Chapter 3 – Sales History and Forecasting

Chapter Outline:

  • Importance of forecasting
  • Using sales history
  • Food, beverage and labor forecasting
  • Factors affecting forecasting
  • Forecasting in the “big picture”

Learning Objectives:

  • Define terms related to sales history and forecasting, such as sales, guest count, check average, etc.
  • Explain the importance of sales history data, including the types of data to collect
  • Explain the importance of forecasting to effectively managing a foodservice operation.
  • Describe the use of a popularity index for forecasting production.
  • List factors that affect forecasting in a foodservice operation.

Key Terms:

  • Sales history
  • Customer count or covers
  • Guest check average
  • Food cost percent
  • Popularity index
  • Beverage cost percent
  • Labor Cost percent
  • Over/Under-pouring
  • Emergency stock
  • Table turns

Forecasting in the Foodservice Operations

The ability to accurately forecast sales and expenses is a necessary skill for a manager or owner to possess. In this chapter we will look at ways to help you to become more proficient in forecasting both your sales and expenses.

Using the history of past sales in your foodservice operation is critical when attempting to accurately forecast future sales. But, as the famous investing quote states “Past Performance does not guarantee future results”. It is not enough to only look at the past to predict the future. There are just too many variables that can positively or negatively affect our operation. In this unit we will look at several ways food service operations can attempt to accurately forecast.

Two important figures to track in a foodservice operation’s sales history, in addition to overall sales, are customer count or number of “covers” and check average. Many operations record these figures hourly and use them daily to control both food production and labor usage. These figures are likely part of a computerized sales and accounting system and can be saved for multiple years. Looking back at these records is often the basis for predicting future sales and customer counts. Guest check average is also useful in comparing performance from one time period to another or one unit to another. Guest check average, past customer counts and sales are also critical to developing budgets and other plans for future operations.

Calculating the guest check average is quite simple as long as the proper records are being kept. Check average is just what it says, the average of what each guest spends. It is calculated by dividing the total food and beverage sales by the total number of guests (or covers) in a particular time period. Challenging operators, managers, and even servers to increase the guest check average over a period of time or during a particular meal period can be a way to motivate everyone in the operation to help increase revenue and “grow” the business.

As managers or owners, we must walk a fine line between having enough product or labor, while not having too much. Many companies are now grading individual units based on how much inventory they have on hand, or how much they spent on labor. If you have too much you would be “in the red”. Each company would have their own formula to decide what is too much. It is typically based on how many weeks worth of inventory you have (calculations for this will be covered in a later chapter.) Of course, having a certain “emergency stock” on hand to account for inaccurate forecasts, unexpected crowds (think tour bus), weather emergencies, delays or missed deliveries is usually a good idea, especially before the business is well established. Also, the more remote your property the more emergency stock you would need to keep on hand. Think remote lodge, or an oil field on the north slope in Alaska. These facilities would obviously need a great deal of “emergency stock” on hand.

Labor will be judged by how much you spent versus how much revenue you brought in. Productivity of labor should be calculated and evaluated on a regular basis. Setting benchmarks for labor productivity can also help an operator schedule employees based on sales history. Think about food or labor cost percent (from the previous chapter) and the foodservice manager being “graded” on how well these two business ratios are managed.

Why is it important to accurately forecast?

Food – If we order, and prepare too much food it will negatively affect our bottom line. Waste will attribute to a high food cost, and high food cost %. Even worse, would be over producing, and serving food that is not at its optimum, which will lead to customer dissatisfaction. Portion control also can affect our forecasting. If we over portion, then we can run out of food. If we under portion, then we short-change our customer. Constantly running out of products can ruin the reputation of your food service operation. Many operations use a concept called a “popularity index” to assist in forecasting how much of each menu item to produce given an overall customer count forecast. Example: The percentage of the total number of entrees sold for each individual entrée is established based on sales history. Then that percentage is applied to the total customer count predicted to be served for a specific meal or day.

Calculation example of a popularity index:

Entree Average daily number sold this week Percent of total entrees sold Number to produce for forecast of 1200 guests
Chicken 400 40% 480 chicken entrees
Fish 200 20% 240 fish entrees
Beef 350 35% 420 beef entrees
Vegetarian 50 5% 60 vegetarian entrees
Total 1000 100% 1200 total entrees

Chapter 3, Figure 1

 

If the forecast of total customers for a particular day is set at 1200, then the percentage for each entrée is applied to this total forecast (see right hand column in chart above.)

Beverage – Accurately forecasting beverage is also vital, and in many ways similar to food. There are of course numerous differences as well. Bartenders may “under pour” alcoholic beverages on purpose so they can build up an excess of inventory, and eventually not ring in an item, and pocket the money. They can also intentionally “over pour” in the hopes of getting a bigger tip in return. This will of course affect the liquor costs, and liquor cost percent. There are legal ramifications as well. Intentionally over-pouring a beverage can lead to lawsuits if the customer is involved in an accident. Many beverages have expiration dates, and if not used in time, would need to be discarded. This would, of course, negatively impact our bottom line.

Labor – As the cost of labor keeps rising and there is pressure to increase the minimum wage, there has never been a more important time to control labor. Having too much labor will negatively affect the food service operation’s profitability. Not having enough labor, will negatively affect the customer service experience. A skilled manager will strive to have just the right amount of labor needed at any given time. Many restaurants will schedule extra servers because they do not cost the restaurant as much as other employees since their wage is low due to receiving tip credit. However, we have to be careful because if we constantly have too many servers on a shift then they will not make as much money, and may eventually leave for a better situation.

Factors affecting forecasting

Using the past to predict the future – One of the first places to look when forecasting is our sales history along with inventory and production records. What did we do on a particular year, quarter, month, week, day, or meal period compared to the prior year, or years, month, or day, even the prior hour or hours?

Weather

To take things even further, we need to look at weather and how it affects forecasting. It is always a good practice to keep track of weather with our sales history so we can compare one day or period to another. Typically weather will have a negative effect on your operation. There are of course exceptions to this rule. An example is a hotel that is connected by a walkway to a large office building. If the weather is bad, the hotel restaurant gets particularly busy because many employees of the neighboring office building choose to eat at the hotel so they did not have to go outside. Another example is a restaurant with outdoor seating. Rain would decrease the number of customers, but beautiful weather would likely increase sales as the restaurant can serve more customers with the increased seating capacity and because customer choose this operation for its patio ambiance. It is a good practice to keep notes about the weather as it relates to your menu mix and sales history.

The Economy

Next we would consider the economy, especially at the local level. Did we lose or gain any new customers? Is our area or region growing? Are there new housing developments, new industry, new businesses coming to our area or are businesses moving out or closing down around us? Do customers have more or less disposable income than they did in the previous year?

City-wide events

It is important to understand what is going on in your market. A city-wide event could change the dynamic of your business. It can directly or indirectly add, or take away business from your establishment. For example, if there is a college or professional sports team and stadium in your area, game day will bring more potential customers. The better the team is performing the more attendees a stadium would have, thus a higher demand for food and beverages. Can you attract them to your business? It will depend on your menu offerings, the time of the game, and many other factors, but it’s important to know when events such as this, concerts, festivals, etc. are happening in your local area. In sports arenas we would look at how well the team is doing in comparison to attendance.

Competition

What about a new restaurant opening in your area? Competition or lack thereof will also affect your forecasting. Did you gain or lose competition in your market set? A new chain restaurant will have a formula and model based on previous openings. A first time non-chain restaurant obviously will have no history to rely on and will need to just make a “best guess” on customers for the first couple of weeks or even months. In this case, you will need to have an estimate of your guest check average, and how many times you plan to turn over your tables. Breaking this down into days of the week and meal periods is recommended. You will typically have more table turns on the weekends than you will during the week. The entrance of new competition into your marketplace may be a time to consider some additional initiatives in your operation related to bringing in and retaining customers.

Operation Initiatives

Any new initiatives or programs implemented in your operation (or by a regional or national chain) are likely to affect your customer counts and forecasting. Most will hopefully increase the number of covers and perhaps the average check with the end result being higher sales/increased revenue. Marketing programs and promotional efforts are designed to increase guest counts, so these need to be taken into consideration when forecasting. Price changes, particularly price increases, can cause a decrease in customer counts or even a reduction in average check and should by carefully monitored, so this type of initiative is usually best paired with a marketing or promotional effort. Other initiatives that can affect forecasting include an improvement in service quality, facility renovations, or “green” initiatives, such as more sustainable sourcing, use of compostable supplies, etc.

Repeat group business

Group business can demonstrate reoccurring trends. In hotels for instance, there are repeat groups that often develop specific traits that can be used to more accurately forecast their spending habits. Some groups only eat at the hotel restaurant, while others rarely do. Some groups patronize the bar, while other groups do not. By gathering and recording all the information regarding group visits operations can more accurately forecast for future visits.

An example of understanding the dynamics of particular groups: One hotel that I worked at was the main hotel for the Arnold Schwarzenegger sports festival. The biggest names in body building and power lifting stayed there during this week long event. The restaurant could barely keep enough chicken breast and sweet potatoes in house. The amount of food consumed during that week was unreal. The chef carefully forecasted based on previous years in an attempt to have enough food in house. On the last day of the event, when most athletes already competed the menu changed again. Body builders that were eating clean and cutting weight for weeks, and months now ate pizza, burgers, and ice cream with reckless abandon. If the chef failed to acknowledge the difference in the quantity, and types of foods consumed by these athletes vs a typical group he would have some upset hungry athletes. Contributed by Mr. George Ruth

Segment focus: On-site foodservice

How does all this forecasting information apply to on-site foodservice segments of the industry? The basic principles are the same. Sales histories need to be maintained and food, beverages and labor all need to be forecasted. Since the customer base may be more consistent, things like labor hours scheduled are often more consistent as well. In fact many operations have unionized employees who are guaranteed a set schedule and a certain number of work hours each pay period. Segments in education such as K-12 and college and university foodservice operations need to look at school enrollments, the academic calendar, current participation rates, and even the exact menu offering of the day could change the forecast for food. Think pizza versus meatloaf (if it’s even served anymore.) If students don’t like a particular item they may elect to brown bag their meal or visit the convenience store on the college campus. The affluence of a school district and its residents may also affect the participation rate from one school to another. School policies can also affect forecasting. For example, does the school permit students to go off-campus to eat during lunch?

A foodservice operation housed within a corporation (business and industry dining) may also look more at participation rates (customer counts) instead of dollar amounts of revenue. Often corporations want to encourage their employees to “stay on campus” or in the building to reduce the amount of time workers are away from their desks. Health care foodservice operations use the “patient census” as the basis for forecasting patient meals, along with percentages typically on special diets, etc. Employee dining operations need to forecast for 24/7 availability of some sort of foodservice. Other on-site operations such as corrections, sporting venues, and convention centers each have their own considerations for forecasting, but on the whole, accurate forecasting is vitally important for all types of foodservice operations

Forecasting in the “big picture”

The better the forecasting, the more efficient and effective the foodservice operation. Whether it be predicting revenue, expenses, amount of food and beverage needed, or worker hours to be scheduled. Accurate forecasting also means more efficient production schedules, improved purchasing, maintenance of proper inventory levels and inventory turnover. Budgets are more accurate if long term forecasting is on target and this can lead to more dollars available for projects such as facility maintenance and growth of the operation. If a foodservice operation is effective at forecasting, profits can increase and the customer likely also gains from lower menu prices and better service. This chapter is just a brief introduction to the importance of forecasting and some of the factors that must be considered. Students of foodservice management will most certainly benefit from further study of forecasting methods, models and strategies.

Review Exercise 1

Review Exercise 2

License

Icon for the Creative Commons Attribution 4.0 International License

Introduction to Food Production and Service by Beth Egan is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

Share This Book