BA curriculum development

IS Faculty Engagement in Pedagogical Research

There is growing interest in pedagogical research in response to demands for accountability in higher education. Major accrediting bodies have also recognized its role in advancing the practice of teaching, with the goal of improving student learning. In this session, we will highlight the benefits of involvement in IS educational research across the full spectrum from reader to reviewer, author, and editorial board member. We will survey publication outlets for IS pedagogy in a range of genres. Guidelines for planning, implementing, and writing up instructional projects will also be discussed

Cellular vs process layouts: an analytic investigation of the impact of learning on shop performance

Evidence from the literature on cellular manufacturing suggests that shops configured as manufacturing cells perform poorly compared to job shops. However, cellular shops, being conducive to the use of teams in the assignment of production activities, have the potential to yield higher productivity than a job shop. Productivity differentials, and in particular, differences in the rates at which processing times can be reduced, have been largely overlooked in prior comparisons of cellular and job shops. This paper uses queuing theory to illustrate the relationship between processing time learning rates and flow time performance in cellular and job shops. Models are developed that make it possible to estimate the learning rate required in a cellular shop in order for it to yield performance comparable to that of a job shop. Simulation is used to validate the models under dynamic conditions as opposed to the steady state conditions assumed by queuing theory. Results indicate that a cellular shop need only achieve a marginally higher learning rate than a job shop in order to perform at a comparable level.Cellular manufacturing Group technology Learning curve Queuing theory Simulation

AN EXCEL-BASED DECISION SUPPORT SYSTEM FOR SCORING AND RANKING PROPOSED R&D PROJECTS

One of the most challenging aspects of technology management is the selection of research and development (R&D) projects from among a group of proposals. This paper introduces an interactive, user-friendly decision support system for evaluating and ranking R&D projects and demonstrates its application on an example R&D program. It employs the scoring methodology developed by Henriksen and Traynor to provide a practical technique that considers both project merit and project cost in the evaluation process, while explicitly accounting for trade-offs among multiple decision criteria.1 The framework of the Excel-based system, PScore, is presented with an emphasis on the potential benefits of using this methodology with computer-automated extensions that facilitate and enhance managerial review and decision-making capabilities.R&D project evaluation, R&D project selection, scoring, ranking, interactive decision support

Utilizing and teaching data tools in Excel for exploratory analysis

In this article we offer Excel as an introductory tool to high end business intelligence (BI) and decision support system (DSS) applications. Because it is ubiquitous, Excel can be used by all managers and business students for exploratory data analysis. We provide three key points in this utilization of Microsoft® Excel 2003: (1) manipulating records using Excel as a database, (2) creating PivotTables® and PivotCharts® using Excel for analysis, and (3) importing data using Excel as an automation container. The basic skill set defined by the above three items allows users to begin to use Excel to its full potential in finding information in business data, and it offers a key tool for future research in improving the utilization of information across organizations.Excel data tools Pivot tables Data analysis Quantitative business education Exploratory data analysis Pivot charts

An Optimization Model for Planning Natural Gas Purchases, Transportation, Storage and Deliverability

Natural gas local distribution companies (LDCs) face the problem of managing natural gas purchases under conditions of uncertain demand and frequent price change. In this paper, we present a stochastic optimization model to solve this problem. Unlike other models, this model explicitly considers deliverability, the rate at which gas can be added to and withdrawn from a storage facility, as a variable, and considers its role in ensuring a secure supply of gas. Deliverability is often overlooked in gas supply planning, yet is a critical factor in achieving a secure gas supply. Using data from an LDC in Huntsville, Alabama, we show how this model can be used to minimize total cost while meeting constraints regarding the security of gas supply. We also demonstrate that security is dependent on the rate of deliverability, which in turn is affected by a number of factors including gas availability, storage and transportation considerations, and weather conditions