Case Study:
Restaurant Location and Re-imaging Decisions @ McDonald's
In the early days of his restaurant company's growth, McDonald's founder Ray Kroc knew that finding the right location was key. He had a keen eye for prime real estate locations. Today, the company is more than 30,000 restaurants strong. When it comes to picking prime real estate locations for its restaurants and making the most of them, McDonald's is way ahead of the competition. In fact, when it comes to global real estate holdings, no corporate entity has more. From urban office and airport locations to Walmart stores and the busiest street corner in your town, McDonald's has grown to become one of the world's most recognized brands. Getting there hasn't been just a matter of buying all available real estate on the market. Instead, the company has used the basic principles and process Ray Kroc believed in to investigate and secure the best possible sites for its restaurants. Factors such as neighborhood demographics, traffic patterns, competitor proximity, workforce, and retail shopping center locations all play a role. Many of the company's restaurant locations have been in operation for decades. And although the restaurants have adapted to changing times-including diet fads and reporting nutrition information, staff uniform updates, and menu innovations such as Happy Meals, Chicken McNuggets, and premium salads-there's more to bringing customers back time and again than an updated menu and a good location. Those same factors that played a role in the original location decision need to be periodically examined to learn what's changed and, as a result, what changes the local McDonald's needs to consider. Beginning in 2003, McDonald's started work on "re-imaging" its existing restaurants while continuing to expand the brand globally. More than 6,000 restaurants have been re-imaged to date. Sophia Galassi, vice president of U.S. Restaurant Development, is responsible for the new look nationwide. According to Sophia, re-imaging is more than new landscaping and paint. In some cases, the entire store is torn down and rebuilt with redesigned drive-thru lanes to speed customers through faster, interiors with contemporary colors and coffee-house seating, entertainment zones with televisions, and free wi-fi. "We work very closely with our owner/operators to collect solid data about their locations, and then help analyze them so we can present the business case to them," says Sophia. Charts and graphs, along with the detailed statistical results, are vital to the decision process. One recent project provides a good example of how statistics supported the re-imaging decision. Dave Traub, owner/operator, had been successfully operating a restaurant in Midlothian, Virginia, for more than 30 years. The location was still prime, but the architecture and décor hadn't kept up with changing times. After receiving the statistical analysis on the location from McDonald's, Dave had the information he needed to make the decision to invest in re-imaging the restaurant. With revenues and customer traffic up, he has no regrets. "We've become the community's gathering place. The local senior citizens group now meets here regularly in the mornings," he says. The re-imaging effort doesn't mean the end to new restaurant development for the company. "As long as new communities are developed and growth continues in neighborhoods across the country, we'll be analyzing data about them to be sure our restaurants are positioned in the best possible locations," states Sophia. Ray Kroc would be proud.
Q1. Sophia Galassi, vice president of U.S. Restaurant Development for McDonald's, indicated that she and her staff work very closely with owner/operators to collect data about McDonald's restaurant locations. Describe some of the kinds of data that Sophia's staff would collect and the respective types of charts that could be used to present their findings to the owner/operators.
Q2. At the end of 2001, Sophia Galassi and her team led a remodel and re-imaging effort for the McDonald's franchises in a major U.S. city. This entailed a total change in store layout and design and a renewed emphasis on customer service. Once this work had been completed, the company put in place a comprehensive customer satisfaction data collection and tracking system. The data in the file called McDonald's Customer Satisfaction consist of the overall percentage of customers at the franchise McDonald's in this city who have rated the customer service as Excellent or Very Good during each quarter since the re-imaging and remodeling was completed. Develop a line chart and discuss what time-series components appear to be contained in these data.
Q3. Referring to question 2, based on the available historical data, develop a seasonally adjusted forecast for the percentage of customers who will rate the stores as Excellent or Very Good for Quarter 3 and Quarter 4 of 2006. Discuss the process you used to arrive at these forecasts.
Q4. Referring to questions 2 and 3, use any other forecasting method discussed in this chapter to arrive at a forecast for Quarters 3 and 4 of 2006. Compare your chosen model with the seasonally adjusted forecast model specified in question 3. Use appropriate measures of forecast error. Prepare a short report outlining your forecasting attempts along with your recommendation of which method McDonald's should use in this case.
Q5. Prior to remodeling or re-imaging a McDonald's store, extensive research is conducted. This includes the use of "mystery shoppers," who are people hired by McDonald's to go to stores as customers to observe various attributes of the store and the service being provided. The file called McDonald's Mystery Shopper contains data pertaining to the "cleanliness" rating provided by the mystery shoppers who visited a particular McDonald's location each month between January 2004 and June 2006. The values represent the average rating on a 0-to-100 percent scale provided by five shoppers. A score of 100% is considered perfect. Using these time-series data, develop a line chart and discuss what time-series components are present in these data.
Q6. Referring to question 5, develop a double exponential smoothing model to forecast the rating for July 2006 (use alpha = 0.20 and beta = 0.30 smoothing constants). Compare the results of this forecasting approach with a simple linear trend forecasting approach. Write a short report describing the methods you have used and the results. Use linear trend analysis to obtain the starting values for C0 and T0.
Provide complete and step by step solution for the question and show calculations and use formulas.