What is the most a new store prototype can serve in a peak hour?

Each test is a physical build. Before rolling out a new store layout or concept, the chain had to show the design could meet forecast demand. Its teams set up prototypes in the lab and ran simulated rushes with real crew and equipment. Every round used labor, product, and lab time, and after many rounds the team could see it would never get through every combination it needed to test.

Most large chains test this way. Burger King built a life-size prototype restaurant, dual-lane drive-thru included, inside a 40,000-square-foot warehouse so crews could run simulated service on new layouts. Labor raises the stakes: in 2024, profitable limited-service operators ran labor at a median 30.0% of sales, against 34.1% for those that lost money (National Restaurant Association).

SimWell built a simulation of several store prototypes, from the order point through production to the pickup window, and matched it to the chain's lab results. The team can change product mix, channel mix, customer arrival patterns, equipment counts, and crew deployment by position, then see each layout's maximum orders per hour and how busy each station runs.

Item build times came from a time-and-motion study rather than estimates. Every run is stored in a database the chain can query and add to as the menu changes, and a Power BI dashboard reports speed of service and throughput for each scenario in terms operators can read without a simulation background.

There are too many combinations to test by hand. SimWell trained an AI agent on the simulation to search staffing options for each scenario, which cut the number of model runs and returned answers sooner. For each shift it set crew size and position assignments that balanced the workload, cut walking between stations, and removed long idle stretches.

The chain can now test new store prototypes far faster than the lab allows, without tying up crew, equipment, or product. In the simulated stores, throughput rose, and order production time and wait times at the order point and pickup window came down. The chain then set up a long-term partnership with SimWell to simulate future store prototypes, new equipment, and continuous improvement initiatives.

In a separate engagement, a national pizza chain used the same approach to test a new make-line flow against a real day of order history before asking its franchise owners to adopt it.