dpf ksrao ppt 3

8/2/2019 Dpf Ksrao Ppt 3

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Demand Planning and Forecasting

Session 3

Demand Forecasting Methods-1

By

K. Sashi Rao

Management Teacher and Trainer


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Forecasting in Business PlanningInputsMarket Conditions

Competitor Action

Consumer TastesProducts Life Cycle

Season

Customers plans

Economic OutlookBusiness Cycle Status

Leading Indicators-Stock

Prices, Bond Yields, Material

Prices, Business Failures, money

Supply, Unemployment

Other Factors

Legal, Political, Sociological,Cultural

Forecasting

Method(s)

Or Model(s)

Outputs

Estimated Demands

for each Product

in each Time Period

Other Outputs

Sales Forecast

Forecast and Demand

for Each Product

In Each Time Period

Processor

Production Capacity

Available Resources

Risk Aversion

ExperiencePersonal Values and

Motives

Social and Cultural

Values

Other Factors

Management Team

Forecast

Errors

Feedback


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Forecasting Methods

Forecasting

Qualitative

Or

Judgmental

Quantitative

Or

Statistical

Projective Causal


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Forecasting Basics

Types Qualitative --- based on experience, judgment,

knowledge;

Quantitative --- based on data, statistics;

Methods Naive Methods --- using ball-park numbers; or

assuming future demand same as before

Formal Methods --- systematic methods

thereby reduce forecasting errors using: time series models (e.g. moving averages and

exponential smoothing);

causal models (e.g. regression)


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Forecasting Approaches(1)

JUDGEMENTAL APPROACHES: The essence of the judgmental approach is toaddress the forecasting issue by assuming that someone else knows and can tellyou the right answer. They could be experts or opinion leaders.

EXPERIMENTAL APPROACHES: When an item is "new" and when there is noother information upon which to base a forecast, is to conduct a demandexperiment on a small group of customers and extrapolated to the widerpopulation. Test marketing is an example of this approach.

RELATIONAL/CAUSAL APPROACHES: There is a reason why people buy ourproduct. If we can understand what that reason (or set of reasons) is, we can usethat understanding to develop a demand forecast. They seek to establish product -demand relationships to relevant factors and/or variables e.g. hot weather to cold

drinks consumption. TIME SERIES APPROACHES: A time series is a collection of observations of well-

defined data items obtained through repeated measurements over time.


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Forecasting Approaches(2)

In general,judgment and experimental approaches tend be

more qualitative

While relationship/causal and time series approaches tend be

more quantitative

Still, these qualitative methods are also scientifically done

with results that are expressed in indicative numbers and

broad trends

Time series/causal methods are completely based on

statistical methods and principles


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Qualitative Approach

Qualitative ApproachUsually based on judgments about causal factors that underlie thedemand of particular products or servicesDo not require a demand history for the product or service, therefore areuseful for new products/services

Approaches vary in sophistication from scientifically conducted surveys tointuitive hunches about future events. The approach/method that isappropriate depends on a products life cycle stage

Qualitative MethodsEducated guessExecutive committee consensus

Delphi methodSurvey of sales forceSurvey of customersHistorical analogyMarket research


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Forecasting Methods-judgmental approach(a)

Surveys - this involves a bottom up method where each

individual/respondent contributes to the overall result; this could be for

product demand or sales forecasting ; also for opinion surveys amongst

employees, citizen groups or voter groups for election polls

Sales Force Composites- where the similar bottom up approach is usedfor building up sales forecasts on any criteria like region-wise or product

wise sales territory groupings from sales force personnel

Consensus of Executive Opinion -normally used in strategy formulation by

sought opinions from key organizational stakeholders- managers,

suppliers, customers, bankers and shareholders Historical analogy- used for forecasting new product demand as similar to

the previously introduced new product benefiting from its immediacy that

same demand influencing factors will apply


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Forecasting Methods-judgmental approach(b)

Consensus thro Delphi method especially for new productdevelopments and technology trends forecasting

It is the most formal judgmental method and has a well definedprocess and overcomes most of the problems of earlier consensusby executive opinion

This involves sending out questionnaires to a panel of expertsregarding a forecast subject. Their replies are analyzed,summarized, processed and redistributed to the panel for revisionsin light of others arguments and viewpoints. By going thro such aniterative process say 3-4 times, the final panel forecast is consideredas fairly accurate and authentic

Yet, difficulties do exist in planning, administering and integratingmember views into a meaningful whole

Course Booklet has a separate chapter on the Delphi method( page107 onwards)


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Forecasting Methods-judgmental approach(c)

Method Short termaccuracy

Medium

term

accuracy

Long term

accuracy

Cost

Personalinsights

POOR POOR VERY POOR VERY LOW

Panel

consensus

POOR TO FAIR POOR TO FAIR POOR LOW

Market survey VERY GOOD GOOD FAIR VERY HIGH

Historical

analogy

POOR FAIR TO GOOD FAIR TO GOOD MEDIUM

Delphi method FAIR TO GOOD FAIR TO GOOD FAIR TO GOOD HIGH


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Forecasting Methods

- experimental approach

Customer surveys- thro extensive formal market research using personalor mail interviews, and newly thro internet modes; also build demandmodels for a new product by an aggregated approach

Consumer panels- particularly used in initial stages of productdevelopment and design to match product attributes to customer

expectations Test marketing- often used after product development but before national

launches by starting in a selected target market/geography to understandany problems or issues to fine-tune marketing plans and avoid costlymistakes before going in a big way

Customer buying data bases- based on selected and acceptedindividuals/families on their buying behavior , patterns and expenditurescaptured using electronic means direct from retailer sales data; givesextensive clues on buying factors, customer attitudes, brand loyalty andbrand switching and response to promotional offers


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Forecasting Methods- relationship/causal approach(1)

Its basic premise is that relationships exist betweenvarious independent demand variables( likepopulation, income, disposable incomes, age, sex etcto consumer needs/wants/expectations( dependentvariables)

Before linking these, we need to find the nature andextent of these causes/relationships in mathematicalterms as regression( linear/multiple)equations

Once done, they can be used to forecast thedependent variable for available independent variables

Various types of causal methods follow in next slide


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Forecasting Methods- relationship/causal approach(2)

Econometric models like discrete choice and multiple regression models

used in large-scale or macro-level economic forecasting

Input-output models used to estimate the flow of goods between markets

and industries, again in macro-economic situations

Simulation models used to establish raw materials and components

demand based on MRP schedules , driven by keyed-in product sales

forecasts; to reflect market realities and imitate customer choices

Life-cycle models which recognize product demand changes during its

various stages(i.e. introduction/growth/maturity/decline) particularly in

short life cycle sectors like fashion and technology


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Forecasting Methods- time series approach(1)

Fundamentally, uses historical demand/sales data todetermine future demand

Basic assumptions are that :

Past data/information is available

This data/information can be quantified

Past patterns will continue into the future and projections made( thoughin reality may not always be the case !)

They involve statistical methods of understanding andexplaining patterns in time series data( like constant series

e.g. annual rainfall; trends e.g. growing expenditure withincomes; seasonal series e.g. umbrella demand during rainyseason; and any random/unexplained noise where actualvalue= underlying pattern+ random noise)


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Forecasting Methods-time series approach(2)

Static elements: Trend

Seasonal

Cyclical Random

Adaptive elements: Moving average

Simple exponential smoothing

Exponential smoothing (with trend)

Exponential smoothing (with trend and seasonality)


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Time Series-static elements

Trend component- persistent overall downward or upward

pattern; due to population, technology or long term

movement

Seasonal component- regular up and down fluctuations dueto weather and/or seasons whose pattern repeats every year

Cyclical component- repeated up and down movements; due

to economic or business cycles lasting beyond one year but

say every 5-6 years

Random component- erratic, unsystematic, residual

fluctuations due to random events or occurrences like one

time drought or flood events


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Forecasting Methods- time series approach(3)

Basic concepts involved are those ofmoving averages and exponentialsmoothing

A simple average forecast method is usable if past pattern is very stable,but very few time series are stable over long periods, hence are of limiteduse

A moving average takes the average over a fixed number( by choice) ofprevious periods ignoring older data periods giving a sense of immediacyto the data used e.g. taking only past 3 months data as relevant forforecasting for next quarter with same weightage; later improved byweighted moving averages with unequal weightage

All moving averages suffer in that(a) all historically used data are givensame /unequal weight and (b) works well only when demand is relatively

constant. Its handicaps are overcome by exponential smoothing Exponential smoothing is based on idea that as data gets older it becomes

less relevant and should be given a progressively lower weightage on anon-linear basis


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Forecasting Examples

Examples from Projects:

Demand for tellers in a bank;

Traffic flow at a major junction

Pre-poll opinion survey amongst voters

Demand for automobiles or consumer durables

Segmented demand for varying food types in a restaurant Area demand for frozen foods within a locality

Example from Retail Industry: American Hospital Supply Corp.

70,000 items;

25 stocking locations;

Store 3 years of data (63 million data points);

Update forecasts monthly;

21 million forecast updates per year.


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Components of an Observation

Observed 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


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Time Series

Forecasting Methods

Goal is to predict systematic component of

demand

Multiplicative: (level)(trend)(seasonal factor) Additive: level + trend + seasonal factor

Mixed: (level + trend)(seasonal factor)

Static methods

Adaptive forecasting


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Static Methods

Assume a mixed model:

Systematic component = (level + trend)(seasonal factor)

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

= forecast in period tfor demand in period t+ lL = estimate of level for period 0

T = estimate of trend

St = estimate of seasonal factor for period t

Dt = actual demand in period t

Ft = forecast of demand in period t


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Adaptive Forecasting

The estimates of level, trend, and seasonalityare adjusted after each demand observation

General steps in adaptive forecasting

Moving average Simple exponential smoothing

Trend-corrected exponential smoothing(Holts model)

Trend- and seasonality-corrected exponentialsmoothing (Winters model)


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Moving Averages(1)

This is the simplest model of extrapolative forecasting

Since demand varies over time, only a certain amount of historicaldata is relevant to the future, implying that we can ignore allobservations older than some specified age

A moving average uses this approach by taking average demandover a fixed number of previous periods( say 3 as in below example)

Example: If product demand is 150, 130 and 125 over the last 3 months,then forecast for 4th month is (150+130+125)/3= 135. If actual demand in4th month is 135 as forecasted( their differences are forecasting errorswhichwill discuss later), then forecast for 5th month is(130+125+135)/3= 130; and this process is repeated for subsequent

periods In above example, all past periods were given equal weightage;

which can then be differentially weighted to give more importanceto most recent periods


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Moving Averages(2)

Used when demand has no observable trend or seasonality

Systematic component of demand = level

The level in period t is the average demand over the last N periods(the N-period moving average)

Current forecast for all future periods is the same and is based on

the current estimate of the levelLt = (Dt + Dt-1 + + Dt-N+1) / N

Ft+1 = Lt and Ft+n = LtAfter observing the demand for period t+1, revise the estimates asfollows:

Lt+1 = (Dt+1 + Dt + + Dt-N+2) / NFt+2 = Lt+1


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Moving Averages(3)

Include n most recent observations

Weight equally

Ignore older observations

weight

today

123...n

1/n


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Moving Averages(4) Forecast Ft is average ofn previous observations or actual

Dt:

Note that the n past observations are equally weighted.

Issues with moving average forecasts: All n past observations treated equally;

Observations older than n are not included at all; Requires that n past observations be retained;

Problem when 1000's of items are being forecast.

!

!

!

t

nti

it

ntttt

Dn

F

DDDn

F

1

1

111

1

)(1

.


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Moving Averages(5)

Internet Unicycle Sales

0

50

100

150

200

250

300

350

400

450

Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13

Month

Units

n = 3


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Simple Moving Averages(6) example

Month Actual Sales ForecastChosen 3 months moving

average

Jan 24500

Feb 27000

Mar 19950

Apr 26000 23817

May 21200 24317

June 18900 22383July 17500 22033

Aug 19000 19200

Sep 18525 18467


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Weighted Moving Averages(1)

This is to overcome the lacuna of ALL past periods being given SAME

importance

Here, different past periods are given different weightage

In same earlier example, let us take past periods weightage as 0.60, 0.30 and

0.10( totaling 1 or 100%) ; then forecast for 4th

month is ( 125x0.60+ 130x0.30+150x0.10)= 75+39+15= 129; and further forecast for 5th month as

(129x0.60+125x0.30+130x0.10)= 127.9; and so on..

Idea is to give more importance to most recent observations

But problems relate to the logic of deciding the number of past periods

and the given differential weightage

Generally, if the demand is stable, then larger n values are chosen; if not,

then a smaller n and using weightage factors is better


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Weighted Moving Averages(2)-example

Month Actual Sales Forecast

Chosen 3 months moving

average

Weightage- immediate

past as 0.45, then 0.30and then 0.25

Jan 24500

Feb 27000

Mar 25500

Apr 26000 25700

May 21200 26100

June 18900 23715

July 17500 21365

Aug 19000 18845

Sep 18525


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Moving Averages- closing remarks

All moving average methods( besides exponential smoothing

to be taken up later) focus on short term forecasting and

provide such capability without consideration of any time

series patterns

But when medium term( say 1 year) or long term( 5 years ormore) forecasting needed, then time series data patterns

need looking into

These data patterns relate to trend, cyclical, seasonal and

random forms( as introduced earlier)

Once these patterns are extracted from a given time series

data , they can be used for forecasting


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Time Series Patterns(1)

0

10,000

20,000

30,000

40,000

50,000

97,2

97,3

97,4

98,1

98,2

98,3

98,4

99,1

99,2

99,3

99,4

00,1


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7-33


0

10000

20000

30000

40000

50000

1 2 3 4 5 6 7 8 9 10 11 12

Period

Demand

Dt

Dt-bar


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Causal Forecasting(1)

Basic idea is to use a cause or a relationship

between and amongst variables as a

forecasting method e.g. product sales is

dependent on its price

Need to identify the independent and

dependent variables

Causal forecasting is illustrated by linear

regression


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Linear Regression

It looks for a relationship of the form:

Dependent variable(P)= q+ r multiplied by

independent variable (S) or P= q+ r S where: q= intercept and r= gradient of the line

Independent variable S

Dependent

Variable P

Intercept q

.

Gradient r ( >0)

r(


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Linear Regression - example

A manufacturer of critical components for two

wheelers is interested in forecasting the trend in

demand during the next year as a key input to its

annual planning exercise. Information on pastdemand is available for last three years( next slide).

We need to develop a linear regression equation to

extract the trend component of the time series and

use it for predicting the future demand forcomponents


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Linear Regression example(contd.)ACTUAL DEMAND FOR LAST THREE

YEARS( in 000 units)

PERIOD Period Number(X) ACTUAL DEMAND(Y)

Year 1- Q1 1 360

Year 1- Q2 2 438

Year 1- Q3 3 359

Year 1- Q4 4 406

Year 2- Q1 5 393

Year 2 -Q2 6 465

Year 2- Q3 7 387

Year 2- Q4 8 464

Year 3- Q1 9 505

Year 3- Q2 10 618

Year 3- Q3 11 443

Year 3- Q4 12 540


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Linear Regression example(contd.)

Period X Y XY XX

PERIOD PERIOD Number ACTUAL DEMAND(Y)

Year 1- Q1 1 360 360 1

Year 1- Q2 2 438 876 4

Year 1- Q3 3 359 1078 9

Year 1- Q4 4 406 1625 16

Year 2- Q1 5 393 1965 25

Year 2 -Q2 6 465 2790 36

Year 2- Q3 7 387 2709 49

Year 2- Q4 8 464 3712 64

Year 3- Q1 9 505 4545 81

Year 3- Q2 10 618 6180 100

Year 3- Q3 11 443 4873 121

Year 3- Q4 12 540 6480 144

SUM 78 5379 37193 650


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Linear Regression example(contd.)

Linear regression equation P= q+ rS

Using method of least squares, the regression coefficients areworked out as X= 78/12= 6.50 and Y= 5379/12= 448.25

Then the gradient r= 37193-(12x6.50x448.25)/650-

(12x6.50x6.50)= 2229.5/143= 15.59 The intercept q= 448.25-15.59x6.50= 346.91

Final regression equation is P= 346.91+ 15.59S

Thus Forecast for Year 4 Q1= 346.91+ 15.59x13= 550

Forecast for Year 4 Q2= 346.91+ 15.59x14= 565




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Multiple Regression

When there are many independent variables involvedwhich influence a dependent variable then issuesbecome complicated

Then not only linear regression equations are required

but also multiple regression analysis is involved wherethe interdependency of the various independentvariables are taken into account

These involve complex statistics beyond the scope ofthis course

For their practical use, advanced techniques and toolsare available thro MS Excel tools, SPSS and othersoftware packages

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