1Md. Moin Uddin (Mamun)Industrial and Production Engineer

BSC Eng. (SUST), MEng.(BUET)E-Mail Mamun_ipe@yahoo.com

Mob: 01730739318

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ForecastingLecture outline: Introduction Forecasting time horizons Types of forecasts Seven steps in the forecasting system Demand Patterns Forecasting approaches Qualitative approaches Quantitative approaches Measure of forecasting accuracy Example

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2Forecasting time horizonsShort range forecast: This forecast has a time span of up to 1 yearbut is generally less than 3 months.

It is used for planning, job scheduling, workforce levels, jobassignments and production levels.

Medium range forecast: A medium-range or intermediate,forecast generally spans from 3 months to 3 years.

It is used for sales planning, production planning andbudgeting, cash budgeting and analyzing various operating plans.

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IntroductionThe art and science of predicting future events.

Long range forecast: Generally 3 years or more in time span, long-range

forecasts are used in planning for new products, capital expenditures, facility

location or expansion and research and development

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3Types of forecasts1.Types of Forecasting in business:Organizations use the three major types of forecasts in futureoperations planning:

1.Economic forecasts address the business cycle by predictinginflation rates, money supplies, housing starts, and other planningindicators

2.Technological forecasts are concerned with rates of technologicalprogress, which can result in the birth of exciting new products requiringnew plants and equipment

3.Demand forecasts are projections of a companys products orservices it also called sales forecasts

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2.Types of Forecasting in Decision Making:Marketing9 Demand forecasting of products9 Forecast of market share9 Forecasting trend in prices

ProductionForecast of 9 Materials requirements 9 Trend in material and labor costs9 Maintenance requirements9 Plant capacity

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4FinanceForecast of 9 Cash flows9 Rates of expenses9 Revenues

PersonnelForecast of 9 Number of workers in each category9 Labor turn over9 absenteeism

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Seven steps in the forecasting system1) Determine the use of the forecast 2) Select items to be forecast3) Determine the time horizon of the forecast4) Select the forecasting models5) Gather the data needed to make the forecast6) Make the forecast7) Validate and implement the results

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5Demand PatternsForecasting is based on the pattern of events in the past. A patternmay solely exist as a function of time. Such a pattern can be identifieddirectly from historical data. Another pattern consists of relationshipbetween two or more variables. So it is important to understand themost common demand patterns.

Historical pattern (Stationary pattern): This exists when thereis no trend in data and when the mean value does not change overtime.

Example: Products with stable sales. Moving, weighted moving andexponential smoothing etc approaches are used.

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Fig: Historical pattern (Stationary pattern)

Fig: Level demand pattern

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Seasonal demand pattern: This demand pattern exists when the series fluctuates according to some seasonal factor. The season may be months, quarters, weeks, etc. sale of refrigerators, sale of refrigerators, sale of soft drink, sale of wool items, etc.

Cyclical pattern: In this type of pattern the length of a single cycle is longer than a year. This cycle does not repeat at constant intervals of time.The best examples are the prices of some metals, gross national product, etc.

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Fig: seasonal demand pattern

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Fig: seasonal demand pattern

6Trend pattern: This type of pattern exists when there is an increase or decrease in the value of the variable over time. The examples are sales many products, stock prices, business and economic indicators.

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timeFig: Cyclical pattern

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timeFig: Trend pattern

Forecasting Models / Approaches

1. Qualitative forecasts:It use subjective approaches. These are useful where no data isavailable and are useful new products. It forecasts thatincorporate such factors as the decision makers intuition,emotions, personal experiences and value systems.

2. Quantitative forecasts:It is based on historical data. These are more accurate andcomputers can be used to speed up the process. It forecasts thatemploy one or more mathematical models that rely on historicaldata and causal variables to forecast demand.

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7Qualitative methods1. Jury of executive opinion: A forecasting technique that takesthe opinion of a small group of high-level managers and results in agroup estimate of demand.

2. Delphi methods: A forecasting technique using a groupprocess that allows experts to make forecasts.

3. Sales forces composite: A forecasting technique based onsalespersons estimates of expected sales.

4. Consumer market survey: A forecasting method that solicitsinput from customers or potential customers regarding futurepurchasing plans.

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Quantitative MethodsTime series models: A forecasting technique that using a series of pastdate points to make a forecast.

Associative (or causal) models: such as linear regression, incorporatethe variables or factors that might influence the quantity being forecast.

Time series models are given below:1.Naive approach: A forecasting technique that assumes demand in thenext period is equal to demand in the most recent period.

2.Moving average: A forecasting method that uses an average of the nmost recent periods of data to forecast the next period.

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811/27/2013 15

Month Actual shed sales 3 month moving average

January 10

February 12

March 13

April 16 (10+12+13)/3=11.667May 19 (12+13+16)/3=13

June 23 (13+16+19)/3=16

July 26 (16+19+23)/3=19

August 30 (19+23+26)/3=22

September 28 (23+26+30)/3=26

October 18 (26+30+28)/3=28November 16 (30+28+18)/3=25

December 14 (28+18+16)/3=20

Month Actual shed sales 3 month Weighted moving average

January 10

February 12

March 13

April 16 (.2*10+.3*12+.5*13)/1=12.1

May 19 (.2*12+.3*13+.5*16)/1=

June 23 (.2*13+.3*16+.5*19)/=

July 26 (.2*16+.3*19+.5*23)/1=

August 30 (.2*19+.3*23+.5*26)/1=

September 28 (.2*23+.3*26+.5*30)/1=

October 18 (.2*26+.3*30+.5*28)/1=

November 16 (.2*30+.3*28+.5*18)/1=

December 14 (.2*28+.3*18+.5*16)/1= 11/27/2013 16

9To be continue

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4.Exponential smoothingA weighted moving average forecasting technique in which data points are weighted by an exponential function.Formula : New forecast = last periods forecast + (Last periods actual demand Last periods forecast)Where is a weight, or constant , chosen by the forecaster, that has a value between 0 and 1.

Now Ft = Ft-1 + ( At-1 Ft-1)Where Ft = New forecastFt-1 = Previous forecast,At-1 = Previous periods actual demandand = smoothing ( or weighting) constant ( 0 1) If is not given than = 2 / (n+1) [n = no of period ]

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ExampleIn January , a car dealer predicted February demand for 142 Ford Mustangs. Actual February demand was 153 autos. Using a smoothing constant chosen by management of = .20, we can forecast March demand using the exponential smoothing model.

Answer: New forecast (for March demand) = 142 + .2( 153-142)= 142+2.2= 144.2Thus, the March demand forecast for Ford Mustangs is rounded to 144.

Note: the smoothing constant, , is generally in the range from .05 to .50 for business applications.

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Weight Assigned to

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Smoothing constant

Most recent period)

2nd Most recent period(1-)

3rd Most recent period(1-)2

4th Mostrecent period(1-)3

5th Most recent period (1-)4

= 0.1 0.1 0.09 0.081 0.073 0.066

= 0.5 0.5 0.25 0.125 0.063 0.031

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5.Linear RegressionRegression means dependence and involves estimating the value of a dependent variable Y, from an independent variable X. In simple regression, only one independent variable is used, whereas in multiple regression two or more independent variable are involved. The simple regression takes the following form

Y = a + bXWhere Y - dependent variableX independent variablea interceptb slope (trend)

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These are represented in the following graph:

[The model for multiple linear regression is shown below:Y = a + b1=X1 + b2X2+ b3X3 + biXi + + bnXn ]

The formulas to compute the constants of the model are given belowb = Which is written by hand.a = Which is written by hand.

And n is the umber of pairs of observations made.

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Y

Xa

b

Fig: Graph showing simple regression

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ExampleA firm believes that its annual profit depends on its expenditures for research. The information for the preceding six years is given below. Estimate the profit when the expenditure is 6 units.

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Year Expenditure for research (X) Annual profit (Y)

1 2 20

2 3 25

3 5 34

4 4 30

5 11 40

6 5 31

7 6 ?

Solution:

X bar = 30/6 =5 and Y bar = 180/6 = 30

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Year X Y XY X2

1 2 20 40 4

2 3 25 75 9

3 5 34 170 25

4 4 30 120 16

5 11 40 440 121

6 5 31 155 25

Total 30 180 1000 200

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b = 2And a = 20The model is :Y = a + bX

= 20 + 2XThe profit when expenditure is 6 units:Y = 20 + 2 * 6

= 32

Measure of forecasting accuracySome common measures are inevitable to measure the accuracy of a forecasting technique. This measure may be an aggregate error (deviation) of the forecast values from the actual demands. The different types of errors which are generally computed are as presented below.1. Mean Absolute Deviation (MAD)2. Mean Square Error (MSE)3. Mean Forecast Error (MFE)4. Mean Absolute Percent Error(MAPE)

The formula for error is given below.et = Dt Ft

WhereDt = demand for the period t.Ft = forecast demand for the period t, andet = forecast error for the period t.

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Mean Absolute Deviation (MAD)

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Mean Square Error (MSE)

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Mean forecast error (MFE)

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ExampleSuppose that a forecast of 165 units had been made for the demand in every period for the data given in table. The calculation of these errors are as shown in the last two columns of table.

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t DemandDt

ForecastFt

DeviationDt - Ft

Absolute DeviationDt - Ft

SquaredError(Dt Ft ) 2

1 150 165 -15 15 225 -10 10

2 160 165 -5 5 25 -3.125 3.125

3 165 165 0 0 0 0.00 0.00

4 175 165 +10 10 100 5.710 5.71

5 180 165 +15 15 225 8.330 8.330

Total +5 45 575 27.165

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MAD = 45/5 = 9

MSE = 575/5 = 115

MAPE = 27.165/5 = 5.433%

MFE = +5 / 5 = +1

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Thanks to all

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