Forecasting Techniques

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FORECASTING TECHNIQUESChapter 16 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

Dr. C. Lightner Fayetteville State University

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Forecasting IntroductionAn 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. There are two basic approaches to forecasting: -Qualitative -Quantitative

Dr. C. Lightner Fayetteville State University

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Qualitative Approaches to ForecastingDelphi 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.

Dr. C. Lightner Fayetteville State University

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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.

Dr. C. Lightner Fayetteville State University

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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.

Dr. C. Lightner Fayetteville State University

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Quantitative Approaches to ForecastingQuantitative 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.

Dr. C. Lightner Fayetteville State University

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Time Series DataTime 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.

Dr. C. Lightner Fayetteville State University

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Components of a Time SeriesThe 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.Dr. C. Lightner Fayetteville State University 8

Time Series DataWe will learn the following Forecasting Approaches: Smoothing Trend Projections

Dr. C. Lightner Fayetteville State University

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Excel Instructions for Drawing a Scatter Plot1. 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.

Dr. C. Lightner Fayetteville State University

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Example: Roberts DrugsDuring the past ten weeks, sales of cases of Comfort brand headache medicine at Robert's Drugs have been as follows: Week Sales 1 2 3 4 5 Week 110 115 125 120 125 Sales 6 7 8 9 10 120 130 115 110 130

Plot this data.

Dr. C. Lightner Fayetteville State University

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Plot Roberts Drugs ExampleExcel Spreadsheet Showing Input Data. Specify cells A4:B13 as the Data A Range. B1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 R bert'sD g o ru s W eek ( 1 2 3 4 5 6 7 8 9 1 0 1 1 t) S ales 10 1 15 1 15 2 10 2 15 2 10 2 10 3 15 1 10 1 10 3t

Dr. C. Lightner Fayetteville State University

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Plot Roberts Drugs ExampleRobert's Drug Example135 130 Sales 125 120 115 110 105 0 5 Week, tDr. C. Lightner Fayetteville State University

I labeled Roberts Drug Example as The Chart title

I labeled Sales as My Value (y) axis

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I labeled Week, t as My Value (x) axis13

Smoothing MethodsIn 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

Dr. C. Lightner Fayetteville State University

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Smoothing Methods: Moving AverageMoving 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.

Dr. C. Lightner Fayetteville State University

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Robert Drugs Example: Moving AverageOur scatter plot for Roberts Drug Sales has no significant trend, seasonal, or cyclical effects. Thus we should employ a smoothing technique for forecasting sales. Forecast the sales for period 11 using a three period moving average (MA3).

Dr. C. Lightner Fayetteville State University

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Example: Roberts Drugs: 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 B4:B13 in the Input Range box. This specifies the value of n Enter 3 in the Interval box. Enter C5 in the Output Range box. This is the column following our data, and one row below where Select OK.our data begins.Dr. C. Lightner Fayetteville State University 17

Roberts Drugs: Moving AverageMA3 (Three period Moving average) for Roberts Drug Example

Robert's DrugWeek (t ) 1 2

Ft is the forecast for week t.

F4 (forecast for week 4)=116.7

F11 (forecast for week 11)=118.3Thus we would forecast the sales for Week 11 to be 118.318

Dr. C. Lightner Fayetteville State University

Smoothing Methods: Weighted Moving AverageWeighted 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.Dr. C. Lightner Fayetteville State University 19

Smoothing Methods: Weighted Moving AverageUse a 3 period weighted moving average to forecast the sales for week 11 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.

Dr. C. Lightner Fayetteville State University

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Smoothing Methods: Exponential SmoothingExponential 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 where: Ft+1 = Ft + (Yt Ft) is the smoothing constant (a number between 0 and 1)

Ft+1 = Yt + (1- )Ft

Ft is the forecast for period t Ft+ is the forecast for period t+1 1 Yt is the actual data value for period tDr. C. Lightner Fayetteville State University 21

Roberts Drugs: Exponential SmoothingForecast the sales for period 11 using Exponential Smoothing = 0.1.

Dr. C. Lightner Fayetteville State University

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Roberts Drugs: Exponential SmoothingSteps to Exponential Smoothing Using ExcelStep 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 Exponent