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 pick-ing 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.
Discussion Questions:
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.