John C. Yi

Business Plus Intelligence Plus Technology Equals Business Intelligence

In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

An expert system to physician-detailing planning

Abstract In the pharmaceutical industry, the main components in determining physician-detailing plans are strategic physician-population segmentation, data driven sales-response functions of physician prescribing behavior, and an optimization algorithm for sales call allocation. Using data mining technology in neural networks coupled with non-linear programming, this paper proposes an expert system to effectively allocate sales-force resources for single-product physician-detailing planning. The result shows that this adaptive and easy-to-implement system helps decision makers to outperform the standard industry practice 1 by 10% in profit gain.

using Business Intelligence in college Admissions: A Strategic Approach

Higher education often lags behind industry in the adoption of new or emerging technologies. As competition increases among colleges and universities for a diminishing supply of prospective students, the need to adopt the principles of business intelligence becomes increasingly more important. Data from first-year enrolling students for the 2006-2008 fall terms at a private, master’s-level institution in the northeastern United States was analyzed for the purpose of developing predictive models. A decision tree analysis, a neural network analysis, and a multiple regression analysis were conducted to predict each student’s grade point average (GPA) at the end of the first year of academic study. Numerous geodemographic variables were analyzed to develop the models to predict the target variable. The overall performance of the models developed in the analysis was evaluated by using the average square error (ASE). The three models had similar ASE values, which indicated that any of the models could be used for the intended purpose. Suggestions for future analysis include expansion of the scope of the study to include more student-centric variables and to evaluate GPA at other student levels.

Markdown optimization at multiple stores

This article studies a markdown optimization problem commonly faced by many large retailers that involves joint decisions on inventory allocation and markdown pricing at multiple stores subject to various business rules. At the beginning of the markdown planning horizon, there is a certain amount of inventory of a product at a warehouse that needs to be allocated to many retail stores served by the warehouse over the planning horizon. In the same time, a markdown pricing scheme needs to be determined for each store over the planning horizon. A number of business rules for inventory allocation and markdown prices at the stores must be satisfied. The retailer does not have a complete knowledge about the probability distribution of the demand at a given store in a given time period. The retailer’s knowledge about the demand distributions improves over time as new information becomes available. Hence, the retailer employs a rolling horizon approach where the problem is re-solved at the beginning of each period by incorporating the latest demand information. It is shown that the problem involved at the beginning of each period is NP-hard even if the demand functions are deterministic and there is only a single store or a single time period. Thus, attention is focused on heuristic solution approaches. The stochastic demand is modeled using discrete demand scenarios based on the retailer’s latest knowledge about the demand distributions. This enables possible demand correlations to be modeled across different time periods. The problem involved at the beginning of each period is formulated as a mixed-integer program with demand scenarios and it is solved using a Lagrangian relaxation – based decomposition approach. The approach is implimented on a rolling horizon basis and it is compared with several commonly used benchmark approaches in practice. An extensive set of computational experiments is perfomed under various practical situations, and it is demonstrated that the proposed approach significantly outperforms the benchmark approaches. A number of managerial insights are derived about the impact of business rules and price sensitivity of individual stores on the total expected revenue and on the optimal inventory allocation and pricing decisions.

Knowledge-based approach to improving micromarketing decisions in a data-challenged environment

With the growing popularity of micromarketing strategies, more companies are tailoring their marketing efforts to meet individual consumer need based on analysis of consumer-level data. However, there are two major obstacles in the way to effective micromarketing strategies: (1) data limitation at the consumer-level making it difficult to construct robust and accurate promotional response function and (2) misplaced usage of a resource allocation approach effective in a data-rich environment to data-challenged environment. These obstacles lead to suboptimal marketing resource allocation decisions. This paper presents a knowledge-based approach specifically designed for effectively allocating marketing resources in the micromarketing environment. A team of domain experts provide knowledge in refining and imputing data to overcome data limitation issues, as well as identifying consumer-level constraints to strengthen the integer programming models performance. Results indicate that this approach is transparent to all levels of management, adaptable to changes in environment, and easy to implement. In addition, there is tremendous potential for improving not only profitability but also growing the companys intellectual capital. When this approach is applied to a random sales territory, it outperforms the traditional method by more than 19% in profit.

An Analysis of the Use of Predictive Modeling with Business Intelligence Systems for Exploration of Precious Metals Using Biogeochemical Data

This study addresses the use of predictive modeling techniques; primarily feed-forward artificial neural networks as a tool for forecasting geological exploration targets for gold prospecting. It also provides evidence of effectiveness of using Business Intelligence systems to model pathfinder variables, anomaly detection, and forecasting to locate potential exploration sites for precious metals. The results indicate that the use of advanced Business Intelligence systems can be of extremely high value to the extractive minerals exploration industry.

Analyzing the Effectiveness of Pharmaceutical Marketing Using Business Intelligence Methods

Pharmaceutical companies have traditionally marketed their products through a combination of several channels: sales details to physicians, direct-to-consumer advertising, professional medical journal advertising, sponsorship of meetings and events and e-promotion. With an impending patent cliff and subsequent loss in revenue, the industry must depend on, among many factors, recently launched products to offset the revenue loss. Coupled with increased generic competition, companies must evaluate the return on investment of their marketing dollars. This paper analyzes the effectiveness of traditional marketing methods, both industry-wide and for recently launched products, using the latest Business Intelligent methods. The dataset used in this paper is a sample of prescription, promotional, competitive, and product data from SDI Health. The analysis in this paper reveals that traditional marketing methods have a decreasing level of impact with the number of prescriptions dispensed, and describes new potential channels for marketing, as well as collecting and analyzing data to aid the industry improve its resource utilization.