Services

15%more revenue from optimizing the table mix

More revenue from the same room, with fewer seats

A restaurant

Diners seated in a busy restaurant whose table mix was optimized with process simulation

The problem

Where the time was going

A popular restaurant faced a frustrating contradiction on its busiest nights: a long line of guests waiting at the door while chairs sat empty inside. When the wait stretched to a couple of hours, parties simply walked away, and the owner could watch the lost revenue leave with them.

The cause was the table mix, not the kitchen or the service. With too few of the right-sized tables, a steady stream of small parties ended up at larger tables, so that when a big party arrived there was nowhere to seat them despite the empty seats. A room with 300 seats but only 200 that fit the parties on hand is wasting a hundred places.

What we modeled

Mapping the process, then testing the fix

The restaurant decided to fix the second problem, the seating, rather than the serving process, and to pursue a table mix that delivered the most throughput from the least space. First it gathered the facts the decision needed: the party mix during peak hours, when each kind of party tended to arrive, the current table mix, and how many spots could take large parties.

With that information, ProcessModel consultants built a working model of customers flowing through the restaurant's existing system. The model let them watch the current process, predict where it would jam, and optimize the table mix specifically for peak hours, when seating is scarce and the mix matters most.

The result

The proof, and the payoff

Where the optimized table mix was adopted, revenue rose about 15 percent, without adding staff, enlarging the building, or spending more on marketing. The gain came purely from seating the right parties at the right tables when it counted.

Seat utilization rose about 30 percent at the same time. The model showed that the restaurant needed far more two-top and four-top tables than large ones, so it lifted utilization sharply while actually reducing the total number of seats, and gave guests faster, more consistent service in the bargain.

15%
more revenue
~30%
higher seat utilization

See your process clearly, then prove the fix

Build the model, run the simulation, find the constraint, and show the improvement before you change a thing.