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The organizers of a music festival may use video from the Friday concert to create a DVD to sell to those who come to the Saturday concert. Attendance on Saturday is uncertain, as is the percentage of those who attend on Saturday who will buy the DVD. Is this a good project? If so, what number of DVDs should be burned early Saturday morning and offered for sale at that evening’s performance? By that time, Friday attendance is known, as well as whether it rained on Friday, and there is a forecast for whether it will rain on Saturday. Historical information on these variables may help us predict Saturday attendance using multiple regression; together with the results of a marketing survey, such analysis will help us make better purchasing decisions. This case series (see also the B case, UVA-QA-0708) can be used to illuminate a multitude of concepts that are covered in basic decision-analysis courses. The series starts by examining the role of uncertainty in decision-making, proceeds through the estimation of probability distributions from sample data with multiple regression, culminates in the development of a full decision model, and ends with a qualitative and quantitative analysis (with a tornado diagram) of how to add value and reduce risk. Key pitfalls for students are failing to recognize both limits on sales (supply and demand), incomplete reasoning in the determination of the attendance probability distribution, and oversimplifying the full forecast model.

This chapter demonstrates that social business models do not meaningfully prioritize or impose accountability to “social good” over other purposes in ways that (a) best protect against owners changing their minds or entry of new owners with different priorities and (b) enable reliable accountability over time and across circumstances. This chapter further suggests a model – a “social primacy company” – that actually prioritizes “social good” and meaningful accountability to it. This chapter thus clarifies circumstances under which existing models might be most useful and are not particularly useful, especially as investors, entrepreneurs, employees, regulators, and others pursue shared, common understandings about purposes, priorities, and accountability.

This case builds on the case "Merck & Company: Product KL-798" (UVA-QA-0582) by providing market uncertainties for the drug (drug quality, the presence of a competitor, market growth, and the time to the drug's release). Student and faculty spreadsheets are provided for the calculation of net present values for the scenarios. There is an additional challenge of how to treat the several downstream decisions (using OptQuest, for example) and how to value the license opportunity. A teaching note is also available to registered faculty members.

A credit-card company must value portfolios of customers based on their future earnings. The payment characteristics of customers serve to classify them into states. This case can be the basis for discussing state dynamics over time in a Markov process.

A regional director of a consulting firm must decide how to compete for a major consulting contract. Appshop can take a level payment contract, a lower level payment with a prospective bonus given high performance, or bid on an RFP where a significant reward is given contingent on the client's savings. The case can be used to introduce Monte Carlo simulation modeling and to build skills in structuring a decision tree in a spreadsheet. It affords an opportunity to simulate alternative risk profiles and to discuss their comparative advantages.

The managing director of a steel plant faces the decision of how much of each raw material to order for the plant for the following month. Due to lower and upper bounds on the amounts of each raw material in a batch and varying amounts of electricity and time consumed for different raw materials, one can't simply use the cheapest raw material. A linear program and the solver optimization function of Excel will provide the optimal amounts that meet the constraints. Interestingly, the best mixture for a batch is not the best mixture for a monthly plan. Shadow prices indicate the value of relaxing constraints. The typical monthly model from a student will be nonlinear, although it can be written as a linear model. This case provides the basis for an introductory class on linear programming and linear versus nonlinear models.

An angel/venture capitalist could invest in an Internet sheet-music publishing start-up. The chance of success multiplied by the value, if successful, suggests that this isn't a good investment. Nevertheless, several friends suggest the optionality present in the venture: abort an unsuccessful website and sell the technology; switch the technology if the website is good, expand, buyout. Decision trees and Monte Carlo simulations are used to value these options, which make the opportunity look very attractive.

Jane Barrow, CEO of Caprica Energy, must recommend to the board which of three potential “unconventional ” natural-gas development sites in different parts of the United States the company should pursue. The case takes place in January 2011, when the “low-hanging fruit ” of natural-gas production in the United States had essentially been picked. All three of the potential sites (shale, coalbed methane, and tight sands) would require hydraulic fracturing, a process of removing gas that was formerly considered inaccessible by injecting water and chemicals into the ground. Because of emerging concerns about the potential harm “fracking ” can do to drinking water, Barrow must not only analyze which site might be most profitable but also what the potential risks to the environment and area residents might be.

A small start-up company must make additional investments to maximize its firm value. But the company owner will not make this investment unless she can renegotiate outstanding debt claims. Solving this “debt overhang” problem through negotiation is the focus of the case. In this context, students are exposed to a variety of issues: the nature of financial claims, bargaining and negotiation fundamentals, and agency costs of debt.