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The Evolution of Scheduling

A more scientific approach to scheduling can help you get ahead.

Every restaurant operator knows the recipe for the perfect schedule: get the right people on the right shift at the right time. But when it comes down to it, that’s much easier said than done.

In an attempt to achieve higher levels of accuracy, operators have been trying out different ways to schedule. As the methods for scheduling evolve, each iteration comes with its own set of challenges. Below, we outline the full evolution. From relying on hunches to relying on science, these are the three most common approaches to scheduling. We’ll also share insight into the more accurate fourth (pun intended!) approach — demand forecasting.

Scheduling Based on a Percentage of Weekly Budget

The traditional approach to scheduling is by weekly budget. This means managers allocate labor across the week based on their allowed labor spend, which is based on expected sales. What does that look like?

Managers have a fixed amount of money to spend on staffing throughout the week. They then assign team members to set shifts. There’s little thought to staggering or activity levels by hour for each shift. So, the resulting day has periods of time overstaffed, and during peaks, staffing feels thin.

Usually, too many are on the schedule for the beginning of the week. The error compounds, and to stay on budget, managers are forced to cut shifts at the end of the week. Unfortunately, these also tend to be the busiest and so you run the risk of impact to the guest experience. On the flip side, if managers assume a lighter front-end of the week, they can get caught off guard. A change in weather or a holiday can drive an unanticipated amount of traffic to the restaurant.

When scheduling to a budget, managers also run the risk of over-staffing on slower weeks. Or, they may deliberately over-staff to avoid budget cuts. A slow, boring shift means employees receive lower tips and have less to do. These employees are then checked out or distracted on the job. Guests may have a hard time getting their server’s attention. On the flip side, they may feel that staff is overbearing and over-attentive. Meanwhile, every over-staffed hour is denting your bottom line.

Many stick with this traditional scheduling approach because it helps them stay within the set weekly budget. That’s definitely important for regulating spend! The impact, however, is negative. Team members take the brunt of the stress, and turnover rates increase. Servers miss key selling opportunities, which adds up to lost potential revenue. Future recurring revenue is also less likely if customers are unhappy. While it works week to week, the long term impact to your bottom line (and growth potential) is real.

Sales Per Labor Hour (SPLH)

SPLH forecasting attributes a value to each labor hour. Managers divide the sales revenue forecast by this number to determine how many employees they can put on the schedule. SPLH targets differ role to role. But here’s a rough example. If the total sales for the week are $2,000 and your target SPLH is $50, then your manager has 40 hours to work with.

As you may guess, the main challenge with scheduling by SPLH is that the workload or staff-impact depends on what is actually ordered. You can make $150 by serving a single bottle of champagne. Or, you can make $150 by running 50 separate $3 soft drinks to different tables. The time spent by a given employee differs for each scenario. The people you need differs too, for different activities. For example, serving a beer requires different training from shaking up a cocktail.

The other limitation here is that the forecasted spend only focuses on revenue-generating activities. This does not account for the tasks that do not generate revenue but still take staff attention and time. For example, what percent of a given shift is taken up by prep work or restocking? Often, these hours are not accounted for when forecasting by SPLH.

SPLH is undoubtedly a useful metric. It allows operators to benchmark employees and identify areas for training and improvement. But using SPLH for forecasting doesn’t provide the level of accuracy for your business that you need.

Scheduling Based on Covers

Some restaurants have now turned to scheduling based on covers (the number of seats filled on a given shift) as a way to predict staffing needs. This does bring in some science to the equation, and covers are good metric. It definitely gets operators a step closer to their goal of accuracy. But this approach still has major limitations when it comes to forecasting the future and analyzing past results.

The main challenge? Scheduling per cover fails to factor in guest behavior and trends. These both impact your employees. The time and effort required to deliver exceptional customer service varies by table.

Let’s look at an example. What if one table orders soft drinks and appetizers, while another opts for a multi-course meal and cocktails? The amount of time required for staff to prepare for each scenario differs. So too does the amount of time needed to serve, clear, and tend to each set of guests.

To make this equation even harder to balance, the table with the multi-course meal could have only two covers, and the four-top may be stopping by for a light, post-work snack. In this way, a night with low cover counts could still mean high demand on your staff. The staffing requirements should depend on the complexity and volume of orders from any given table.

While this approach has obvious limitations, it is a step in the scientific direction.

The Fourth Solution: Demand Forecasting

Demand (or item-based) forecasting is currently the most scientific approach to scheduling. It fully utilizes the power of data. Demand forecasting harnesses the data that exists within the organization (like historical sales analysis and recent trends), combines it with external information (such as weather, national and local events, holidays), and finally overlays local manager knowledge. Now, it’s very possible to predict who will be needed for a given shift.

Self-learning algorithms take all this data and accurately forecast the individual items that will be sold. It gets better. This forecast predicts sales down to 60-, 30- or 15-minute increments. Now you can more accurately forecast when you’re likely to sell that $150 champagne. And, what times the shift will be full of less expensive, non-alcoholic beverage orders. Managers can staff both shifts accordingly.

Demand forecasting also takes into account exactly how much time is needed to deliver a certain activity. (Even non-revenue-generating, but necessary, activities like prep and finish.) With all this data factored in, the system can suggests the right number of employees to meet demand by area, and throughout the day. Managers get a more accurate forecast the first time, and can also then stagger shift start and end times. This maximizes productivity and minimizes labor spend.

The benefits abound. Last-second shift swapping decreases. Plus, with a more accurate forecast, managers no longer have to cut employees mid-shift. As a result, your staff has a better sense of what to expect on a given shift. They’ll feel more relaxed and prepared to handle the workload. Your team will have the bandwidth to drive sales and deliver a winning guest experience. They’re also better able to plan their time and schedule outside of work, which will help them feel more engaged when they’re there.

What Next?

With labor as the largest line item in most restaurants’ P&L, creating an accurate schedule has always been top-of-mind. Now, a higher level of accuracy is possible. Equip your team with a scientific approach. By leveraging existing data, trend analysis, and the manager’s local knowledge, you can match staffing levels to demand. That way, managers can more confidently get the right people on the right shift at the right time. The result? Happier employees, more satisfied guests, and a healthier bottom line.

Want to learn more about demand forecasting and how to optimize your scheduling? Download our complimentary Science of Scheduling white paper.

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