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The decision maker is in charge of procurement auctions at the department of transportation of Orangia (a fictitious U.S. state). Students are asked to assist him in estimating the winning bids in various auctions concerning highway repair jobs using data on past auctions. The decision maker is faced with various professional, statistical, and ethical dilemmas.

To analyze highway procurement auctions from the buyer-auctioneer perspective, establish basic facts regarding the project price-to-estimated cost ratio, set up and estimate a structural regression model to predict the winning bid, and compute the probability the winning price will be below estimated cost. Difficulties include heteroskedasticity, logarithmic specification, and omitted variable bias. Also to estimate a Logit regression and predict bidder collusion probability.

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 financial success of dairy farms depends critically on the price of their main output, milk. Large volatility in the price of milk poses a considerable business risk to dairy farms. This is particularly true for family-run dairy farms. The question then arises: how can a farm owner hedge the milk price risk? The standard approach to establish a price floor for a commodity such as milk is to purchase put options on commodity futures. At the Chicago Mercantile Exchange, farmers can buy put options on the price of a variety of milk products. However, the price a farm receives for its milk depends on many factors and is unique to the farm. Thus, a farmer cannot directly buy put options on the price he receives for the milk his farm produces. Instead the farmer needs to determine which of the options available for trade at the Chicago Mercantile Exchange offer the best hedge for his own milk price. The assignment in this case is to examine historical data on several prices of milk products and the milk price received by a family-run dairy farm in California. Students need to find the price that is most closely correlated to the farm's milk price and to then choose options with the appropriate strike price that serve as the best hedge for the farm's price risk.

The objective is to expose students to an interesting but simple finance application of linear regression analysis. To solve the case, students must run several simple linear regressions, then use the best regression model they find to make a prediction for the dependent price variable and analyze the prediction interval in order to achieve the desired objective outlined in the case. By completing the case, students will acquire a good understanding of their regression model and its usefulness.

This case study intends to add knowledge and understanding of supply chain management particularly with respect to international logistics.

The case study can be used in both undergraduate and postgraduate levels. Students pursuing Master of Science in Logistics, Supply Chain Management and those doing bachelor degrees in the same areas can have a better insight and special interest of the case. Professional boards may also use the case to empirically make students understand this area.

The railway sub-sector in East Africa – Tanzania in particular – is an important transport mode but has a declining performance. The market share is estimated at only 4 percent of the freight market. Still knowledge about traffic, particularly for freight, is scant. The main dilemma is whether traffic of the central corridor is more intra- or inter-Tanzania. The case studies techniques appropriate for meaningful traffic forecasting and through a simple regression model it resolves the freight conflicts between Kenya rail and the Central Corridor. It provides students with applied traffic forecasting tools.

The case focuses on techniques of traffic forecasting, development of traffic scenarios and on issues related to intermodal transport especially between road, rail and ocean. At the end of using this Case students should be able to: explain the methods, techniques and models used in traffic forecasting; understand intermodal linkages in international Logistics; use different approaches to make logistics market assessment; and forecast traffic in all modes using different scenarios.

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

Nicolas Dupont, the owner of Chateau de Montana, a struggling (and old) boutique hotel in Crans-Montana Ski Resort, Switzerland, wished to renovate and reposition his family-owned hotel to target higher room rates. Dupont commissioned Olga Mitireva and Yulia Belopilskaya as consultants to assess the proposition. The consultants had to extract cues for the room rate of the repositioned hotel from comparable hotels. However, the room rates varied significantly across similar hotels due to their differing characteristics and locations. It was a cognitive challenge to read the patterns from a few comparable hotels. They collected the data of 200 hotels from similar locations and simulated room prices using hedonic regression models.

Data used to develop the case included primary data from employees and supervisors of a commercial floorcovering manufacturing plant in Northwest Georgia. The case company is not disguised.

The survey was developed using existing instruments from the Organizational Behavior and Human Resources Literature. Instruments were listed in Exhibits 2 through 7. The survey administration had the support of the Vice President for Resources and Facilities, and employees and their supervisors were given time to complete the surveys. The data gathered was analyzed by the researcher using SPSS statistical software.

Established in 1957, J&J started as a family-owned business but had grown and diversified its product offerings by focusing on commercial flooring. It survived several economic downturns and remained competitive in a market dominated by more prominent flooring manufacturers. J&J Industries strived to empower its 800 employees with various incentive programs. Employees remained loyal to J&J; many had worked for the company for over 15 years. However, management wanted to measure the impact of empowering and initiatives on employee performance and satisfaction to determine the real power of employee incentive programs. The Resources and Facilities Vice President employed Professor Lopez, a Management Professor, to develop a survey to measure these constructs and analyze the data to guide future incentive programs. Data from the employee and supervisor survey was provided along with the statistical analysis results for interpretation and recommendations for VP Fordham.

The target audience for this case is primarily students in a research methodology course and students studying quantitative regression analysis and interpretation. The focus is predominantly on graduate-level students in Master of Business Administration or Master of Accounting programs in business. Graduate students should have completed courses in management or organizational behavior, business statistics or quantitative methods or data visualization and cleaning as background knowledge for this case. Specifically, students should understand regression analysis and know when and how the tool is used for managerial decision-making.

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.

This disguised case examines the issue of outsourcing to a low-cost country based on a thorough analysis of competitive cost drivers. The case demonstrates that labor cost is only one potential advantage and that transportation cost and other factors could more than offset labor savings in some product lines.

The case is about an Indian company hedging soya oil price risk in the US futures market instead of in the Indian market to take advantage of better liquidity and wider choice of hedging instruments there. A stable long run relationship (cointegration) between the two markets appeared to make the cross border hedge viable, but hedge accounting considerations appeared to stand in the way.

By 2009 Netflix had all but trounced its traditional bricks-and-mortar competitors in the video rental industry. Since its founding in the late 1990s, the company had changed the face of the industry and threatened the existence of such entrenched giants as Blockbuster, in large part because of its easy-to-understand subscription model, policy of no late fees, and use of analytics to leverage customer data to provide a superior customer experience and grow its e-commerce media platform. Netflix's investment in data collection, IT systems, and advanced analytics such as proprietary data mining techniques and algorithms for customer and product matching played a crucial role in both its strategy and success. However, the explosive growth of the digital media market presents a serious challenge for Netflix's business going forward. How will its analytics, customer data, and customer interaction models play a role in the future of the digital media space? Will it be able to stand up to competition from more seasoned players in the digital market, such as Amazon and Apple? What position must Netflix take in order to successfully compete in this digital arena?

To examine the benefits and risks of investment in analytical technology as a means for mining customer data for business insights. Students will develop a strategy position for Netflix's investment in technology and its digital media business. Students must also consider how new corporate partnerships and changes to the customer channel model will allow the company to prosper in the highly competitive digital space.