Dealing with: Customer complaints about wait times to be seated and the wait time for food. Solution: Collect data in order to understand if inventory (food, patrons, employees) management is inefficient, resulting in poor process flow and capacity issues. Using data gathered to create a capacity analysis report (an example of a capacity analysis is found at the end of this blog) is the ideal way to fully grasp where the root of the problem stems from.
The Popularity of a restaurant can be a great thing but unmanaged growth can be a hindrance to an operation asthe inability to manage the resulting variabilitycaused by the growth can result in two issues:
- Long wait times for a table
- Long wait times for food
Key Benchmarks on which data should be gathered to create a capacity analysis
- How many seats does the restaurant hold and what is the mix?
- How many employees?
- What are the hours of operation?
- What is the ideal and actual total employee task performance per table (ex. 14.5 min)?
- What is the projected and actual turnaround time for a table?
Based on the capacity report below, at the most basic level, one can determine that more staff is needed between 5-6 pm. If staff is increased then turn around time could be increased, ultimately making up for the extra labor cost. Furthermore, wait time would be eliminated and, if food quality stays consistent, then reviews should improve thereby attracting more patrons. Additionally, one can use such analysis to see where staff can be decreased and where staff can be used better. Lastly, one can use such an analysis in order to come up with more creative solutions, such as forgoing the need for reservations and implementing a pager system.
| Resource | Quantity | Unit of Analysis | Cycle Time(min./unit) | Capacity (meals/hour) | Capacity Utilization at Average Demand (meals/hour) | Capacity Utilization5-6pm | Capacity Utilization8-9pm | |||
| Greeter/Cashier | 1 | Table | 2 | 108 | 90% | 76% | 38% | |||
| Servers | 4 | Table | 8 | 108 | 90% | 76% | 38% | |||
| Cooks | 3.5 | Meal | 2.5 | 84 | 115% | 98% | 49% | |||
| Bussers | 2 | Table | 2 | 216 | 45% | 38% | 19% | |||
| Seating | 90 | Meal | 75 | 72 | 135% | 114% | 57% | |||
| Guests/table | 3.6 | 5-6pm | 82 | |||||||
| Peak demand | 103 | 6-7pm | 103 | |||||||
| Average demand | 80 | 7-8pm | 94 | |||||||
| 8-9pm | 41 | |||||||||
Written by: Ariel Landa- Global Hospitality Leadership (MPS)
Date: May 22, 2018
