Due to changing consumer tastes and industry competition, fast food is no longer a simple business. As a result, figuring out how to keep wait times down in ever busier drive-thru lanes has become more challenging.

StrategyWise is working with a top national fast food chain to find ways to decrease drive-thru wait times, using data analytics and simulations to build route optimization algorithms. These tools are used to identify problematic areas and contributing factors, and to develop solutions to minimize drive-thru wait times. This will therefore improve the broader customer experience, which is a critical component and key differentiator for this particular chain’s success.

Prior to working with StrategyWise, the client had already been experimenting with tablet use and in-line payment in some drive-thru lines. It had also launched a mobile app for customers to order meals, pay in advance, and pick up their food inside the restaurant, thereby skipping the line. The chain came to StrategyWise seeking ways to maximize technology to streamline drive-thru processes.

Aims and Goals

The client’s goals are threefold:

  • Reduce drive-thru wait times.
  • Reduce failed entrances (customers driving by crowded drive-thru lines).
  • Improve the overall customer drive-thru experience.

Strategies

The stated project goals were achieved by improving drive-thru designs and customer service strategies. Design features that were of particular interest were:

  • Using mobile workers (“runners”) to take orders (via tablets) and deliver those orders.
  • Using “pullaway” lanes: A customer, having received their order, may exit the drive-thru via an encircling pullaway lane.
  • Providing special parking spots in which customers may wait for large orders.
  • Processing card payments in an area separate from cash payments.

Evaluation Method

StrategyWise developed a computer simulation to gauge which of the above drive-thru designs and customer service strategies optimize wait times. The solutions will then be used in real life, physical simulations by the client.

Data scientists at StrategyWise first focused on modeling each section of the drive-thru in isolation. They then wrote code to perform various simulations. These simulations helped the project team to think more clearly about the business problems under consideration.

After developing the simulations, analysts wrote Python code to model the entire drive-thru and to produce animations that would make the situation come to life for the client. These animations were essential to help the client, particularly the drive-thru managers, visualize and accept the results. Results demonstrated clearly what does and does not work and where the drive-thru bottlenecks occur. One solution in particular was shown to optimize wait times and to reduce “bounces,” that is, people choosing not to enter a busy drive-thru line.

Results

We found that for a typical restaurant working at capacity (e.g. at lunchtime), a pullaway lane would reduce the drive-thru time for an average customer by 15% and raise the number of customers that could be processed by 19%.

Future Applications

The client is now completing the optimal strategy and design for drive-thru lines. Once finished, it may turn its attention to in-store dynamics, evaluating and simulating lines to determine the optimal interior layout.