There are many ingredients that go into the perfect schedule, and every manager relies on a slightly different recipe. For many, it’s not an easy thing to whip up, but because they make schedules all the time, they’ve found a method that works well enough.
The thing is, with razor thin margins, increasing labor costs, and predictive scheduling laws adding complications in many states, “well enough” simply can’t cut it.
Luckily, there is a tried and true solution, one that gets you a schedule with the right people on at the right time. It makes for a delicious success: happier employees, fewer headaches for managers, and a healthier bottom line.
So what goes into the perfect schedule? The ingredient list is short:
- Hefty serving of data
- Dash of manager knowledge
- Sprinkle of employee availability
- Pinch of collaboration and flexibility
- And, a good measure for forecasting accuracy, like WAPE
Let’s break it down.
Adding the right data into the mix is key. The data must be fresh, it must be relevant, and above all else, accurate.
Some managers just use their gut instincts. Some managers throw in a dash of data. Others season liberally with one type of data.
The thing is, variety is the spice of success here. You need many kinds of data to see the full picture. This should include historical performance data, recent and year-on-year trends, upcoming local and national events, holidays, and even the weather.
You also need the right type of data. When looking at historical performance, don’t just look at the dollar value. Look at the specific items you’ve sold (and when) so you can use that data to more accurately predict demand across the day.
By knowing what you will be selling, you’ll be able to put the right people on the right shift at the right time, staggering start and end times to meet demand.
Your manager knows the ins and outs better than anyone. They’ll know when there’s a local game on that might increase demand, or when there’s construction in the area that might reduce foot traffic. Blending their expertise with the aggregated data means you’ll have the perfect harmony of flavors in the mix.
This is a case where engaging your employees will not mean too many cooks in the kitchen.
Scheduling should be a collaborative process. To get the best-possible result, understanding when your employees are unavailable is critical.
Select a mobile tool that lets your employees suggest times when they can’t make a shift (and why) to avoid creating a schedule that will need to change right away. Your managers will then have a better understanding of who can work when as they review and approve these suggestions.
Collaboration and Flexibility
It is true that you can’t predict everything. Sometimes, schedules will need to shift. Using a mobile tool that allows employees to quickly suggest swaps that work and to pick up extra hours by responding to open shift posts will make it easier for your managers to do their job. They simply need to use the same mobile app to review and approve. What’s more, it helps employees feel like you have their back, so it’s a win/win.
The batter, er, schedule, should now look pretty reasonable. You’ve scheduled based on the forecasted demand across the shape of the day, ensuring enough people to cover the busier hours, while lightening up when the rush dies down, and you’ve successfully avoided last minute shift cuts due to budgetary restraints or because too many are standing around bored with nothing to do.
You’ve made sure that your employees are busy and engaged without being run ragged. Plus. your guests are happy, getting everything they need (and more, as your staff now has time to offer second rounds of drinks or dessert, or both)! Are you done?
The final ingredient requires measuring forecasting accuracy.
If you can see how every single location performed against their forecast, you can see key areas for improvement. You can make better decisions to help under-performing locations recover, and you can help the locations that are doing well do even better.
Driving top-line sales and cutting the waste from your bottom line will make your restaurant run more efficiently and more profitably.
There are different ways to measure forecasting accuracy. Here, we use Weighted Absolute Percentage Error (WAPE), which is the best method for measuring forecasting accuracy. It looks
at the forecast vs. the actuals on a daily basis. That way, operators get actionable insights and more accurate data. Most importantly, it doesn’t hide the true picture in weekly averages or aggregated sites.
When looking at a weekly average, a good day and a bad day can cancel each other out, which hides what’s truly going on.
Better sales than expected is great, but it means you were still off in your forecast, which can impact labor and inventory. Let’s say you were off in your projection by 3 points on one day. Being down on another day is 3 more points off. When looking at weekly averages this would look like everything was fine. The values cancel each other out. But really, the total error is 6! The errors compound even further with more than one location.
From 1 location to 500, WAPE is a critical tool in understanding the difference between expectation and reality. Looking at your daily WAPE can help you identify areas of improvement, save money by reducing operational costs caused by over- and under- forecasting, and can allow managers to adjust throughout the rest of the week to take advantage of profit-driving opportunities.
The Final Step? Enjoy the Result.
With the right data and the right tools, developing the perfect schedule is easier than ever. You’ll get the right number of chefs in the kitchen — and the right number of servers on the floor. By improving forecasting accuracy, your restaurant can lessen the impact on the bottom line, while driving top-line sales. Less busywork for your managers, and happier employees and guests is a definite recipe for success!
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The 2019 Spark conference, presented by HotSchedules and Fourth, brought restaurant industry leaders together to address challenges facing operators today.
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