8 Forecasting PKB

8/11/2019 8 Forecasting PKB

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Demand Forecasting


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Introduction

Demand estimates for products and services arethe starting point for all the other planning inoperations management.

Management teams develop sales forecastsbased in part on demand estimates.

The sales forecasts become inputs to bothbusiness strategy and production resourceforecasts.


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Examples of Production

Resource Forecasts

Long

Range

Medium

Range

ShortRange

Years

Months

Days,Weeks

Product Lines,

Factory Capacities

Forecast

Horizon

Time

Span

Item Being

Forecasted

Unit of

Measure

Product Groups,

Depart. Capacities

Specific Products,Machine Capacities

Rupees,

Tons

Units,

Pounds

Units,Hours


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Time Series Forecasting

Methods Based on the assumption that the forces that

generated the past demand will generate thefuture demand, i.e., history will tend to repeat

itself

Analysis of the past demand pattern provides agood basis for forecasting future demand

Requires large amount of past data


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A time series is a set of numbers where theorder or sequence of the numbers is important,e.g., historical demand

Analysis of the time series identifies patterns

Once the patterns are identified, they can beused to develop a forecast

Generally useful for shortrange forecasts

Time Series Analysis


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Demand of the product or service is assumed tobe caused by other explanatory variables

Some of the explanatory variables can be leading

or lagging indicators If the demand can be estimated as a function

of the causal or explanatory variables, it can be

forecast if the future values of these variablesare known

Generally useful for mediumrange forecasts

Causal Methods


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Different Approaches:

Regressionsimple or multiple

Simultaneous Equations models

Simulation

Causal Methods


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Qualitative Methods

Usually based on judgments about causal factors thatunderlie the demand of particular products or services

Do not require a demand history for the product or

service, therefore are useful for new products/services Approaches vary in sophistication from scientifically

conducted surveys to intuitive hunches about futureevents

The approach/method that is appropriate depends on aproducts life cycle stage

Generally useful for longrange forecasts


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Delphi Method

l. Choose the experts to participate. There should be avariety of knowledgeable people in different areas.

2. Through a questionnaire (or E-mail), obtain forecasts

(and any premises or qualifications for the forecasts)from all participants.3. Summarize the results and redistribute them to the

participants along with appropriate new questions.

4. Summarize again, refining forecasts and conditions,and again develop new questions.

5. Repeat Step 4 if necessary. Distribute the final resultsto all participants.


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Ranging Forecasts

Forecasts for future periods are only estimatesand are subject to error.

One way to deal with uncertainty is to developbest-estimate forecasts and the ranges withinwhich the actual data are likely to fall.

The ranges of a forecast are defined by theupper and lower limits of a confidence interval.


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Length of Time Number of

Before Pattern Length of Seasons

Is Repeated Season in Pattern

Year Quarter 4

Year Month 12

Year Week 52

Month Day 28-31

Week Day 7

Seasonal Patterns


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Time Series Methods

Simple Moving Average

Weighted Moving Average

Exponential Smoothing (exponentially weightedmoving average)

Exponential Smoothing with Trend

Exponential Smoothing with Seasonality Exponential Smoothing with Trend and

Seasonality


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Evaluating Forecast-Model

Performance

Short-range forecasting models are evaluatedon the basis of four characteristics:

Impulse response

Noise-dampening ability

Accuracy

Precision


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Performance

Impulse Response and Noise-DampeningAbility

If forecasts have little period-to-period fluctuation,

they are said to be noise dampening.

Forecasts that respond quickly to changes in data aresaid to have a high impulse response.

A forecast system that responds quickly to datachanges necessarily picks up a great deal of randomfluctuation (noise).

Hence, there is a trade-off between high impulseresponse and high noise dampening.


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Performance

Accuracy and Precision

Accuracy and precision are the typical criteriafor judging the performance of a forecastingmodel

Accuracy measures the performance of theforecasting model on an average

Precision measures how well the forecastedvalues match the actual values


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Forecast Errors

et = Forecast error in period t

= (Actual Demand in period t

Forecast for period t)

= Dt- Ft


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Monitoring Accuracy or Bias

Accuracy can be measured by one of thefollowing:

Average Error (AE)

Running Sum of

Forecast Errors (RSFE)


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Monitoring Precision

Precision of a forecasting model needs to bemonitored to assess the confidence you can havein its forecasts and changes in the market may

require reevaluation of the approach

Precision can be measured in several ways

Mean absolute deviation (MAD)

Mean squared error (MSE)

Mean Absolute Percentage Error (MAPE)


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Mean Absolute Deviation (MAD)

n

periodsnfordeviationabsoluteofSum=MAD


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Mean Squared Error (MSE)



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Mean Absolute Percentage error (MAPE)

A small value for MAD, MSE or MAPE

means actual demand figures are tightlygrouped around the forecast figures and errorrange is small.



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The weights used to compute the forecast(moving average) are exponentiallydistributed.

The forecast is the sum of the old forecast

and a portion (a) of the forecast error (Dt-1-Ft-1).

Ft= Ft-1+ a(Dt-1-Ft-1)

. . . more

Exponential Smoothing


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Example: Central Call Center

Exponential SmoothingIf a smoothing constant value of .25 is used and

the exponential smoothing forecast for Day 11 was

180.76 calls, what is the exponential smoothing

forecast for Day 13?

F12= 180.76 + .25(198180.76) = 185.07

F13= 185.07 + .25(159185.07) = 178.55


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Forecast Precision - MAD

Which forecasting method (the AP = 3

moving average or the a= .25 exponentialsmoothing) is preferred, based on the MADover the most recent 9 days? (Assume that theexponential smoothing forecast for Day 3 is the

same as the actual call volume.)


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AP = 3a

= .25Day Calls Forec. |Error| Forec. |Error|

4 161 187.3 26.3 186.0 25.0

5 173 188.0 15.0 179.8 6.86 157 173.3 16.3 178.1 21.17 203 163.7 39.3 172.8 30.28 195 177.7 17.3 180.4 14.6

9 188 185.0 3.0 184.0 4.010 168 195.3 27.3 185.0 17.011 198 183.7 14.3 180.8 17.212 159 184.7 25.7 185.1 26.1

MAD 20.5 18.0


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Criteria for Selecting

a Forecasting Method Cost

Accuracy and Precision

Data availableTime span

Nature of products and services

Impulse response and noise dampening


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a Forecasting Method

Cost, Accuracy and Precision

There is a trade-off between cost and precision;generally, more forecast precision can be obtained at

a cost. High-precision approaches have disadvantages:

Use more data

Data are ordinarily more difficult to obtain

The models are more costly to design, implement, andoperate

Take longer to use


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Cost and Precision

Low/Moderate-Cost Approachesstatisticalmodels, historical analogies, executive-committee

consensus High-Cost Approachescomplex econometric

models, Delphi, and market research


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Data Available

Is the necessary data available or can it beeconomically obtained?

If the need is to forecast sales of a new product, thena customer survey may not be practical; instead,historical analogy or market research may have to beused.


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Time Span

What operations resource is being forecast and forwhat purpose?

Short-term staffing needs might best be forecastwith moving average or exponential smoothingmodels.

Long-term factory capacity needs might best bepredicted with regression or executive-committeeconsensus methods.


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Nature of Products and Services

Is the product/service high cost or high volume?

Where is the product/service in its life cycle?

Does the product/service have seasonal demandfluctuations?


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Impulse Response and Noise Dampening

An appropriate balance must be achieved between:

How responsive we want the forecasting model to be to

changes in the actual demand data Our desire to suppress undesirable chance variation or

noise in the demand data


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Monitoring and Controlling

a Forecasting Model

Tracking Signal (TS)The TS measures the cumulative forecast error over

n periods in terms of MAD

If the forecasting model is performing well, the TS

should be around zeroThe TS indicates the direction of the forecasting

error; if the TS is positive -- increase the forecasts, ifthe TS is negative -- decrease the forecasts.


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Monitoring and Controlling

a Forecasting Model

Tracking Signal

The value of the TS can be used to automaticallytrigger new parameter values of a model, thereby

correcting model performance. If the limits are set too narrow, the parameter values

will be changed too often.

If the limits are set too wide, the parameter valueswill not be changed often enough and accuracy willsuffer.


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Computer Software for

Forecasting

Examples of computer software with forecastingcapabilities

Forecast Pro

Autobox

SmartForecasts for Windows

SAS

SPSS

SAP

POM Software Libary

Primarily for

forecasting

Have

Forecastingmodules

SIMPLE EXPONENTIAL SMOOTHING


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SIMPLE EXPONENTIAL SMOOTHINGModel:

Actual Demand = Base Demand + Error

Forecast = BaseUsing Symbols,

tt SF 1,

10

...1

11

)1(1

1

33

22

1

21

1

a

aa

aaa

aaa

a

where

D

DDD

SDD

SDS

t

ttt

ttt

ttt

1)1(

321,

ttt

tt,m

SDSFinally,

,......,,m,SFAlso

EXPONENTIAL SMOOTHING WITH TREND


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Model:

Actual Demand = Base Demand + Trend + ErrorForecast = Base + Trend

Using Symbols,

ttt TSF

1, ttt,m mTSFand

1,11, ttt FDSFinally a

111 ttt TSD a

11 1 tttt TSSTand

10

10

a

and

where

EXPONENTIAL SMOOTHING WITH


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EXPONENTIAL SMOOTHING WITH

SEASONALITYModel:

Actual Demand = Base Demand XSeasonality Index + ErrorForecast = Base XSeasonality Index

Using Symbols,

mLttmtLttt ISFISF .;. ,11,

Ltt

tt I

S

DIand

1

10

10

a

and

where

11

t

Lt

tt S

I

DS aFinally,



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& SEASONALITY (WINTERS MODEL)Model:

Actual Demand = (Base Demand +Trend)X

Seasonality Index +Error

Forecast = (Base + Trend) XSeasonality IndexUsing Symbols,

11, . Ltttt ITSF

Ltt

tt I

S

DIand

1

)(1 11

tt

Lt

tt TS

I

DS a

mLtttmt ImTSF .,

11 1 tttt TSST

Finally,

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