Sales Forecasting Methods

40
© OCTAVE Business School Sales Forecasting

description

Contains valuable methods on sales forcasting using excel

Transcript of Sales Forecasting Methods

Page 1: Sales Forecasting Methods

© OCTAVE Business School

Sales Forecasting

Page 2: Sales Forecasting Methods

© OCTAVE Business School

2

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

Page 3: Sales Forecasting Methods

© OCTAVE Business School

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

Page 4: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 5: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 6: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 7: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 8: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 9: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 10: Sales Forecasting Methods

© OCTAVE Business School

Time Series Data

Forecasting Approaches:

Smoothing

Trend Projections

Page 11: Sales Forecasting Methods

© OCTAVE Business School

Excel Instructions for Drawing a Scatter Plot

1. Enter data in the Excel spreadsheet.2. 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.

3. In step 2 specify the cells where your data is located in the data range box.4. 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.

Page 12: Sales Forecasting Methods

© OCTAVE Business School

During the past ten months, sales of cars of XYZ brand have been as follows:

Month Sales Month Sales 1 110 6 120 2 115 7 130 3 125 8 115 4 120 9 110 5 125 10 130

Plot this data.

Example: XYZ Car sales

Page 13: Sales Forecasting Methods

© OCTAVE Business School

Plot XYZ Car sales: Example

Excel Spreadsheet Showing Input Data. Specify cells A3:B12 as the Data Range.

Page 14: Sales Forecasting Methods

© OCTAVE Business School

XYZ Car Sales

Page 15: Sales Forecasting Methods

© OCTAVE Business School

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

Page 16: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 17: Sales Forecasting Methods

© OCTAVE Business School

XYZ Car Sales Example: Moving Average

Our 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).

Page 18: Sales Forecasting Methods

© OCTAVE Business School

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:

Enter B3:B12 in the Input Range box.

Enter 3 in the Interval box.

Enter C5 in the Output Range box.

Select OK.

This specifies the value of n

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

Page 19: Sales Forecasting Methods

© OCTAVE Business School

Moving Average

MA3 (Three period Moving average)

Page 20: Sales Forecasting Methods

© OCTAVE Business School

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.

Page 21: Sales Forecasting Methods

© 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= 122.5

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

Sales for themost recentperiod

Sales for 2nd most recentperiod

Sales for 3rd most recentperiod

Page 22: Sales Forecasting Methods

© OCTAVE Business School

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:

Ft+1= Yt + (1- )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

This is the same as Ft+1 = Ft + α (Yt – Ft)

Page 23: Sales Forecasting Methods

© OCTAVE Business School

Exponential Smoothing

Forecast the sales for month11 using Exponential Smoothing α= 0.1.

Page 24: Sales Forecasting Methods

© OCTAVE Business School

Exponential Smoothing

Steps 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 a = 0.1) in Damping Factor box.

Enter C4 in the Output Range box.

Select OK.

Damping factoris always 1-α

Page 25: Sales Forecasting Methods

© OCTAVE Business School

Exponential Smoothing

F11 = 0.1 * Y10 + .9 F10

= .1 *130 + .9 * 115.4099 = 116.87

Thus we would forecast sales for month11 to be 116.87

Page 26: Sales Forecasting Methods

© OCTAVE Business School

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 α 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.

Page 27: Sales Forecasting Methods

© OCTAVE Business School

Measures of Forecast Accuracy

Mean Squared Error (MSE)

The average of the squared forecast errors for the historical data is calculated. The forecasting method or parameter(s) which minimize this mean squared error is then selected.

Mean Absolute Deviation (MAD)

The mean of the absolute values of all forecast errors is calculated, and the forecasting method or parameter(s) which minimize this measure is selected. The mean absolute deviation measure is less sensitive to individual large forecast errors than the mean squared error measure.

You may choose either of the above criteria for evaluating the accuracy of a method (or parameter).

Page 28: Sales Forecasting Methods

© OCTAVE Business School

Selecting the best Smoothing Technique for XYZ car example

Determine the smoothing technique that is best for forecasting XYZ Car sales: A two period moving average, a three period moving average, exponential smoothing (α=0.1), or exponential smoothing (α=0.2)

Realistically we should have experimented with more values of n for the moving average, and α for exponential smoothing to determine the absolute best parameters to use for our technique.

On the next slide we randomly chose to use the MSE criterion to judge the best technique.

Page 29: Sales Forecasting Methods

© OCTAVE Business School

XYZ Car sales:Comparing Smoothing Techniques

MSE for MA2

Page 30: Sales Forecasting Methods

© OCTAVE Business School

XYZ Car Sales: Comparing Smoothing Techniques

MSE for MA3

Page 31: Sales Forecasting Methods

© OCTAVE Business School

Comparing Smoothing Techniques

MSE for ExponentialSmoothing α=0.1

Page 32: Sales Forecasting Methods

© OCTAVE Business School

Comparing Smoothing Techniques

MSE for ExponentialSmoothing α=0.2

Page 33: Sales Forecasting Methods

© OCTAVE Business School

Comparing Smoothing Techniques

Page 34: Sales Forecasting Methods

© OCTAVE Business School

Trend Projection

If a time series exhibits a linear trend, the method of least squares may be used to determine a trend line (projection) for future forecasts.

Least squares, also used in regression analysis, determines the unique trend line forecast which minimizes the mean square error between the trend line forecasts and the actual observed values for the time series.

The independent variable is the time period and the dependent variable is the actual observed value in the time series.

Page 35: Sales Forecasting Methods

© OCTAVE Business School

Trend Projection

Using the method of least squares, the formula for the trend projection is: Yt = b0 + b1t.

where: Yt = trend forecast for time period t b1 = slope of the trend line

b0 = trend line projection for time 0

b1 = n tYt - t Yt

nt 2 - (t )2

where: Yt = observed value of the time series at time period

t

= average of the observed values for Yt

= average time period for the n observations

0 1b Y b t

t

t Y

t

Page 36: Sales Forecasting Methods

© OCTAVE Business School

Example: ABC Car sales

ABC Car sales

Month Sales

Mar 353

Apr 387

May 342

Jun 374

Jul 396

Aug 409

Sep 399

Oct 412

Nov 408

Forecast the sales for Dec

Page 37: Sales Forecasting Methods

© OCTAVE Business School

Trend Line in Excel

Excel Spreadsheet Showing Input Data

ABC Car sales

Month Sales

1 353

2 387

3 342

4 374

5 396

6 409

7 399

8 412

9 408

Page 38: Sales Forecasting Methods

© OCTAVE Business School

Example: ABC Car sales

Steps to Trend Projection Using Excel

Step 1: Select an empty cell (B25) in the worksheet.

Step 2: Select the Insert pull-down menu.

Step 3: Choose the Function option.

Step 4: When the Select Category dialog box appears:Choose Statistical in Function Category box.Choose Forecast in the Function Name box.Select OK.

Step 5: When the Forecast dialog box appears:

Enter 10 in the x box (for month 10).

Enter B16:B24 in the Known y’s box.

Enter A16:A24 in the Known x’s box.

Select OK.

Page 39: Sales Forecasting Methods

© OCTAVE Business School

Example: ABC Car Sales

Spreadsheet Showing Trend Projection for Month 10

Page 40: Sales Forecasting Methods

© OCTAVE Business School

XYZ Car sales example

Suppose we neglected to plot trend line in XYZ car sales example, and therefore we do not know that a trend does not exist. Use trend analysis to forecast the sales for month 11.