In a restaurant, forecasting uses data to predict how much the company can expect in sales over a given period of time. At the macroeconomic level, sales forecasting helps a company set growth objectives and determine its overall profits and revenues. At the microeconomic level, forecasting helps a restaurant plan inventory orders and how many employees need to work each shift to prepare and sell food. An inaccurate sales forecast can result in wasted funds on labor, inventory, and even operating expenses for the restaurant.
Traditional restaurant sales forecasting involves analyzing past sales and predicting future sales based on information collected. Once you have an idea of a sales forecast for a given day of the week, you can plan all your variable costs based on it. These include ordering inventory, preparing food, and scheduling staff. In virtually every decision they make, today's executives consider some kind of forecast.
Accurate predictions of demands and trends are no longer luxury items, but a necessity if managers want to deal with seasonality, sudden changes in demand levels, competitive price reduction maneuvers, strikes and major changes in the economy. Forecasting can help them deal with these problems, but it can help them more the more they know about the general principles of forecasting, what it can and cannot do for them today, and the techniques that suit their current needs. Here, the authors try to explain the potential of forecasting to managers, paying special attention to the sales forecast of Corning Glass Works products, since they have matured throughout the product life cycle. A summary of forecasting techniques is also included.
Using past sales history in your food service operation is critical when trying to accurately forecast future sales. However, as the famous investment quote says, “past performance does not guarantee future results.”. Looking to the past is not enough to predict the future. There are too many variables that can positively or negatively affect our operation.
In this unit, we'll discuss several ways in which food service operations can attempt to forecast accurately. In a service company, for example, you can use a forecast to ensure that there are enough front desk employees to meet the fluctuating demand that often involves meeting immediate requests for customer service. For this reason, and since low-cost forecasting techniques, such as exponential smoothing and adaptive forecasting, do not allow the incorporation of special information, it is advantageous to also use a more sophisticated technique, such as X-11, for groups of elements. Once these factors and their relationships have been clarified, the prognosticator can build a causal model of the system that captures both the facts and the logic of the situation, which is, after all, the basis of a sophisticated prediction.
General managers are the people on the ground and they most likely know if any type of service interruption will be caused, whether due to an event, unforeseen weather, or due to construction work on the outer road. Both the director and the forecaster have a role to play in selecting the technique; and the better they understand the range of forecasting possibilities, the more likely a company's foresight efforts are to bear fruit. At these meetings, the decision to review or update a model or forecast is compared to the various costs and the amount of forecast error. Although the X-11 was not originally developed as a prediction method, it does establish a basis from which good forecasts can be made.
Changes in supply and demand for various foods can cause you to put your sales forecast back on the drawing board. How Tenzo creates hyper-accurate forecasts using machine learning to offer you the best tool for forecasting restaurant sales. However, short- and medium-term sales forecasts are fundamental to these more elaborate initiatives, and we will focus on sales forecasts. When historical data is available and sufficient analyses have been carried out to explain explicitly the relationships between the factor to be forecast and other factors (such as related companies, economic forces, and socioeconomic factors), the forecaster usually builds a causal model.
Because economic forecasts are becoming more and more accurate and also because there are certain general “main economic forces” that change before subsequent changes occur in specific sectors, it is possible to improve business forecasts by including economic factors in the forecast model. Keep in mind that weather forecasts are only accurate in the short term, so keep an eye on the forecasts to see if your staff's schedule needs last-minute changes. A manager generally assumes that when a forecaster is asked to prepare a specific projection, the request itself provides enough information for the forecaster to get to work and do the job. Other initiatives that may affect the forecast include improving the quality of service, the renovation of facilities or “green” initiatives, such as a more sustainable supply, the use of compostable supplies, etc.
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