Sales Forecasting Methods

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Transcript of Sales Forecasting Methods

Sales Forecasting

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FORECASTING TECHNIQUES

Qualitative Approaches to Forecasting Quantitative Approaches to Forecasting The Components of a Time Series Using Smoothing Methods in Forecasting Measures of Forecast Accuracy Using Trend Projection in Forecasting Using Regression Analysis in Forecasting

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Forecasting Introduction

An essential aspect of managing any organization is planning for the future. Organizations employ forecasting techniques to determine future inventory, costs, capacities, and interest rate changes and more importantly to forecast the sales. There are two basic approaches to forecasting: -Qualitative -Quantitative

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Qualitative Approaches to Forecasting Delphi Approach A panel of experts, each of whom is physically separated from the others and is anonymous, is asked to respond to a sequential series of questionnaires. After each questionnaire, the responses are tabulated and the information and opinions of the entire group are made known to each of the other panel members so that they may revise their previous forecast response. The process continues until some degree of consensus is achieved.

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Qualitative Approaches (continued)

Scenario Writing Scenario writing consists of developing a conceptual scenario of the future based on a well defined set of assumptions. After several different scenarios have been developed, the decision maker determines which is most likely to occur in the future and makes decisions accordingly.

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Qualitative Approaches (continued)

Subjective or Interactive Approaches These techniques are often used by committees or panels seeking to develop new ideas or solve complex problems. They often involve "brainstorming sessions". It is important in such sessions that any ideas or opinions be permitted to be presented without regard to its relevancy and without fear of criticism.

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Quantitative Approaches to Forecasting Quantitative methods are based on an analysis of historical data concerning one or more time series. A time series is a set of observations measured at successive points in time or over successive periods of time. If the historical data used are restricted to past values of the series that we are trying to forecast, the procedure is called a time series method. If the historical data used involve other time series that are believed to be related to the time series that we are trying to forecast, the procedure is called a causal method. Quantitative approaches are generally preferred. In this chapter we will focus on quantitative approaches to forecasting.

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Time Series Data

Time Series Data is usually plotted on a graph to determine the various characteristics or components of the time series data.

There are 4 Major Components: Trend, Cyclical, Seasonal, and Irregular Components.

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Components of a Time Series The trend component accounts for the gradual shifting of the time series over a long period of time. Any regular pattern of sequences of values above and below the trend line is attributable to the cyclical component of the series. The seasonal component of the series accounts for regular patterns of variability within certain time periods, such as over a year. The irregular component of the series is caused by short-term, unanticipated and non-recurring factors that affect the values of the time series. One cannot attempt to predict its impact on the time series in advance.

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Time Series Data

Forecasting Approaches:

Smoothing

Trend Projections

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Excel Instructions for Drawing a Scatter Plot1. 2. Enter data in the Excel spreadsheet. Click on Insert on the toolbar and then click on the Chart tab. The Chart Wizard will appear. In step 1 on select the XY (scatter) chart type and then click next. In step 2 specify the cells where your data is located in the data range box. In step 3 you can give your chart a title and label your axes. In step 4 specify where you want the chart to be placed.

3. 4.

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Example: XYZ Car salesDuring the past ten months, sales of cars of XYZ brand have been as follows:

Month Sales 1 110 2 115 3 125 4 120 5 125Plot this data.

Month Sales 6 120 7 130 8 115 9 110 10 130

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Plot XYZ Car sales: Example Excel Spreadsheet Showing Input Data. Specify cells A3:B12 as the Data Range.

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XYZ Car Sales

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Smoothing Methods In cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular components of the time series. Three common smoothing methods are:

Moving average Weighted moving average Exponential smoothing

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Smoothing Methods: Moving Average Moving Average Method The moving average method consists of computing an average of the most recent n data values for the series and using this average for forecasting the value of the time series for the next period.

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XYZ Car Sales Example: Moving AverageOur scatter plot for XYZ Car sales has no significant trend, seasonal, or cyclical effects. Thus we should employ a smoothing technique for forecasting sales. Forecast the sales for month11 using a three period moving average (MA3).

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Example: Moving Average Steps to Moving Average Using Excel

Step 1: Select the Tools pull-down menu. Step 2: Select the Data Analysis option. Step 3: When the Data Analysis Tools dialog appears, choose Moving Average. Step 4: When the Moving Average dialog box appears:This specifies the value of n

Enter B3:B12 in the Input Range box. Enter 3 in the Interval box. Enter C5 in the Output Range box. Select OK.

This is the column following our data, and one row below where our data begins.

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Moving Average MA3 (Three period Moving average)

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Smoothing Methods: Weighted Moving Average Weighted Moving Average Method The weighted moving average method consists of computing a weighted average of the most recent n data values for the series and using this weighted average for forecasting the value of the time series for the next period. The more recent observations are typically given more weight than older observations. For convenience, the weights usually sum to 1. The regular moving average gives equal weight to past data values when computing a forecast for the next period. The weighted moving average allows different weights to be allocated to past data values. There is no Excel command for computing this so you must do this manually. You can either manually enter the formulas into excel and apply to all periods or compute value by hand. OCTAVE Business School

Smoothing Methods: Weighted Moving Average Use a 3 period weighted moving average to forecast the sales for month11 giving a weight of 0.6 to the most recent period, 0.3 to the second most recent period, and 0.1 to the third most recent period. F11 = (0.6)*130 + (0.3)*110 + (0.1)* 115= 122.5Sales for the most recent period Sales for 2nd most recent period Sales for 3rd most recent period

Thus we would forecast the sales for week 11 to be 122.5.

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Smoothing Methods: Exponential Smoothing Exponential Smoothing Using exponential smoothing, the forecast for the next period is equal to the forecast for the current period

plus a proportion () of the forecast error in the current period. Using exponential smoothing, the forecast is calculated by: This is the same as Ft+1= Yt + (1- )Ft Ft+1 = Ft + (Yt Ft) where: is the smoothing constant (a number between 0 and 1) Ft is the forecast for period t Ft +1 is the forecast for period t+1 Yt is the actual data value for period t

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Exponential Smoothing

Forecast the sales for month11 using Exponential Smoothing = 0.1.

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Exponential SmoothingSteps to Exponential Smoothing Using Excel Step 1: Select the Tools pull-down menu. Step 2: Select the Data Analysis option. Step 3: When the Data Analysis Tools dialog appears, choose Exponential Smoothing. Step 4: When the Exponential Smoothing dialog box appears: Enter B4:B12 in the Input Range box. Enter 0.9 (for = 0.1) in Damping Factor box. Damping factor is always 1- Enter C4 in the Output Range box. Select OK.

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Exponential Smoothing

Thus we would forecast sales for month11 to be 116.87

F11 = 0.1 * Y10 + .9 F10 = .1 *130 + .9 * 115.4099 = 116.87

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Questions That You Should Be Asking For the Moving Average technique, how do I determine the best value of n to use for forecasting? For Exponential Smoothing, how do I determine the best value of to use? If I realize that a smoothing technique should be employed, how do you know which smoothing technique is best? In order to answer the above questions, we need criteria for judging the accuracy of a forecasting technique. Once we select a criterion, the method (or parameter) which provides the best value for our criterion is the best method (or parameter) to use for forecasting our scenario.

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Measures of Forecast Accuracy Mean Squared Error (MSE) The average of the squared forecast errors for