An analytics-based decision support system for evaluating the fiscal health of academic programs

Academia’s business model is significantly different than most other businesses. The underlying fiscal unit is the academic program. The fiscal model is complex when balancing full-time students attending with flat-rate tuition or per credit and faculty members who may teach in multiple programs and may have other administrative or scholarship duties. This study presents an analytics-based Decision Support System (DSS) for academic programming and fiscal health. The proposed model computes, without averaging, the operating/direct contribution margin of academic programs. The model uses descriptive analytics to provide information for predictive analytics - such as developing business cases for the new program(s), diagnostic analytics for sensitivity analysis - such as a “what if” analysis of program data and/or a faculty hiring rubric, and prescriptive analytics - such as interactive forecasting for choosing the best alternative courses of action. The model takes the guesswork out and allows academic officers to convert data into knowledge and insight for effective decision-making and academic programming.