Demand Planning and ForecastingSession 3

Demand Forecasting Methods-1By

K. Sashi RaoManagement Teacher and Trainer

Forecasting in Business Planning

InputsMarket ConditionsCompetitor ActionConsumer TastesProducts’ Life CycleSeasonCustomers’ plans

Economic OutlookBusiness Cycle StatusLeading Indicators-Stock Prices, Bond Yields, Material Prices, Business Failures, money Supply, Unemployment

Other FactorsLegal, Political, Sociological,Cultural

Forecasting Method(s)Or Model(s)

OutputsEstimated Demands for each Product in each Time PeriodOther Outputs

Sales ForecastForecast and Demand for Each Product In Each Time Period

Processor

Production CapacityAvailable ResourcesRisk AversionExperiencePersonal Values and MotivesSocial and Cultural ValuesOther Factors

Management Team

Forecast Errors

Feedback

Forecasting Methods

Forecasting

QualitativeOr

Judgmental

QuantitativeOr

Statistical

Projective Causal

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)

Forecasting Approaches(1)• JUDGEMENTAL APPROACHES: The essence of the judgmental approach is to address the

forecasting issue by assuming that someone else knows and can tell you the right answer. They could be experts or opinion leaders.

• EXPERIMENTAL APPROACHES: When an item is "new" and when there is no other information upon which to base a forecast, is to conduct a demand experiment on a small group of customers and extrapolated to the wider population. ‘Test marketing’ is an example of this approach.

• RELATIONAL/CAUSAL APPROACHES: There is a reason why people buy our product. If we can understand what that reason (or set of reasons) is, we can use that 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.

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

Qualitative Approach

• Qualitative Approach Usually based on judgments about causal factors that underlie the demand of particular products or servicesDo not require a demand history for the product or service, therefore are useful for new products/servicesApproaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events. The approach/method that is appropriate depends on a product’s life cycle stage

• Qualitative Methods Educated guessExecutive committee consensusDelphi methodSurvey of sales forceSurvey of customersHistorical analogyMarket research

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 used for 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

Forecasting Methods-judgmental approach(b)

• Consensus thro “Delphi method ‘ especially for new product developments and technology trends forecasting

• It is the most formal judgmental method and has a well defined process and overcomes most of the problems of earlier ‘consensus by executive opinion’

• This involves sending out questionnaires to a panel of experts regarding a forecast subject. Their replies are analyzed, summarized, processed and redistributed to the panel for revisions in light of other’s arguments and viewpoints. By going thro such an iterative process say 3-4 times, the final panel forecast is considered as fairly accurate and authentic

• Yet, difficulties do exist in planning, administering and integrating member views into a meaningful whole

• Course Booklet has a separate chapter on the Delphi method( page 107 onwards)

Forecasting Methods-judgmental approach(c)

Method Short term accuracy

Medium term accuracy

Long term accuracy

Cost

Personal insights

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

Forecasting Methods- experimental approach

• Customer surveys- thro extensive formal market research using personal or mail interviews, and newly thro internet modes; also build demand models for a new product by an aggregated approach

• Consumer panels- particularly used in initial stages of product development 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 understand any problems or issues to fine-tune marketing plans and avoid costly mistakes before going in a big way

• Customer buying data bases- based on selected and accepted individuals/families on their buying behavior , patterns and expenditures captured using electronic means direct from retailer sales data; gives extensive clues on buying factors, customer attitudes, brand loyalty and brand switching and response to promotional offers

Forecasting Methods- relationship/causal approach(1)

• Its basic premise is that relationships exist between various independent demand variables( like population, income, disposable incomes, age, sex etc to consumer needs/wants/expectations( dependent variables)

• Before linking these, we need to find the nature and extent of these causes/relationships in mathematical terms as regression( linear/multiple)equations

• Once done, they can be used to forecast the dependent variable for available independent variables

• Various types of causal methods follow in next slide

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

Forecasting Methods- time series approach(1)

• Fundamentally, uses historical demand/sales data to determine 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( though in reality may not always be the case !)

• They involve statistical methods of understanding and explaining patterns in time series data( like constant series e.g. annual rainfall; trends e.g. growing expenditure with incomes; seasonal series e.g. umbrella demand during rainy season; and any random/unexplained ‘noise’ where actual value= 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)

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 due to 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

Forecasting Methods- time series approach(3)

• Basic concepts involved are those of moving averages and exponential smoothing

• 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 limited use

• A ‘moving average’ takes the average over a fixed number( by choice) of previous periods ignoring older data periods giving a sense of immediacy to the data used e.g. taking only past 3 months data as relevant for forecasting for next quarter with same weightage; later improved by weighted moving averages with unequal weightage

• All moving averages suffer in that(a) all historically used data are given same /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 a non-linear basis

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 t for demand in period t + l

L = 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 seasonality are adjusted after each demand observation

• General steps in adaptive forecasting

• Moving average

• Simple exponential smoothing

• Trend-corrected exponential smoothing (Holt’s model)

• Trend- and seasonality-corrected exponential smoothing (Winter’s model)

Moving Averages(1)• This is the simplest model of extrapolative forecasting

• Since demand varies over time, only a certain amount of historical data is relevant to the future, implying that we can ignore all observations older than some specified age

• A moving average uses this approach by taking average demand over 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 in 4th month is 135 as forecasted( their differences are forecasting errors which will 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 importance to 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 level

Lt = (Dt + Dt-1 + … + Dt-N+1) / N

Ft+1 = Lt and Ft+n = Lt

After observing the demand for period t+1, revise the estimates as follows:

Lt+1 = (Dt+1 + Dt + … + Dt-N+2) / N

Ft+2 = Lt+1

Moving Averages(3)• Include n most recent observations

• Weight equally

• Ignore older observations

weight

today123...n

1/n

Moving Averages(4)• Forecast Ft is average of n 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

ntiit

ntttt

Dn

F

DDDn

F

11

111

1

)(1

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

Un

its

n = 3

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 22383

July 17500 22033

Aug 19000 19200

Sep 18525 18467

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

Weighted Moving Averages(2)-example

Month Actual Sales Forecast

Chosen 3 months moving average

Weightage- immediate past as 0.45, then 0.30 and 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

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 or more) 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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Time Series Patterns(2)

0

10000

20000

30000

40000

50000

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

Period

Demand

Dt

Dt-bar

Time Series Patterns(3)

Time Series Patterns(4)

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

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”

DependentVariable “P”

Intercept “q”

……………………………

.Gradient “r” ( >0)

“r”(<0)

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 past demand 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 for components

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

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

Linear Regression – example(contd.)

• Linear regression equation P= q+ rS

• Using method of least squares, the regression coefficients are worked 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

• Forecast for Year 4 Q3= 346.91+ 15.59x15= 581

• Forecast for Year 4 Q4= 346.91+ 15.59x16= 596

Multiple Regression

• When there are many independent variables involved which influence a dependent variable then issues become complicated

• Then not only linear regression equations are required but also multiple regression analysis is involved where the interdependency of the various independent variables are taken into account

• These involve complex statistics beyond the scope of this course

• For their practical use, advanced techniques and tools are available thro MS Excel tools, SPSS and other software packages