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In 2017, while donating blood on campus, a management science graduate student noticed that the mobile blood donor clinic set up at his university’s community centre was a congested tandem queuing system. Finding one-and-one-half hours too long for donors to wait, the student considered how the process could be reduced by at least half an hour. He needed to devise a reasonably precise model to represent the donor flow in the clinic. Using either the mode or the average service times supplied by the nurses, the student could build a relatively straightforward discrete-event simulation model to identify bottlenecks and improve the donor flow.

Instructor spreadsheet for product 8B17E010.

Instructor spreadsheet to accompany product 8B17E008.

The customer relationship manager at JSW Steel Ltd., a large steel manufacturer in India, needed to analyze his available transportation and logistics options to meet an urgent order for a long-time and valued client. The manager needed to decide whether to send the shipment through the customary rail route or, instead, to use the new sea route that his company had recently developed. His dual objective was to meet the customer's requirements in time, while also delivering some financial benefit to boost his company’s quarterly results.

In January 2016, the chief executive officer of Harley’s Corner, an India-based online retailer of gourmet dog food, faced a decision dilemma. He needed to decide on the most suitable positioning strategy for the business, which competed against several well-known brands, including other fresh, gourmet, home-cooked food for pets. Harley’s Corner needed to convey a proposition that ran deeper than simply representing an “all-natural, home-cooked” fresh pet food. How could he differentiate the offerings in a relevant way to retain his loyal customers, attract new customers, and thereby grow the business? What was the best way to position his brand, Harley’s Corner, which catered to a niche segment?

In October 2013, the Royal Bank of Canada (RBC), Canada’s largest bank, hired a new head of Enterprise Fraud Strategy, a department tasked with protecting RBC’s global customers from fraud. The department head’s immediate priority was to prevent fraudulent transactions by RBC’s own customers—a phenomenon called first-party fraud—by implementing a bourgeoning technology called social network analysis (SNA). The technology used predictive analytics and big data to forecast the occurrence of first-party fraud. The head of Enterprise Fraud Strategy had three primary questions: First, how should SNA be used to bring down the ratio of fraud alerts to actual fraud at RBC? Second, how should the cost of maintaining SNA protocols be reduced? Finally, how should the issues around systemic performance of SNA be resolved?

In early 2016, LUX* Resorts & Hotels, a hotel group headquartered in Mauritius, needed to determine the room mix to sell to foreign guests to achieve a specific earnings target. The hotel group also intended to sell some of the remaining rooms to local residents at affordable (discounted) prices. The objective was to maximize the revenue stream to finance local corporate social responsibility projects. The case highlights the practical use of Microsoft Excel Solver, an optimization tool, to resolve routine planning challenges faced by a hotel group.

In early December 2016, the owner of the Asian Grill restaurant in Henderson, Nevada, noticed that the restaurant had busy nights where customers left due to long wait times, yet there were empty seats due to small groups occupying large tables. She had a proposed new table layout before her and had to evaluate whether this would allow the restaurant to seat more customers on her busiest nights (Thursday to Saturday), or whether a different table layout would be even better.

Linear regression is a common tool used in statistics and is considered the foundation for most predictive analytics. It creates a line of best fit in a data set and uses that line to explain the relationship between two quantities, which helps forecast future values. This note provides a high-level, non-technical overview of linear regression.

<p style="color: rgb(197, 183, 131);"><strong> AWARD WINNER - INFORMS Case and Teaching Materials Competition</strong></p><br>Founded in 1972, the Association of Tennis Professionals (ATP) was the governing body of three professional men’s tennis circuits: the ATP World Tour, the ATP Challenger Tour, and the ATP Champions Tour. In addition to the ATP World Tour tournaments, there were four highly coveted and extremely competitive Grand Slam tournaments, one of which was Wimbledon. In 2014, two professional tennis players sought to determine their respective odds of placing first at Wimbledon, by using an understanding of probability, stochastic modelling, and Markov chain application. Both players were ranked outside the top 100 and faced a momentous task.

In 2016, a soccer fan closely followed the scores, standings, and transfers of players in the European soccer leagues. Having witnessed countless exchanges of players from one team to another, she wondered about the factors that team managers considered when determining the players’ market values. She gathered data on 77 notable soccer players in an effort to better understand the essential factors that made the players valuable. Could she use the variables she had collected to also build a functioning model that would predict a player’s market value?

In late 2012, the owner of the Ratnagiri Alphonso Orchard considered whether or not to purchase information from a climatology firm regarding the probability of unseasonable rains that could damage some or all of his family’s mango harvest. The owner needed to decide whether or not he should lease the orchard to a fruit merchant or keep the orchard for his family to harvest, despite the possibility of rain. Would the climatology firm provide helpful information, or should he make an independent decision? Regardless of whether he purchased information from the climatology firm, what was the best informed decision he could make?

In 2014, the owner of a food truck based in Hamilton, Ontario, was looking over the first year of her operations. In addition to working in Hamilton, she had tried to maximize her revenues by driving to several other cities and charging various prices for each burger, depending partly on the fresh ingredients available in each city. Besides location, the owner had collected data on a few other factors—the weather, the day of the week, the city’s population, and whether a festival was going on—that had had an impact on the demand for her product. She wondered whether analytics could help her decide where to sell and how much to charge on a daily basis. The owner also wondered whether this decision-making and data-collection process could be automated since she would be using it every day.

Instructor spreadsheet for product 8B16E001.

Student spreadsheet for product 9B16E001.

APLO was a reputable supplier of LED lighting systems for diverse countries from Taiwan to the United Kingdom. In 2015, APLO signed a contract to supply and install 30,000 pieces of 60-watt ecological street lights in several towns within one of Indonesia’s provinces. With over 250 million inhabitants, Indonesia was in dire need of modernizing its infrastructure as it expanded economically. Having already been contracted for street lighting in several parts of Jakarta, Indonesia’s capital city, APLO was eager for more projects across Indonesia. Would APLO be able to make this its showcase project and win many more contracts throughout Indonesia’s 34 provinces?

In November 2014, the founder and chief executive officer of HomeZilla, in Toronto, Canada, was considering how to provide value-added services to his business. One of the company’s main services was to work with real estate agents to provide web listings to attract home shoppers. Many Internet companies were analyzing web-browsing data in an effort to better understand user behaviour and thus improve their business. Inspired by this trend, the founder considered how his company could use web-browsing data to better attract home shoppers on the company’s website and thereby add value to his business.

On February 29, 2016, an investor, who was a business school graduate, purchased one share of Tesla Motors, Inc. stock for $191.93. In doing so, the investor wondered what trading strategy should be followed over the next six months to maximize returns while minimizing risk. In choosing a strategy, he wanted to make good use of the knowledge gained from his financial analytics classes. To that end, he did not know if he should choose a buy and hold, or a stop-loss strategy with an optimal stop-loss threshold. The investor wondered how he would make this decision. Would a six month comparison of the stop-loss strategy with thresholds from 1 per cent to 99 per cent versus the buy and hold strategy resolve his dilemma?

Spreadsheet to accompany product # 9B16E023.

The purpose of this note is to introduce students to the basic use and vocabulary of Microsoft Excel. Excel is commonly used to do quantitative analysis in business. Students will encounter Excel applications in just about every area of business, including finance, accounting, operations management, marketing, and analytics.