1 Mks Forecasting

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Transcript of 1 Mks Forecasting

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What is Forecasting?What is Forecasting?
Process of predicting aProcess of predicting afuture eventfuture event
Underlying basis ofUnderlying basis ofall business decisionsall business decisions ProductionProduction
InventoryInventory
PersonnelPersonnel
FacilitiesFacilities
??
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Demand Management
A
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand:
Raw Materials,
Component parts,Subassemblies, etc.
Independent Demand:Finished Goods
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Independent Demand:
What a firm can do to manage it?
Can take an active role to influence
demand
Can take a passive role and simply
respond to demand
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Shortrange forecast Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce levels, jobassignments, production levels
Mediumrange forecast 3 months to 3 years
Sales and production planning, budgeting
Longrange forecast
3+ years New product planning, facility location, research and
development
Forecasting Time HorizonsForecasting Time Horizons
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Distinguishing DifferencesDistinguishing Differences
Medium/long range forecasts deal with more
comprehensive issues and support management
decisions regarding planning and products, plants
and processes
Shortterm forecasting usually employs different
methodologies than longerterm forecasting
Shortterm forecasts tend to be more accuratethan longerterm forecasts
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Types of ForecastsTypes of Forecasts
Economic forecasts
Address business cycle inflation rate, money
supply, housing starts, etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts Predict sales of existing products and services
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The RealitiesThe Realities
Forecasts are seldom perfectForecasts are seldom perfect
Most techniques assume an underlying stabilityMost techniques assume an underlying stabilityin the systemin the system
Product family and aggregated forecasts areProduct family and aggregated forecasts aremore accurate than individual product forecastsmore accurate than individual product forecasts
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Simple Moving Average
Ft = At 1 +At 2 +At 3+..Atn
n
Ft= Forecast for the coming period At 1 = Actual value in period t 1
n = Number of periods to be averaged
At 2,At 3 ,Atn Actual occurrences two periods ago ,three
periods ago and so on up to n periods ago
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JanuaryJanuary 1010
FebruaryFebruary 1212MarchMarch 1313
AprilApril 1616
MayMay 1919
JuneJune 2323
JulyJuly 2626
ActualActual 33MonthMonthMonthMonth Shed SalesShed Sales Moving AverageMoving Average
(12 + 13 + 16)/3 = 13(12 + 13 + 16)/3 = 13 22//33(13 + 16 + 19)/3 = 16(13 + 16 + 19)/3 = 16
(16 + 19 + 23)/3 = 19(16 + 19 + 23)/3 = 19 11//33
Moving Average ExampleMoving Average Example
1010
12121313
((1010 ++ 1212 ++ 1313)/3 = 11)/3 = 11 22//33
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Three period Moving Average
If actual demand in period 6 turns out tobe 38, the moving average forecast for
period 7 would be
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Three Period Moving Average
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Weighted Moving Average
The formula for a weighted moving average
Ft = w At 1 + w At 2+.. + wnAt n
w= weight to be given to actual occurrence for the periodt1
w= weight to be given to actual occurrence for the
period t2wn = weight to be given to actual occurrence for theperiod tn
n =Total Number of Periods in the forecast .
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Weighted Moving Average
Although many periods may be ignored (that is theirweights are zero ) and the weighting scheme may be inany order (for e.g. more distant data may have greater
weights than more recent data) the sum of all theweights must be equal to 1.
Choosing Weights Experience and trial and error arethe simplest ways to choose weights .As a general rule,the most recent past is the most important indicator ofwhat to expect in the future and therefore it should get ahigher weighting .
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Illustration
Given the following demand data, a) Computeaweighted
averageforecastusingaweight of .40 forthemostrecent
period, .30 forthenextmostrecent, .20 forthenext,and .10
forthenext. b)If theactual demand forperiod 6 is 39,
forecast demand forperiod 7using thesameweightsasin
parta.
Period 1 2 3 4 5
Demand 42 40 43 40 41
Solution: 
F6 =.10(40) +.20(43) +.30(40) +.40(41) = 41.0
F7 =.10(43) +.20(40) +.30(41) +.40(39) = 40.2
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Moving Average AndMoving Average AndWeighted Moving AverageWeighted Moving Average
3030
2525
2020
1515
1010
55
Salesdemand
Salesdemand
           
JJ FF MM AA MM JJ JJ AA SS OO NN DD
ActualActualsalessales
MovingMovingaverageaverage
WeightedWeightedmovingmovingaverageaverage

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Form of weighted moving average
Weights decline exponentially
Most recent data weighted most Requires smoothing constant (E)
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of past data
Exponential SmoothingExponential Smoothing
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Illustration
A department store may find that in a four month period the
best forecast is derived using 40percent of the actual sales
for the most recent month ,30 percent of two months ago
,20 percent of three month ago and 10 percent of four
month ago .If actual sales experience was
Month 1 Month2 Month3 Month4
100 90 105 95 ?
The forecast for month 5 would be
F5=0.40 (95)+0.30(105)+0.20(90)+.10(100)=
=38+31.5+18+10
=97.5
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Exponential dependencies.
Many processes in nature have exponential
dependencies .The decay with time of the amplitudeof a pendulum swinging in air ,the decrease in time
of the temperature of an object that is initially
warmer than its surroundings and the growth in time
of an initially small bacterial colony are all processesthat are well modelled exponential relationships .
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Y=exp(x)
.
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Exponential Smoothing 1
Ft = Ft 1 + (At 1  Ft 1)
Ft =Exponentially smoothed forecast for Period t
Ft 1 =Exponentially smoothed forecast for Period t1
= Smoothing constant ,
At 1 = Actual demand in the prior period
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Exponential Smoothing 2
The smoothing constant represents a percentage of the
forecast error.Each newforecastisequal to theprevious
forecastplusapercentage of thepreviouserror.
For example, suppose the previous forecast was 42 units,actual demand was 40 units, and =.10. The new forecast
would be computed as follows:
Ft=42+0.10(4042)=41.8
Then,if theactual demand turns out to be 43, thenext
forecastwould be
Ft= 41.8 +0.10(4341.8)=41.92
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Illustration
The long run demand for the product under study isrelatively stable and a smoothing constant (alpha) of 0.05 isconsidered appropriate .If the exponential method wereused as a continuing policy a forecast would have been
made for last month .Assume that last months forecast Ft 1was 1050 units .If1000 actually were demanded rather than1050 ,what would be the the forecast for this month ?
Ft = Ft 1 + (At 1  Ft 1)
=1050+0.05(10001050)
=1050+0.05(50)
=1047.5 units
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Exponential Smoothing 3
Higher the value of alpha ,the more closely the forecast
follows the actual .
Single exponential smoothing has the shortcoming of lagging
changes in demand .
To more closely track actual demand ,a trend factor may beadded .Adjusting the value of alpha also helps .
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Impact ofDifferent Alpha
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Forecast Accuracy
Three commonly used measures for summarizing historical
errors are;
1. The mean absolute deviation (MAD)2. The mean squared error (MSE)
3. The mean absolute percent error (MAPE).
The formulas used to compute MAD, MSE, and MAPE are asfollows:
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Common Measures ofErrorCommon Measures ofError
Mean Absolute DeviationMean Absolute Deviation ((MADMAD))
MAD =MAD = ActualActual  ForecastForecast
nn
Mean Squared ErrorMean Squared Error ((MSEMSE))
MSE =MSE = ((Forecast ErrorsForecast Errors))22
nn
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Common Measures ofErrorCommon Measures ofError
Mean Absolute Percent ErrorMean Absolute Percent Error ((MAPEMAPE))
MAPE =MAPE =100100ActualActualii  ForecastForecastii/Actual/Actualii
nn
nn
ii = 1= 1
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Measuring Forecast Accuracy
Mean Squared Error (MSE)
represents the variance of errors in a forecast. This criterion is
most useful if you want to minimize the occurrence of a major
error(s).
n
e
=n
)2FtAt(n
1=t
=MSE
2t
n
1=t

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Measuring Forecast Accuracy
Mean Absolute Deviation (MAD)
measures the average absolute error of a forecast. A signof an error, which represents over or underestimation, isreally not important in most cases; we are rather
concerned with the value of deviation.
where:
At = actual value in period t,
Ft = forecasted value in period t,
et = forecast error in period t,
n = number of periods.
n
e
=n
FA
=MAD
t
n
1=t
tt
n
1=t

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Accuracy and Control of Forecasts
Forecast error: is the difference between the value that
occurs and the value that was predicted for a given time
period.
Error = Actual Forecast

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Illustration
Compute MAD, MSE, and MAPE for the following data,showing actual and predicted numbers of accountsserviced.

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MAD/MAPE/MSE
MSE= (e) = 76/8=9.5
n
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