For restaurants and hospitality businesses, there will always be occasions during peak trading where the business crashes, gets into the weeds or the kitchen goes down, but these occasions should be rare. In truth, for some businesses, this is happening on a regular basis. But, there are measures that you can and should take to improve and remove this very costly risk.
The best businesses – the ones where as a guest you receive an amazing welcome, the service is seamless and the food is served in a timely manner, meeting or exceeding your expectations – cause you to leave happy, knowing you will return.
What are these businesses are doing brilliantly? They are executing flawlessly against a solid plan, with a couple of key aspects:
- A great forecast. These businesses are taking time to couple great tech with good business knowledge to deliver a forecast that is close enough to reality to avoid the surprises that lead to bad service. They then use this forecast to establish what level of activity the forecast represents using data and metrics, defining the shape of day to ensure they have enough staff at the right times and great service for the guest.
- They use the forecast to do two things that aid the kitchen. First, prep planning, so that they do not run out of key items during peak service, resulting in long wait times while they recover. Second, they gain insights from the forecast data to engineer the menu, establishing expected volume of dishes at peak times and making sure that no dishes create bottlenecks due to complexity at key trading periods.
These aspects lend themselves to happier teams, who work in environments where management communicates the plan effectively, with the right team members and number deployed. This ensures that the whole team can do their jobs to the best of their abilities, and that guest expectations are met and customers return again and again.
Download our complimentary white paper to find out how we can help you achieve a more accurate forecast and bring more science to your scheduling.
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