By definition, a forecast is not a guess, a vision, or a speculation about the future, but is an expectation derived from analyzing past performance and drawing conclusions about the future. Brought to you by CrunchTime!
By Matthew Klyman
What is forecasting?
Building business plans require both knowledge and expectations about future events. Long term planning includes product lines, concepts, and locations for operation. Many of the key decisions that business leaders at all levels of operations face include beliefs and prospects for near term future events. In both retail and restaurant locations, these key decisions include the amount and type of inventory that should be purchased and held and how many people will be needed to meet customer demand and provide acceptable levels of surface coverage. To make these types of decisions, managers must plan based upon a forecast.
Merriam-Webster Online defines a forecast as calculating or predicting some future event “usually as a result of study and analysis of available pertinent data”. By definition, a forecast is not a guess, a vision, or a speculation about the future, but is an expectation derived from analyzing past performance and drawing conclusions about the future.
In either retail stores or restaurants, forecasts can include expectations about sales, traffic, average ticket, items per transaction, promotional costs, etc.
What are the most common approaches?
Managers can generate a forecast through formal or informal processes and use either qualitative or quantitative and data-driven methodologies. For example, a manager of a newly opened location may rely on intuition, drawing parallels to existing locations, using a budget or plan from the real estate team, etc. Other approaches use expert opinion in either informal or more formal models like Delphi Method. These approaches are especially useful when locations either lack history, or have experienced significant changes to their operating environments. For example, a location that was dependent upon traffic flow will be impacted by construction work that may reroute traffic. Using historical data may provide incorrect inferences about future sales and activities.
For locations that have been opened for a period of time, typically at least one year, there are several approaches to forecasting available. These approaches rely upon historical data at the correct level for predictions. For example, regional managers want to predict sales for a week, while site level GM’s are forecasting sales by day. If accurate data is not available, the qualitative approaches discussed above are best used.
There are two quantitative approaches. One forecasts future events and results by using underlying causational data. For example, a restaurant near a convention center knows that its sales highly correlate to attendance at conventions. In this example, the manager can take estimates of attendance and translate them into a forecast.
If highly correlated information such as convention counts, hotel fill rates, train schedules, etc. are not available or reliable, a second approach exists that uses historical sales information to predict/forecast future sales. These approaches can look at selling history directly or can also add trends and seasonality to forecasts to increase accuracy. The use of sales history to directly determine forecast is commonly called Time Series Approach. This method can range from the very simple approach of using last week’s sales to forecast this week to methods that weigh weeks differently to models that include both seasonal and current trend information.
Why Forecast – Risks and Benefits
Restaurant and retail locations share a number of attributes. Both businesses succeed based upon the acceptance and ongoing support of direct use customers. Typically, many of those customers are also repeat customers who repeatedly select to frequent a location because both the products offered and the level of service meet or exceed expectations. In some instances, 80% of a retail or restaurant’s business comes from 20% of its customer base (the Pareto Principle). To drive return business, customers need to know that they will find the products and menu items they expect and desire with a level of service that exceeds their expectations. To accomplish both in stock positions for inventory and the right level of service, purchases or distributions must be completed in a timely manner and the staff must be properly scheduled to work when customers seek service. Both of these management functions depend on the quality of a forecast to ensure high levels of service.
In a restaurant location, raw inventory turns (is used and replaced) at least once a week. Restaurant managers need to order their deliveries in advance to hold the right list of products for menu items. Thus, a daily forecast for a week in advance would be required to complete the ordering for products. In order to maintain service levels, the forecast not only needs to exist at a daily level, but by day part to correctly schedule needed service and culinary staff.
Forecasts that consistently are too high or too low will diminish a location’s ability to deliver goods and services as needed.
Should you Use Qualitative or Quantitative Approaches?
There are two broad approaches to developing a forecast. The first type uses sales history, recent trends, correlated information, etc., to create a forecast. The second approach relies on expert opinions, drawing analogies to similar locations, etc. If no valid data can be used because a location is too new or the sales data may be inaccurate or suspect, qualitative approaches must be used. A second reason to rely more heavily on qualitative insights is that historical data and recent trends may not create a pattern of reliable results.
In this example (Example 1 above), there is consistency in the pattern of sales both by day of the week and by year over year trend. However, if patterns are not discernible and underlying causes of daily fluctuations are dependent upon weather, local events, etc. a qualitative approach may produce better results (Example 2 below).
Preparing to Forecast
Whether you use a sophisticated statistical approach or an expert opinion, there are some key preparations that should be completed as the General Manager or home office prepare a forecast. First, sales history should be reviewed to observe any potential aberrations that would create false or misleading results. For example, sales from a year ago could have been impacted by a major weather event, like Hurricane Sandy. The location may have been closed or operating with very limited hours of operation. Similarly, there may have been a special event like a celebration of a sports championship. Reviewing a graph of last year’s sales (like Examples 1 and 2 above) may be the easiest to find these events. To adapt to the random nature of these events, you can simply use the week before or after to replace the week of sales data with the anomaly. There are also more advance statistical corrections available.
With data and some history handy, the forecast itself should account for:
- The current trend of the business;
- Potential impact of the weather on sales;
- Planned promotions by your restaurant and local and direct competitors;
- Impact of local events like elections, etc.
Who Should Complete the Forecast?
Forecast Cycles and Frequency - The benefits of developing an accurate forecast have been reviewed earlier are, at the most basic level, the best way to predict both product and labor needs to meet and exceed customer demand and expectations. While product shipments may only occur once to twice a week, labor needs are based upon expected daily sales and traffic. Given that both food and labor costs are based upon daily sales and activity, the forecasts need to operate on a daily basis.
Both common sense and statistical reality point to the impact of random events and anomalies as a primary cause of forecasting errors. Forecasting errors among items in a group usually have a canceling effect. While the impact of weather may affect a single day’s forecast, overall weather patterns are not likely to change year over year at the weekly or monthly levels. This reality supports the idea of generating monthly and then weekly forecasts for a location to use a guideline for daily forecasts. But, who’s going to do this?
The Case for Home Office or Area Ownership of Forecasting
The key question for the person tasked with developing or adjusting the forecast is how to use historical data and which data to use. Traditionally, restaurant GM’s and retail store managers do not possess sophisticated skills in data analysis, nor are these types of skills correlated with exceptional performance at the store level. Good managers excel in their abilities to maintain order, recruit and motivate employees, etc. Great data analytical skills are not essential and, in fact, may be counterproductive because the GM would be motivated to analyze business data before managing the staff and surroundings. As a blogger on LinkedIn once noted “you cannot make money by sitting in the office all day.” Home office staff, accountants, brand managers, site analysts, etc. have both experience and a strong aptitude towards creating financial models. The team that develops the forecasts can include a range of daily numbers based upon weather conditions or other known events. They can also use self-correcting approaches that look at which methods would have generated the best fit for the preceding two week and use that method rolling forward. Finally, home office or multi-unit staff has the benefit of looking at the trends of overall business and the fit of both recent sales and year over year sales in developing the forecast.
The Case for the GM or Store Manager to Forecast
Local managers have two overriding reasons to want to either develop or modify forecasts. The first is about owning your business, its future, and its outcomes and profitability. Managers are held accountable for delivering profitability, not just sales. By creating a realistic forecast and managing product purchases, display needs, production, and selling labor, a manager can achieve profit goals even in trying sales periods. Owning the forecast and reacting to needs quickly can ensure profits.
The second rationale for local ownership is directly related to the knowledge and additional business intelligence and context that a manager offers. An experienced manager will look at variables such as new competitors, weather, local business openings and closings, road work impact, etc. both as they are planned and as they may have impacted historical results. Home Office staff may note a decline in business, but not be aware of causative factors. The GM should know the underlying causes and how they may change and impact upcoming sales.
Forecasts must be Flexible
Whether the forecast was generated by home office or the general manager, real world events will intervene to alter the underlying assumptions of the planner. A predicted major weather event will certainly change any plans that were generated before the event was evident. GMs must adjust their forecasts on a daily basis to gain the positive impacts they desire. Whether a significant change (20% or more) is going to increase or decrease expected sales, this latest information needs to be incorporated into the labor plan, product purchasing, as well as the production and display plan for the day. This can avoid unneeded labor costs and waste or allow a manager to maximize the potential upside of a much better than initially planned sales event.
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For the best restaurant forecasting available, go to www.crunchtime.com and learn more.
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CrunchTime is proud to present this article written by Matthew Klyman, an expert senior level executive and independent consultant with a 25-year proven track record in key positions in Restaurants, Food Service, Retail, Wholesale and Distribution Organizations. Mr. Klyman has brought significant value to clients and employers as a trusted consultant and “C” level officer. He has developed and implemented strategic plans and initiatives for clients to increase sales and profitability by 50%. His expertise in process reengineering in retail and restaurant companies has reduced labor and inventory costs for clients while supporting their sales gains. If you wish to contact Matthew, reach him at email@example.com or +1 (201) 951-5146 or follow him on twitter @thunderft1