Question

Case Study 1: Drive-Thru Service Times @ McDonald's (McDonalds Drive-Thru Waiting Times spreadsheet) When you're on the go and looking for a quick meal, where do you go? If you're like millions of people every day, you make a stop at McDonald's. Known as "quick service restaurants" in the industry (not "fast food"), companies such as McDonald's invest heavily to determine the most efficient and effective ways to provide fast, high-quality service in all phases of their business. Drive-thru operations play a vital role. It's not surprising that attention is focused on the drive-thru process. After all, over 60% of individual restaurant revenues in the United States come from the drive-thru experience. Yet, understanding the process is more complex than just counting cars. Marla King, professor at the company's international training center Hamburger University, got her start 25 years ago working at a McDonald's drive-thru. She now coaches new restaurant owners and managers. "Our stated drive thru service time is 90 seconds or less. We train every manager and team member to understand that a quality customer experience at the drive-thru depends on them," says Marla. Some of the factors that affect customers' ability to complete their purchases within 90 seconds include restaurant staffing, equipment layout in the restaurant, training, efficiency of the grill team, and frequency of customer arrivals, to name a few. Also, customer order patterns also play a role. Some customers will just order drinks, whereas others seem to need enough food to feed an entire soccer team. And then there are the special orders. Obviously, there is plenty of room for variability here. Yet, that doesn't stop the company from using statistical techniques to better understand the drive-thru action. In particular, McDonald's uses graphical techniques to display data and to help transform the data into useful information. For restaurant man agers to achieve the goal in their own restaurants, they need training in proper restaurant and drive-thru operations. Hamburger University, McDonald's training center located near Chicago, Illinois, satisfies that need. In the mock-up restaurant service lab, managers go through a "before and after" training scenario. In the "before" scenario, they run the restaurant for 30 minutes as if they were back in their home restaurants. Managers in the training class are assigned to be crew, customers, drive-thru cars, special needs guests (such as hearing impaired, indecisive, clumsy), or observers. Statistical data about the operations, revenues, and service times are collected and analyzed. Without the right training, the restaurant's operations usually start breaking down after 1 0–1 5 minutes. After debriefing and analyzing the data collected, the managers make suggestions for adjustments and head back to the service lab to try again. This time, the results usually come in well within standards. "When presented with the quantitative results, managers are pretty quick to make the connections between better operations, higher revenues, and happier customers," Marla states. When managers return to their respective restaurants, the training results and techniques are shared with staff who are charged with implementing the ideas locally. The results of the training eventually are measured when McDonald's conducts a restaurant operations improvement process study, or ROIP. The goal is simple: improved operations. When the ROIP review is completed, statistical analyses are performed and managers are given their results. Depending on the results, decisions might be made that require additional financial resources, building construction, staff training, or reconfiguring layouts. Yet one thing is clear: Statistics drive the decisions behind McDonald's drive-through service operations.

ONLY ANSWER QUESTION #2

Questions:

1) After returning from the training session at Hamburger University, a McDonald's store owner selected a random sample of 362 drive-thru customers and carefully measured the time it took from when a customer entered the McDonald's property until the customer received the order at the drive-thru window. These data are in the file called McDonald's Drive-Thru Waiting Times. Note, the owner selected some customers during the breakfast period, others during lunch, and others during dinner. Construct any appropriate graphs and charts that will effectively display these drive-thru data. Prepare a short discussion indicating the conclusions that this store owner might reach after reviewing the graphs and charts you have prepared.

2) Referring to question 1, suppose the manager comes away with the conclusion that his store is not meeting the 90-second customer service goal. As a result he plans to dig deeper into the problem by collecting more data from the drive-thru process. Discuss what other measures you would suggest the manager collect. Discuss how these data could be of potential value in helping the store owner understand his problem. 3. Visit a local McDonald's that has a drive-thru facility. Randomly sample 20 drive-thru customers and collect the following data:

a. the total time from arrival on the property to departure from the drive-thru window

b. the time from when customers place the order until they receive their order and exit the drive-thru process

c. the number of cars in the line when the sampled vehicle enters the drive-thru process

d. Using the data that you have collected, construct appropriate graphs and charts to describe these data.

****Write a short report discussing the data

Answer 1

2)

Given: the manager comes away with the conclusion that his store is not meeting the 90-second customer service goal.

The extra data that the manager should collect to better understand the problem are:

1. The manager should include data related to order details, like some people order only 1 deal, some order between 3 to 5 deals and some people order more than 10 deals. Using this, the manager can find out, when people generally order more deals.
2. The manager should also measure the time a customer takes to place the order.

These data are of huge potential value in helping the store owner understand the problem in the following ways:

1. When the manager notes the order details, the store owner is in a better position to understand at what time of day, they will be receiving big orders from customers. Thus, they can have the staff prepared to handle big orders and can also increase the number of staff present at these times.
2. When the manager measures the time a customer takes to place the order, this type of data could be helpful to find the total drive time and delay in time factors, that is, how much of the total 90-seconds delivery time, is consumed by the customer in finalising the order. This can then be utilised to find out how much further decrease in order delivery time is required.