Forecasting Tools in Supply Chain

© 2007 Pearson Education

Demand Forecastingin a Supply Chain

7-1

7-2

Role of Forecasting in a Supply Chain

u The basis for all strategic and planning decisions in a supply chain

u Used for both push and pull processesu Examples:

– Production: scheduling, inventory, aggregate planning– Marketing: sales force allocation, promotions, new production

introduction– Finance: plant/equipment investment, budgetary planning– Personnel: workforce planning, hiring, layoffs

u Mature products-Easy forecasts,u Fashion goods and High tech products-Difficult forecasts.

7-3

Characteristics of Forecasts

u Forecasts are not accurate. Should include expected value and measure of error.

u Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important)

u Aggregate forecasts are more accurate than disaggregate forecasts.

u Farther up the supply chain,larger the forecast error.

7-4

Basic Approach toDemand Forecasting

u Understand the objectives of forecasting.u Integrate demand planning and forecasting.u Identify major factors that influence the demand

forecast.u Understand and identify customer segments.u Determine the appropriate forecasting technique.u Establish performance and error measures for the

forecast.

7-5

Forecasting Methodsu Qualitative: primarily subjective; rely on judgment and

opinionu Quantitative/Time Series: use historical demand only

– Static – Adaptive

u Causal: use the relationship between demand and some other factor to develop forecast

u Simulation– Imitate consumer choices that give rise to demand– Can combine time series and causal methods

Qualitative Methods

Qualitative

Market Research

HistoricalAnalogy

Delphi Method

Grass roots

PanelConsensus

Delphi Method

1.Choose the experts to participate. There should be a variety of knowledgeable people in different areas.

2.Through a questionnaire, obtain 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 step4 if necessary.distribute the final results to all.

Quantitative Methods

Time-series models Associative models

Simple moving average

WeightedMoving average

Linear Regression

Exponential Smoothing

Time series Components

TrendTrend

SeasonalSeasonal

CyclicalCyclical

RandomRandom

Trend Componentu Persistent, overall upward or downward patternu Due to population, technology etc.u Several years duration

Mo., Qtr., Yr.

Response

© 1984-1994 T/Maker Co.

Seasonal Componentu Regular pattern of up & down fluctuationsu Due to weather, customs etc.u Occurs within 1 year

Mo., Qtr.

ResponseSummer

© 1984-1994 T/Maker Co.

Cyclic Componentu Repeating up & down movementsu Due to interactions of factors influencing economyu Usually 2-10 years duration

Mo., Qtr., Yr.Mo., Qtr., Yr.

ResponseResponseCycle

Irregular Componentu Erratic, unsystematic, ‘residual’ fluctuations

u Due to random variation or unforeseen events

– Union strike

– Tsunami

– Floods/Earthquake

u Short duration & nonrepeating

General Time Series models

u Any observed value in a time series is the product (or sum) of time series components

u Multiplicative model– Yi = Ti · Si · Ci · Ri Additive model– Yi = Ti + Si + Ci + Ri

7-15

Components of an ObservationObserved demand (O) =Systematic component (S) + Random component (R)

Level (current deseasonalized demand)

Trend (growth or decline in demand)

Seasonality (predictable seasonal fluctuation)

• Systematic component: Expected value of demand• Random component: The part of the forecast that deviates from the systematic component• Forecast error: difference between forecast and actual demand

Simple Moving Average formulau This model assumes an average is a good

estimator of future behavior.

u The formula for MA is: Ft = At-1+At-2+At-3+…..+At-n

nFt: forecast for the coming period.n: No. of periods to be averaged.At-1: actual occurrence in the past period.

Moving Average ExampleYou’re manager of a museum store that sells historical replicas. You want to forecast sales (000) for 2003 using a 3-period moving average.

1998 41999 62000 52001 32002 7

© 1995 Corel Corp.

Moving Average Solution

Time Response Yi

Moving Total (n=3)

Moving Average

(n=3) 1998 4 NA NA 1999 6 NA NA 2000 5 NA NA 2001 3 4+6+5=15 15/3 = 5 2002 7 2003 NA

Moving Average solution

Time Response Yi

Moving Total (n=3)

Moving Average

(n=3) 1998 4 NA NA 1999 6 NA NA 2000 5 NA NA 2001 3 4+6+5=15 15/3 = 5 2002 7 6+5+3=14 14/3=4 2/3 2003 NA

Moving Average solution

Time Response Yi

Moving Total (n=3)

Moving Average

(n=3) 1998 4 NA NA 1999 6 NA NA 2000 5 NA NA 2001 3 4+6+5=15 15/3=5.0 2002 7 6+5+3=14 14/3=4.7 2003 NA 5+3+7=15 15/3=5.0

Moving Average Graph

95 96 97 98 99 00Year

Sales

2468 Actual

Forecast

Weighted Moving average Formula

u While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods.

u The formula for WMA is:Ft = w1At-1+w2At-2+….+wnAt-n

Σwi w t: weight given to time period “t” occurrenceΣwi = 1 (Weights must add to one)

Numericalu A department store may find that in a four month

period, the best forecast is derived by using 40% of the actual sales for the most recent month, 30% of two months ago, 20% of three months ago and 10% of four months ago.If actual sales experience was

Month1 Mon 2 Mon 3 Mon 4 Mon 5 100 90 105 95 ?

Exponential Smoothing formulau The equation for exponential smoothing is:

Ft : the exponentially smoothed forecast for period tFt-1 : the exponentially smoothed forecast for prior period.At-1 : the actual demand in the prior period.smoothing or weighting constant.

Ft = Ft-1 + (At-1 - Ft-1)

Exponential Smoothing Example

During the past 6 quarters, the Port of Baltimore has unloaded large quantities of grain. ( = .10). The first quarter forecast was 175. Quarter Actual

1 180 2 168

3 1594 1755 190

6 205

Find the forecast for the 7th quarter?

Exponential Smoothing Solution

Ft = Ft-1 + 0.1(At-1 - Ft-1)

Quarter Actual Forecast, Ft

(

1 180 175(Given) 2 168 175+0.1(180-175) 174.50 3 159 174.50+0.1(168-174.50) 174.75 4 175 173.18 5 190 173.36 6 205 175.02 7 ? 175.02+0.1(205-175.02)=178.02

Linear Regressionu Used for forecasting linear trend line.u Functional relationship between two or more

correlated variables.u Used to predict one variable given the other.

u Estimated by least squares method– Minimizes sum of squared errors

Linear Regression Equations

u Equation:

u Slope:

u Y-intercept:

ii bxaY

22i

n

1i

ii

n

1i

xnx

yxnyx b

xby a

Using a Trend LineYear Demand1997 741998 791999 802000 902001 1052002 1422003 122

The demand for electrical power at N.Y.Edison over the years 1997 – 2003 is given at the left. Find the overall trend.

Finding a Trend Line

xy=3,063x2=140y=692x=2885449122720038523614262002525251055200136016904200024098031999158479219987417411997

xyx2Power Demand

Time Period

Year

The Trend Line Equation

megawatts 151.56 10.54(9) 56.70 2005in Demand

megawatts 141.02 10.54(8) 56.70 2004in Demand

56.70 10.54(4) - 98.86 xb - y a

10.5428

295(7)(4)140

86)(7)(4)(98.3,063xnΣxyxn -Σxy b

98.867

692 n

Σyy 47

28n

Σxx

222

Actual and Trend Forecast

Numericalu A manufacturer of critical components for two

wheelers in the automotive sector is interested in forecasting the trend of demand during the next year as a key input to its annual planning exercise. Information on the past sales is available for the last three years. Extract the demand component of the time series data and use it for predicting the future demand of the components.

65037193537978Sum144648054012Y3-Q4121487344311Y3-Q3100618061810Y3-Q28145455059Y3-Q16437124648Y2-Q44927093877Y2-Q33627904656Y2-Q22519653935Y2-Q11616254064Y1-Q4910783593Y1-Q348764382Y1-Q213603601Y1-Q1X x XXYDemand(Y)Period(X)Year

u B= 15.59u A= 346.91

u Y= 346.91+15.59X

u Forecast for Year4 – Q1 = 346.91+15.59X 13= 550u Forecast for Year4 – Q2= 346.91+15.59X 14= 565u Forecast for Year4 – Q3= 346.91+15.59X 15= 581u Forecast for Year4 – Q4= 346.91+15.59X 16= 596

Extract the seasonal component and adjust the forecast demand obtained for seasonality.

540Y3-Q4443Y3-Q3

1.196516.750526.25618Y3-Q21.007500.250507.25504Y3-Q10.979474.125493.25464Y2-Q40.877441.125455.00387Y2-Q31.107420.00427.25465Y2-Q20.960409.250412.75393Y2-Q11.009402.375405.75406Y1-Q40.909394.875399.00359Y1-Q3

390.75438Y1-Q2360Y1-Q1

Seasonality IndexAvg demand4 qtr MADemandPeriod

0.9940.9791.009Quarter 4

0.8930.8770.909Quarter 3

1.1521.1961.107Quarter2

0.9841.0070.960Quarter1

AverageYear 3Year 2Year 1

The forecast after incorporating seasonality index for the next 4 qtrs

5920.994596Quarter 4

5190.893581Quarter 3

6511.152565Quarter 2

5410.984550Quarter 1

Seasonality adjust forecast

SITrend adjusted forecast

Year 4

Forecast errors

Seek to minimize the Mean Absolute Deviation (MAD)

If: Forecast error = demand - forecast

Then:

nerrorsforecast

MAD nerrorsforecast

MAD

Standard deviation and tracking signal

u S.D = 1.25 MADu MSE= 1/n X Σ (Error)2

u TS = RSFE MADRSFE: The running sum of the forecast errors.MAD: The average of all the forecasted errors.

3.481141698.9131311012310

2.13102.90258.44551041099

1.4695.562258.881515951108

-0.25104.384822981007

-0.4487.14100910-10105956

0.68101.201698.8013-13110975

2.4584.251967.7514141101244

0.884745.672-21321303

0.9368.50167.504-41181142

1.001211211111111091201

TSMSEErrX Err

MADAbs. dev.

F-errFDPeriod