7317173 Demand Forecasting Lecture

8/8/2019 7317173 Demand Forecasting Lecture

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Demand Forecasting in aDemand Forecasting in aSupply ChainSupply ChainDemand Forecasting in aDemand Forecasting in aSupply ChainSupply Chain

Presented byPresented by

Prof. M. K. TiwariProf. M. K. Tiwari


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At the end of session you will

Understand the role of forecastingfor both an enterprise and a SupplyChain (SC)

Identify the components of ademand forecasts. Forecast demand in a SC given

historical demand data using timeseries methodologies.

Analyze demand forecasts toestimate forecast error.


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Forecasting!......why?

Push system requires planning about: Level of production

Pull system requires planning about: Level of available capacity Level of inventory

Both require future demand of customers.

Either Pull or push, both processes aredriven by customer demand.


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Example of Dell Computer:Mastering Pull and Push

Dell orders components anticipatingcustomers order (Push)

It determines capacity of assemblyplants on customer demand basis.(Pull)

For both purposes it requires demandforecasting.


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Forecasting: Definitionand its role

Definition: In its simplest form It isestimation of expected demand over aspecified future period.

If each SC stage makes own demandforecast variation is unavoidable.

Collaborative forecasts tend to be moreaccurate.

Role: This accuracy enables SC to be more

responsible and efficient in serving theircustomers.


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Forecasting makes decisions

about:1. Production: scheduling, inventory control,

aggregate planning, purchasing.

2. Marketing: sales-force allocation,promotions, new product introduction.

3. Finance: plant/equipment investment,budgetary planning.

4. Personal: workforce planning, hiring,layoffs.


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Characteristics of forecasts

1. Should include both expected and measure offorecast error (demand uncertainty).

Consider, two car dealers

One expects sales between 100 and 1900 Other expects sales between 900 and 1100.

Even though for both average sales is 1000,sourcing strategy will be different.

First dealer will have to arrange more resourcesdue to higher forecasting error.

HighUncertaint

y

LowUncertainty


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2. Long term forecasts are usually lessaccurate than short term forecasts.

3. For same percentage error, aggregateforecasts (e.g. GDP of a country) areusually more accurate than

disaggregate forecasts (e.g. yearlyrevenue of company or product wisedetails).



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The classic example of summing up theforecast error is bullwhip effect. Hereorder variation is amplified as theymove up in SC from the end customers.

Mature products with stable demandare usually easiest to forecast.

Forecasting is difficult when either thesupply of raw materials or the demand

for the finished products is highlyvariable.



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Factors related to demand

forecast Past demand Lead time of products

Planned advertising ormarketing efforts

State of the economy

Planned price discounts Actions competitors havetaken.


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Classification of forecasting

methods Qualitative:

Methods are subjective and rely on

human judgment. Appropriate when there is little

historical data available or expertshave market intelligence.

Used to forecast demand severalyears into the future in a newindustry.


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methods Time series: Uses historical demand to make

forecasts.

Based on assumption that pastdemand history is a good indicator offuture demand.

Appropriate when the basic demand

pattern does not vary significantlyfrom one period to next. Simple to use and can serve as a good

starting point.


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Causal Assumes that demand forecast is highly

correlated with certain factors in the

environment (e.g. state of economy,interest rates etc.).

This method find the correlationbetween demand and environment anduse estimates of environment factors toforecast future demand.


methods


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Simulation These methods imitate consumer choices that

give rise to demand to arrive at a forecast.

Using it a firm can combine time series andcausal method to answer:

1. What will be impact of price promotion?2. What will be the impact of a competitor opening a

store nearby?

3. Airlines simulate customers buying behavior toforecast demand for higher fare seats.


methods


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Appropriate method

Several studies have indicated that usingmultiple forecasting method is moreeffective than any individual method.

Deal with time series method whenfuture forecast is expected to followhistorical method.

Historical demand, growth pattern, anyseasonal pattern influence the forecast.


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Components of demand

Observation demand can be brokeninto two components. Observed demand (0)=systematic (S)

+random (R) Systematic component measures the

expected value of demand.

Random component is that part offorecast that deviate from thesystematic part.

Companycan not

forecastthis value.


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Basic approach

Step 1: Understand the objective offorecasting

Step 2: Integrate demand planningand forecasting through the SC

Step 3: Understand and identifycustomers segments


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Basic approach

Step 4: Identify the major factorsthat influence the demand forecast

Step 5: Determine the appropriateforecasting technique

Step 6: Establish performance anderror measure for the forecast.


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Step 1: Understand theobjective of forecasting Clearly identify the decisions such as:

How much of a particular product to make?

How much to inventory?

How much to order? It is important that all parties must come

up with a common forecast demand.

Failure to make such decisions jointly

may results either too much or too littleproduct in various stages of supply chain.


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Step 2: Integrated demandplanning and forecasting

through SC A company should link its forecast to all

planning activities throughout SC.

Link should exist at both the informationsystem and human resource system.

A

s a variety of functions are affected by theoutcomes of the planning process, it isimportant that all of them are integrated intothe forecasting process.


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Step 3: Understand and

identify customers segments Customers may be grouped by similarities

in service requirements, demand volumes,order frequency, demand volatility,seasonality.

Companies may use different forecastingmethods for different segments.

Clear understanding facilitates anaccurate and simplified approach forforecasting.


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Step 4: Identify major factors that

influence the demand forecasts A proper analysis of major factors is

central to developing an appropriate

forecasting technique. The main factors are demand, supply,

and product-related phenomena.

On the demand side, a company must

ascertain whether demand is growing,declining or has a seasonal pattern.

Must be based on demand not salesdata.


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Step 5: Supply side Vs Productside

On the supply side, a company mustconsider available supply sources todecide on accuracy of forecast desired.

If alternative supply sources with short leadtime is available, a highly accurate forecastmay not be specially important.

On the product side, firm must know thenumber of variants of a product being

sold. If demand for a product influences or is

influenced by demand of another product,two forecasts are made jointly.


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Step 6: Determine appropriate

forecasting technique A company should first understand the

dimensions that will be relevant toforecasts.

These dimensions include geographicalarea, product groups, and customersgroups.

A firm should be wise enough to havedifferent forecasts and techniquesfor each dimension.


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Step 7: Establish performance and

error measure of forecast These measures should evaluate accuracy and

timeliness of forecast. Measure should correlate with the objective of

the business decisions based on the forecasts. Example:

A mail order company uses forecast to place orders tosuppliers.

Orders are send to the suppliers with two months lead

time Orders are to provide company with a quantityminimizing both extra product left over at the end ofsale season and any lost sale due to unavailability.


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At the end of season the companymust compare actual demand toforecasted demand to estimate the

accuracy of forecast.

The observed accuracy should becompared with the desired andresulting gap should be used toidentify corrective action thatcompany needs to take.

Step 7: Establish performance and

error measure of forecast


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Time series forecast

methods

Goal of forecasting is to predict

systematic component demand andestimate the random component.

In general, systematic component

data contains 3 factors: level factor (L)

trend factor (T) , and

seasonal factor (S).


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Forms of seasonal component

Multiplicative:systematic component=level * trend *seasonal factor

Additive:systematic component=level + trend +seasonal factor

Mixed:systematic component=(level + trend)*seasonal factor


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Static methods

For static methods, the level, trend, andseasonality within systematic component

is estimated on the basis of historicaldata and then same values are used forfuture forecasts.


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Mathematical model

The forecast in period t for demandin period t+l

Ft+1=[L+(t+l)T]St+1

where,L=estimate of level at t=0

T=estimate of trend

St=estimate of seasonal factor for period t

Dt= actual demand observed for period t

Ft=forecast for demand for period t


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Example problem to estimate L, T, and S.

Tahoe , a producer of salt noticed that hisretailers always overestimated the demand. Thislead to his excess inventory holding costs of rocksalt used in the production of salt. To reduce his

inventory costs. Tahoe decided to produce acollaborative demand forecast.

The demand is measured on quarterly basis and

demand pattern repeats every year (i.e. p =4). p isthe periodicity defined as the number of periodsafter which seasonal cycle repeats itself.


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Quarterly demand for Tahoe salt

Year Quarter Period Demand Dt2000 2 1 8,000

2000 3 2 13,000

2000 4 3 23,000

2001 1 4 34,000

2001 2 5 10,000

2001 3 6 18,000

2001 4 7 23,000

2002 1 8 38,000

2002 2 9 12,000

2002 3 10 13,000

2002 4 11 32,000

2003 1 12 41,000


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Estimation of threeparameters

Two steps1. Deseasonalize demand and run

linear regression to estimate level

and trend.

1. Estimate seasonal factors.


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Estimating level at period 0and trend

First, deseasonalize the demand data.

Deseasonalized demand represents thedemand that would have been observedin the absence of a seasonalfluctuations.


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Method

To ensure that each season is given equalweight when deseasonalizing the demand,take average of p consecutive periods.

Average of demand from period l+1 to l +p provides deseasonalized demand forperiod l+(1 + p)/2.

This method provides deseasonalized

demand for existing period, if p is odd, at a point between period l+(p)/2 and l +1+ (

p)/2 , if p is even,


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Method

By taking the average of deseasonalized demand

provided by periods l+1 to (l + p) and l+2 to

(l+ p+1) we obtain deseasonalized demand for

period l+1+(p/2) .i t 1 ( p / 2 )

t t p / 2 t p / 2 i

i t 1 ( p / 2 )

t p / 2

i

i t p / 2

D D D 2D / 2p if p iseven

D / p, forp odd

!

!

-

! -

!

-

Contd...


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For example, in the case of Tahoe salt

where p=4, for t=3 the decentralizeddemand is given by

4

3 1 5 i

i 2

D D D 2D / 8!

!

-


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Deseasolized demand for TahoeDemand

Period Demand Dt Deseasonalizeddemand

1 8,000

2 13,000

3 23,000 19,750

4 34,000 20,6255 10,000 21,250

6 18,000 21,750

7 23,000 22,500

8 38,000 22,125

9 12,000 22,62510 13,000 24,125

11 32,000

12 41,000


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Once the demand is deseasonalized it iseither growing or declining at a steadyrate. It can be expressed as follows:

Where,

L= level or deseasonalized demand at period t.

T=rate of growth of deseasonalized demand or trend

tD L Tt!


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L and T estimation

For previous formula, need to estimate thevalues of L and T.

Use linear regression with deseasonalizeddemand( using excel sheet).

For the example of salt, L=18439, T=524

tD 18,439 524t!


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Deseasonalized demand andseasonal factor for Tahoe Salt

Period Demand Dt Deseasonalized demand

SeasonalFactor

1 8,000 18,963 0.42

2 13,000 19,487 0.67

3 23,000 20,011 1.154 34,000 20,535 1.66

5 10,000 21,059 0.47

6 18,000 21,583 0.83

7 23,000 22,107 1.04

8 38,000 22,631 1.689 12,000 23,155 0.52

10 13,000 23,679 0.55

11 32,000 24,203 1.32

12 41,000 24,727 1.66


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Estimating seasonal factors The seasonal factor for period tis the ratio of actual

demand to deseasonalized demand and is given as follows:

For a given periodicity, p, we can obtain the

seasonal factor for a given period by averagingseasonal factors corresponding to similar periods.

Example ,if periodicity p=4, the periods 1,5,and 9

will have similar seasonal factors. The seasonalfactor for these periods is obtained as the averageof the 3 seasonal factors.

t t tS D / D!

_

tS


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Estimating seasonal factors

Given r seasonal cycles in data, for all periods of

the form we obtain the seasonalfactor as:

For the Tahoe salt example, a total of 12 periods andperiodicity of 4 implies r=3 therefore we get

_ _ _

1 1 5 9( ) / 3 (0.42 0.47 0.52) / 3 0.47S S S S ! ! !

r 1

i jp i

j 0

S S / r

!

!

,1pt i i p e e


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Adaptive forecasting

In this, the estimates of level, trend,and seasonality are updated after eachdemand observation.

In most general setting, systematiccomponent of demand data contains alevel, a trend, and a seasonal factor.

Can be easily modified for other cases

also. We have historical data for n periods

and demand is periodic with periodicity p


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Mathematical model

In adaptive methods the forecast for period t+1isgiven as follows:

where Lt=estimate of level at the end of period t Tt=estimate of trend at the end of period t St=estimate of seasonal factor at the end of period t Dt= Actual demand observed at the end of period t

Ft=forecast for demand at the end of period t Et=forecast for demand at the end of period t

( )t l t t t l F l S !


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Steps in adaptive

forecasting framework1. Initialize:

1. Compute initial estimates of level (L0),

trend (T0), seasonal factor (S1,, SP)

from given data.

2. Forecast:1. Given the estimates in period t,

forecast demand for t+1.2. First forecast if for period 1 and is

made with the estimates of level,trend, and seasonal factor at period 0.


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Steps3. Estimate error

Record the actual demand Dt+1 for

period t+1 and compute the error Et+1in the forecast for period t+1 as thedifference between the forecastand actual demand. The error forperiod t+1 is stated as follows:

Et+1= Ft+1- Dt+1


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Steps

4. Modifying estimateso Modify estimates of level (Lt+1), trend (Tt+1),and seasonal factor (St+p+1) for a given errorEt+1 .

o Desirable modifications can be such that If the demand is lower than forecast,

estimates are revised downward. If demand is higher than forecast, estimatesare revised upward.

Revised estimates in period t+1 are thenused to make forecast for period t+2.

Steps 2, 3, 4 are repeated until allhistorical data upto period n have beencovered.


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Moving Average Used when demand has no observable

trend or seasonality.

i.e. Systematic components ofdemand=level

Estimate level in period t is given asthe average demand over mostrecent N periods.

1 1( ... ) /t t t t N D D D N !


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Moving average

The current forecast of all future periods issame and is based on current estimate of level.The forecast is stated as follows:

To compute new moving average, simply add thelatest observation and drop the oldest one.Revised moving average serves as the nextforecast.

1t tF ! t n tF !and

1 1 2 2 1( ... ) / ,t t t t N t t D D D N F ! !


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Simple exponential smoothing

Appropriate when demand has noobservable trend or seasonality.

i.e. Systematic component=level

Initial estimate of level, L0, is taken tobe the average of all historical data.

The current forecast for all the futureperiods is equal to the current estimateof level is as follows

n

0 i

i 1

1L D

n !!

1t tF ! t n tF !and


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Simple exponential smoothing

After observing demand Dt+1 for period t+1,estimate the level as

is the smoothing constant for the level,0<


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Trend corrected exponentialsmoothing (Holts method) Appropriate when demand is assumed to have a

level and a trend in systematic component butno seasonality.i.e. Systematic component of demand=level + trend

Initial estimation of level and trend byrunning a linear regression between demandDt and time period t

Dt=at+bwhere b mesures the estimate of the

demand at period t=0 and is our estimate ofinitial level L0andslope a measure the rate of change indemand per period and is our intial estimateof trend T0.


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Holts model

Running a linear regressionbetween demand and time periodsis appropriate since demand has a

trend but no seasonality.

Forecast for future periods is

Ft+1=Lt+Tt and Ft+n=Lt +nTt


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Holts Method

After observing the demand forperiod t, we estimate the level andtrend as follows

Lt+1=Dt+1+(1-)(Lt +Tt)Tt+1=(Lt+1-Lt)+(1- )Tt

is the smoothing constant for thelevel 0


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Trend and seasonality exponentialsmoothing (winters model)

Appropriate when systematiccomponent of demand assumed to behave a level, a trend, seasonal factor.

Systematic component ofdemand=(level + trend) X seasonalfactor Initiate with the estimation of level and

trend, and seasonal factor. Forecast for future periods

Ft+1=(Lt +Tt)St and Ft+1= (Lt +lTt)St+1


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Winters model

Lt+1=(Dt+1/St+1)St +(1-) (Lt+Tt)

Tt+1= (Lt+1 - Lt)St +(1-)Tt

St+p+1= (Dt+1/Lt+1)St +(1- )St+1

is the smoothing constant for the level 0


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Forecast error Every demand has a random component. A good

forecast method should capture the systematiccomponent of the demand but not the randomcomponent. The random component manifests

itself in the form of forecast error. Reasons for the error analysis of forecast. Use error analysis to determine Whether the current

forecasting method is accurately predicting the systematiccomponent of demand. E.g. If a forecasting method continues

to give positive error appropriate measures can be taken bythe manager.

Estimate forecast error because any contingency plan mustaccount for such an error


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Measures of forecast error Observed error are within historical error estimates,

firms can continue to use their current forecastingmethod.

In the other case, finding may indicate forecastingmethod is no more appropriate.

Forecast error Et for tth time period is given as thedifference between the forecast for period t and actualdemand.

1 1 1t t tE F D !


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Mean Squared Error(MSE)

One measure of forecast error is the mean

squared error (MSE) and is given by :

n2

n t

i 1

1M SE E

n !!


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Absolute deviation

Absolute deviation is the absolute value oferror in period t

Mean absolute deviation (MAD) to be averageof absolute deviation over all periods

MAD can be used to estimate the SD ofrandom component assuming it to be normallydistributed.

t tA !

n

n t

t 1

1A D A

n !!

f


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Mean percentage oferror

Is the average absolute error as apercentage of demand

We can use the sum of forecast errorsto evaluate the bias

The bias will fluctuate around 0 if theerror is truly random and not biased oneway or the other.

nt

t 1 t

n

100D

AP

n

!!

n

n t

t 1

bias!

!


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Tracking signal The tracking signal is the ratio of the

bias and the MAD and is given asfollows:

tt

t

biasS

MAD!


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Tracking signal

If the TS at any period is outside the range+6, this signal that the forecast is biased and

is either under-forecasting (TS below -6) orover forecasting (TS below -6) .

In such a case, a firm will choose a new forecasting method.


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Specialty Packaging Corporation: A Case Study

Company Profile

Manufacturer of disposable containers

Major customers are from the food industry

Main raw material is Polystyrene resin

Manages inventory through a make-to-stock policy


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Manufacturing Process

Polystyrene is stored in the form of resin pallets

Extruder generates rolled sheets, which may be stored

or further processed Thermo-setting press trims the rolls into containers

ResinStorage

RollStorage

ExtruderThermo-SettingPress

Fig. Manufacturing Process at SPC


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Market Scenario

Steady growth in demand, which will stabilize after2005

Unable to meet the peak demand, extrusion becomes a

bottleneck Lost sales occur frequently

Plastic

Clear Black

GroceryStore

Bakery RestaurantsGroceryStore

Catering

Fig. Customer Base

Peak demand in summerPeak demand in fall


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GoalSynergizing marketing and customer feedbacks to improvesupply chain performance by adequate demand matching

Objective

Forecasting quarterly demand during 2003-2005 for both

types of containersResults

Method of forecasting

Likely forecast errors

Documents

7317173 Demand Forecasting Lecture