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In this, the second of a two-case decision analysis series, an entrepreneur must decide whether there are additional benefits from operating in more than one U.S. city. He explores the business model of operating local production facilities in major cities, metro areas with a population of over one million. Going national in year three would allow for a year four launch. Again, the entrepreneur must choose between a discounted and a premium strategy. He must also consider how the U.S. national florists might react to whatever strategy he chooses. Student spreadsheets are available for both the A and the B cases.

Spreadsheet for case UV6293

In this, the second of a two-case decision analysis series, an entrepreneur must decide whether there are additional benefits from operating in more than one U.S. city. He explores the business model of operating local production facilities in major cities, metro areas with a population of over one million. Going national in year three would allow for a year four launch. Again, the entrepreneur must choose between a discounted and a premium strategy. He must also consider how the U.S. national florists might react to whatever strategy he chooses. Student spreadsheets are available for both the A and the B cases.

This case was awarded first place in the INFORMS 2011 case competition. A seasoned Internet retailing entrepreneur considers starting a national floral delivery service to compete with the major U.S. players. Decisions include whether to open a production facility in a single city and begin to deliver there? Which strategy should he use to decide-a premium or a discounted strategy? Ideal for MBA and undergraduate students in decision analysis modules or courses, this case offers an optional student spreadsheet useful for considering different strategies and calculating the resulting net present value of the opportunity. A B case with a spreadsheet is also available.

Spreadsheet Supplement for UV1094.

Spreadsheet for case UV1474

This case is suitable for graduate-level quantitative analysis, business and government, environment and sustainability, and global economics courses. Students must consider the tradeoffs between continuing to run an old coal-burning plant and purchasing emissions allowances (EAs) versus upgrading to emissions-reducing wet or dry scrubbers. Reducing emissions creates the possibility of selling the plant's surplus EAs (which are likely to increase in price). Choosing a wet or dry scrubber requires considering installation cost and construction time, variable cost, and SO2 removal efficiency. Ideally, the investment should pay back over time, but management believes some net investment could also be justified. For that, however, complete analyses from both economic and environmental perspectives are required. A supplemental spreadsheet is available to accompany the case (UVA-S-QA-0726).

In the early months of the 2007-08 financial crisis, a loan manager faces a real estate financing decision. Should he approve a bullet structure three-year loan to a longstanding client, a legendary Texan developer? The developer, who near retirement downsized his business, is seeking financing for his only project: residential or commercial development on an attractive piece of land in suburban Houston. The loan manager considers the decision in light of the mortgage market turmoil, seeing commercial projects as safer, but also factoring that the residential market could bring higher returns if the market stabilizes soon. The manager collects the data and asks an analyst to assess the risks; that ultimately requires assessing the economics of both projects from both the bank's and the developer's perspectives. The bank could still change the interest rate on the loan to receive adequate compensation for the risk it carries, but the loan manager knows that doing so will change their long-term client willingness to take on the loan.

This case provides an extension of the A case (UVA-QA-0709) by including two types of owners' requests that were omitted in the A case for simplicity. Solving it requires knowledge of integer programming and how it can be used to reinforce logical conditions. Could be used as an assignment or part of an exam. A supplemental Excel spreadsheet is available.

This award-winning case series examines how optimization-based decision-making leads to a new business design for a timeshare exchange: an exchange fair. Students model a complex managerial problem as a linear program, identify the types of data needed, and use models to provide decision support. The series covers linear and integer programming, multiple-criteria decision-making, network flow problems, local search heuristics, and Pareto-optimality. The series demonstrates the advantages and difficulties involved in applying management science techniques to a real-life business environment. It teaches standard techniques of extracting relevant data from a mass of more- and less-relevant information and forces students to confront issues consultants face: How do you motivate management to even consider a management science approach? How do you build a "business case" demonstrating the shortcomings of the methodology already in place? The case provides background on the timeshare industry, exchanges, and the exchange fair, in which owners submit their existing intervals to a centralized pool, together with requests for the intervals they want to exchange; the exchange determines who gets which interval. Various aspects of a functioning exchange fair are presented, and, starting from a heuristic, the series effectively guides students toward the "better" optimization-based way of executing the exchange. This case series is a winner of the 2005 INFORMS case competition and received an honorable mention at the 2005 DSI case competition. Suitable for both general MBA and executive education programs and as a basis for an exam or assignment, this series is also appropriate for specialized undergraduate and graduate programs in services management (e.g., tourism and hotel administration), logistics, and industrial and systems engineering.