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In January 2013, small biotechnology firm Orexigen was in the final stages of testing Contrave, a promising new pharmaceutical product for the treatment of obesity. At the time, Orexigen had no products in the market, so all its hopes of financial success rested on this new treatment. Contrave had proven to be highly effective in clinical trials, and Orexigen executives were confident it would receive FDA approval.

At the same time, a much larger pharmaceutical company was considering acquiring Orexigen. Because the decision to acquire would ultimately be a financial one, the project team from the large company had to complete a valuation for Orexigen's only significant product in its pipeline, Contrave. What was the new product actually worth?

A consulting team to an international food packaging company (SDYesBox) is attempting to decide which algorithm is the most useful for selecting two national markets in Central America and the Caribbean. SDYesBox wants to work closely with its immediate customers – manufacturers in the dairy and food industry and their customers (retailers) – to develop and market innovative products to low-income consumers in emerging markets; the “next big opportunity for the dairy industry” according to SDYesBox.

New product development and market selection in emerging markets in Latin America.

Five algorithms are “on the table” for assessing 14 countries by 12 performance indicators: weighted-benchmarking each country by the country leader's indicator scores; tallying by ignoring indicator weights and selecting the countries having the greatest number of positive standardized scores; applying a conjunctive and lexicographic combination algorithm; and using a “fluency metric” of how quickly consumers can say each country aloud. At least one member of the consulting team is championing one of these five algorithms. Which algorithm do you recommend? Why?

Learners gain skills, insights, and experience in alternative decision tools for evaluating and selecting choices among emerging markets to enter with new products for low-income (bottom of the pyramid) products ands services.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Towers Watson (TW) has always conducted its own research into alternative approaches to market cap investing. A senior investment consultant with TW, impressed by a recent presentation by the CIO of Research Affiliates (RA) about an innovative investing concept called the “Fundamental Index methodology,” thinks it might be an important innovation in applying nonmarket cap approaches. But he has some concerns about the approach and whether or not it would be appropriate for TW's clients who depend on the firm to keep them on the cutting edge of institutional investing.

Benecol Spread, a cholesterol-lowering margarine, was a product with unusual media-planning challenges. With a narrow target group and unproven market potential, Johnson & Johnson needed to get the most “bang for the buck” from its Benecol advertising. Would a media-planning model (optimizer) requiring executives to quantify their judgment on several key inputs be helpful in this process? A spreadsheet accompanying the case allows students to weight the target groups and to choose among different advertising vehicles to form the best possible media plan.

Greg Mills describes his search for the perfect engagement ring which includes an analysis of the prices of 6,000 diamonds. An engineer, Greg hopes to impress Sarah Staggers by using regression to find an underpriced diamond. Students are asked to either select one of the 6,000 diamonds or provide point forecasts for prices of 3,142 diamonds in a hold-out sample. The instructor can use the actual prices of the held-out diamonds to evaluate student pricing models.

The case evolves around the Powerball lottery and the rule changes implemented in 2015, which, among other things, changed the chances of winning the jackpot from 1 in 175 million to 1 in 292 million. What is the impact of such rules on lottery revenues? The expected value rule is unable to explain why people play in the first place and fails to give the appropriate weight to the factors that explain the attractiveness of a lottery. This case is ideal to introduce the notion of decision weights as put forward by Kahneman and Tversky's prospect theory. By calculating decision weights, we obtain a reasonable prediction for the willingness to pay for the lottery as a function of different jackpot amounts. Using past data, we can correlate lottery revenues with predicted willingness to pay for a ticket. Quantitative-inclined audiences can then develop a simulation model of how likely it is that the jackpot grows, which, coupled with the prediction of revenues as a function of the jackpot, would give the evolution of the revenues under the new rule. The accompanying spreadsheet provides data for students to work out various scenarios to narrow objectives and maximize revenue from Powerball tickets.

It was early 2015 and executives in iShares' Factor Strategies Group were considering the launch of a new class of exchange-traded funds (ETFs) called smart beta funds. Specifically, the group was considering smart beta multifactor ETFs that would provide investors with simultaneous exposure to four fundamental factors that had shown themselves historically to be significant in driving stock returns: the stock market value of a firm, the relative value of a firm's financial position, the quality of a firm's financial position, and the momentum of a firm's stock price. The executives at iShares were unsure whether there would be demand in the marketplace for such multifactor ETFs, since their value added from an investor's portfolio perspective was unknown. Students will act as researchers for iShares' Factor Strategies Group and conduct detailed analysis of Fama and French's five-factor model and the momentum effect, smart beta ETFs including multifactor ETFs, and factor investing with smart beta ETFs to help iShares make its decision.

Alice Monroe was an admissions officer at the Kellogg School of Management at Northwestern University. It was early January 2017 and Alice had enrolled in Northwestern's 403(b) retirement plan two months earlier. After spending a considerable amount of time examining the mutual funds available through the university's retirement plan, Alice had picked two to invest in: a large-cap equity growth fund and a mid-cap equity fund. (See the related case "Selecting Mutual Funds for Retirement Accounts (A).") Her initial allocations were 50% of her investment dollars in each fund.

Upon further reflection, however, she realized these initial allocations were somewhat simplistic. She recalled, from an investments class she had taken at college, the topic of modern portfolio theory, which held that by adding more funds to her portfolio she might be able to achieve greater diversification and thereby reduce the overall risk of her portfolio and/or achieve a higher expected return. Alice now was considering adding an intermediate-term bond fund and a real estate fund to her retirement account.

She hoped to use modern portfolio theory to prove that these new funds would indeed help her diversify her portfolio. If they did, she would also reassess her portfolio weights to determine the optimal allocation.

Addresses how flow times and capacity calculations can be made for a service process such as the Bariatric Surgery Center at a clinic. Highlights how these calculations can be made for a service process just as in any manufacturing setting. Discusses the notions of critical paths and bottlenecks and what factors affect both time and capacity. Also, discusses the relative profitability of two types of bariatric surgery, the goal being to link product profitability to the process.

Marketing management.

Undergraduate/Postgraduate.

The present case is a disguised one and is meant to enable students to critically analyse the operations and functioning of a fitness centre located in a city. Despite a runaway success, “Fit in” was facing problems of time-management and members’ management. Thus, catering to the members was becoming unwieldy, especially with respect to providing the personalized experience to the members, which was their forte. Thus, Mukesh and Naina faced the challenge of motivating their existing members and getting fresh referrals. Should they cut some of their services? Should they make some changes in manpower management? Or, should they change the operating strategy for their business?

Expected learning outcomes are as follows: how to ensure customer loyalty in service organization settings and how to enhance motivation among the service customers.

Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS: 3: Entrepreneurship.