Applied Business Forecasting and Planning Introduction To Business Forecasting

Chapter Overview Introduce Business Forecasting. Review Subjective Forecasting Methods. Introduce two simple “Naïve” Forecasting Models. Develop Criteria for Evaluating Forecast Accuracy

Introduction What is forecasting? “Forecasting is an attempt to foresee the future by examining the past.” Forecasts require judgment Most estimates obtained in quality forecasting are derived in an objective and systematic fashion and do not depend solely on subjective guesses and hunches of the analyst. It consists of generating unbiased estimates of the future magnitude of some variable, such as sales, on the basis of past and present knowledge and experience. The essence of forecasting is estimating future events according to past patterns. Mere extensions of past sales tendencies into the future do not constitute forecasting. Projections are numerical manipulation of functions; forecasts require judgment.

Forecasting and Decision Making Example: Decision to “Whether to build a new factory” Requires forecasts on Future demand, technological innovations, cost, prices, competitor’s plan, labor, legislation, etc. Most forecasting required for decision making is handled judgmentally in an intuitive fashion, often without separating the task of forecasting from that of decision making. In this course we learn how to make a forecast through systematic, and explicit procedures. We plan to cover a collection of these techniques and illustrate their weakness and strength and learn how to select and apply individual methods.

Forecasting and Decision making Systematic, explicit approaches to forecasting can be used to supplement the common sense and management ability of decision makers. All types and forms of forecasting techniques are extrapolation, that is, predicting within the existing data. Quantitative forecasting techniques should be used in with analysis, judgment, common sense, and business experience in order to produce an effective forecasting outcome. There is no simple, reliable way to predict what will happen when established pattern or relationships changes, But By knowing the factors that affect the outcome of interest we can identify the systematic change more quickly and interpret better the effect of such change on the future. It is important to remember that sound Judgment should be used along with numerical results in order to have a good forecasting results.

Advantages and Disadvantages of Subjective Methods Subjective (qualitative or judgmental) forecasting methods are sometimes considered desirable because they do not require any particular mathematical background of the individuals involved. Subjectivity is the most important advantage of this class of methods. There are often forces at work that cannot be captured by quantitative methods. They can can however, be sensed by experienced business professionals and can make an important contribution to improve forecasts.

Advantages and Disadvantages of Subjective Methods The disadvantages of subjective methods are: They are almost always biased They are not consistently accurate overtime. It takes yeas of experience for someone to learn how to convert intuitive judgment into good forecast.

Who needs forecasts? Every organizations, large and small, private and public. Needs for forecasts cuts across all functional lines. It applies to problems such as: How much this company worth? (Finance) Will a new product be successful? (Marketing) What level of inventories should be kept? (Production) How can we identify the best job candidates? (Personnel) All organizations that must plan to meet the needs of the future for which it has incomplete knowledge. Forecasts are needed in finance, marketing, personnel, and production.

Naïve Method 1 It is based solely on the most recent information available. Suitable when there is small data set. Some times it is called the “no change” forecast. The naïve forecast for each period is the immediately proceeding observation.

Naïve Method 1 The simplest naïve forecasting model, in which the forecast value is equal to the previous observed value, can be described in algebraic form as follows: Since it discards all other observations, it tracks changes rapidly.

Example:Sales of saws for Acme Tool Company,1994-2000 The following table shows the sales of saws for the Acme tool Company. These data are shown graphically ass well. In both forms of presentation you can see that the sales varied considerably throughout this period, from a low of 150 in 1996Q3 to a high of 850 in 2000Q1. The Fluctuations in most economic and business series (variables) are best seen after converting the data graphic form.

Example:Sales of saws for Acme Tool Company,1994-2000

Example:Sales of saws for Acme Tool Company,1994-2000

Example:Sales of saws for Acme Tool Company,1994-2000 The forecast for the first quarter of 2000 , using the naïve method is:

Example:Sales of saws for Acme Tool Company,1994-2000

Example:Sales of saws for Acme Tool Company,1994-2000

Naïve Method 2 One might argue that in addition to considering just the recent observation, it would make sense to consider the direction from which we arrived at the latest observation. That is: if the series dropped to the latest point, perhaps it is reasonable to assume further drop and if we have observed an increase, it may make sense to factor into our forecast some further increase.

Naïve Method 2 In general algebraic terms the model becomes Where P is the proportion of the change between period t-2 and t-1 that we choose to include in the forecast. We call this Naïve method(2).

Example:Sales of saws for Acme Tool Company,1994-2000 The forecast for the first quarter of 2000 using the Naïve method(2) with P = 50% is:

Example:Sales of saws for Acme Tool Company,1994-2000

Example:Sales of saws for Acme Tool Company,1994-2000

Evaluating Forecasts We have looked at two alternative forecasts of the sales for the Acme Tool Company. Which forecast is best depends on the particular year or years you look at. It is not always possible to find one model that is always best for any given set of business or economic data. But we need some way to evaluate the accuracy of forecasting models over a number of periods so that we can identify the model that generally works the best.

Evaluating Forecasts Among a number of possible criteria that could be used, five common ones are: Mean absolute error (MAE) Mean percentage error (MPE) Mean absolute percentage error (MAPE) Mean squared Error (MSE) Root Mean squared Error (RMSE) Theil’s U-statistic

Evaluating Forecasts To illustrate how each of these is calculated, let yt = Actual value in period t = Forecast value in period t n = number of periods used in the calculation

Mean Absolute Error The mean absolute error (MAE) Measures forecast accuracy by averaging the magnitudes of the forecast errors.

Mean Percentage Error The Mean Percentage Error (MPE) Can be used to determine if a forecasting method is biased (consistently forecasting low or high) Large positive MPE implies that the method consistently under estimates. Large negative MPE implies that the method consistently over estimates.

Mean Percentage Error The forecasting method is unbiased if MPE is close to zero.

Mean absolute Percentage Error The Mean Absolute Percentage Error (MAPE) Provides an indication of how large the forecast errors are in comparison to actual values of the series. Especially useful when the yt values are large.

Mean absolute Percentage Error Can be used to compare the accuracy of the same or different methods on two different time series data.

Mean Squared Error This approach penalizes large forecasting errors.

Root Mean Squared Error The RMSE is easy for most people to interpret because of its similarity to the basic statistical concept of a standard deviation, and it is one of the most commonly used measures of forecast accuracy.

Theil’s U-statistic This statistic allows a relative comparison of formal forecasting methods with naïve approaches and also squares the errors involved so that large errors are given much more weight than smaller errors. Mathematically, Theil’s U-statistic is defined as

Theil’s U-statistic U = 1 The naïve method is as good as the forecasting technique being evaluated. U < 1 The forecasting technique being used is better than the naïve method. U > 1 There is no point in using a formal forecasting method since using a naïve method will produce better results

Example:VCR data Data was collected on the number of VCRs sold last year for Vernon’s Music store.

Example:VCR data

Example:VCR data

Example:VCR data

Example:VCR data Error analysis: RMSE Forecast (N1) 7.24

Evaluating Forecasts We will focus on root-mean-squared error (RMSE) to evaluate the relative accuracy of various forecasting methods. All quantitative forecasting models are developed on the basis of historical data. When RMSE are applied to the historical data, they are often considered measures of how well various models fit the data (how well they work in the sample).

Evaluating Forecasts To determine how accurate the models are in actual forecast (out of sample) a hold out period is often used for evaluation. It is possible that the best model “in sample” may not be the best in “out of sample”.

Using Multiple Forecasts When forecasting Sales or some other business economic variables, it is best to consider more than one model. In our example of VCR sales, using the two naïve model, we could take the lowest forecast value as the most pessimistic, the highest as the most optimistic, and the average value as the most likely. This is the simplest way to combine forecasts. Since it is unlikely that one model will always provide the most accurate forecast for any series. (8.97+7.24)/2=8.1 is the most likely Monthly VCR sales.

Sources of Data The quantity and type of data needed in developing forecasts can vary a great deal from one situation to another. Some forecasting techniques require only data series that is to be forecasted Naïve method, exponential smoothing, decomposition method. Some , like multiple regression methods require a data series for each variable included in the forecasting model.

Sources of Data Sources of data Internal records of the organization. Outside of the organization Trade associations Governmental and syndicated services There is a wealth of data available on the internet.