Getting on board: the business case for improving on-campus transportation at NAU. Issue 5 (11th September 2017) - Anderson, Joe; Hilliard, James I.; Williams, Josh; Williams, Susan K.
Abstract : Synopsis: Josh Williams is a Student at the NAU who has driven buses on campus and wants to improve the transportation on campus. He is convinced that purchasing a new type of bus that is more fuel efficient, has larger capacity, better designed for boarding, and has a longer life is worth the higher purchase cost. He sets out to prove it by creating a discounted cash flow (DCF) analysis. Since many of the estimates for the DCF analysis are uncertain, he decides to perform a Monte Carlo simulation (MCS) analysis. Students are asked to step into Josh's role and perform the analysis. Research methodology: Josh Williams was a Student in the authors' MBA program. Both authors teach in this program and one author was the Advisor for Net Impact and worked with Josh to present his idea to the university administration. The authors have changed a name or two but otherwise, the case describes a real situation in a real organization without disguise. Relevant courses and levels: The authors have used this case in a first semester MBA-Applied Management course, Decision Modeling and Simulation. Students already have experience with DCF analysis and have been introduced to MCS. With this case, students apply MCS at the conclusion of a three-week module on predictive analytics. Students have run at least two MCS models and have become comfortable with the software. The case would also be appropriate for a senior-level undergraduate course such as business analytics or management science. It might also be useful for other courses that include the MCS modeling technique learning objectives such as project management. Theoretical bases: This case provides an opportunity for students to perform an MCS analysis. MCS is useful when many of the inputs to a DCF analysis (or any model) have been estimated and the modeler is concerned that the estimates are uncertain and could perhaps be a range of values. MCS can be used to understand the effect of this uncertainty on NPV which in turn may affect the decision. The case could also be used without MCS focusing just on the DCF analysis with deterministic sensitivity analysis.