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Demand Planning and Forecasting Session 3

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Demand Planning and ForecastingSession 3 . Demand Forecasting Methods-1. By. K. Sashi Rao. Management Teacher and Trainer – PowerPoint PPT presentation

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Title: Demand Planning and Forecasting Session 3


1
Demand Planning and ForecastingSession 3
  • Demand Forecasting Methods-1
  • By
  • K. Sashi Rao
  • Management Teacher and Trainer

2
Forecasting in Business Planning
Inputs Market Conditions Competitor
Action Consumer Tastes Products Life
Cycle Season Customers plans Economic
Outlook Business 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
Management Team
Production Capacity Available Resources Risk
Aversion Experience Personal Values and
Motives Social and Cultural Values Other
Factors
Processor
Forecast Errors
Sales Forecast Forecast and Demand for Each
Product In Each Time Period
Feedback
3
Forecasting Methods
Forecasting
Qualitative Or Judgmental
Quantitative Or Statistical
Projective
Causal
4
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)

5
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.

6
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

7
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 products life cycle stage
  • Qualitative Methods
  • Educated guessExecutive committee
    consensusDelphi methodSurvey of sales
    forceSurvey of customersHistorical
    analogyMarket research

8
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

9
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 others 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)

10
Forecasting Methods-judgmental approach(c)
11
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

12
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

13
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

14
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)

15
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)

16
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

17
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

18
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.

19
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

20
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

21
Static Methods
  • Assume a mixed model
  • Systematic component (level trend)(seasonal
    factor)
  • Ftl L (t l)TStl
  • 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

22
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 (Holts
    model)
  • Trend- and seasonality-corrected exponential
    smoothing (Winters model)

23
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 (150130125)/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
    (130125135)/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

24
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-N1) / N
  • Ft1 Lt and Ftn Lt
  • After observing the demand for period t1,
    revise the estimates as follows
  • Lt1 (Dt1 Dt Dt-N2) / N
  • Ft2 Lt1

25
Moving Averages(3)
  • Include n most recent observations
  • Weight equally
  • Ignore older observations

weight
1/n
...
1
2
n
3
today
26
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.

27
Moving Averages(5)
n 3
28
Simple Moving Averages(6) example
29
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) 753915 129 and
    further forecast for 5th month as
    (129x0.60125x0.30130x0.10) 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

30
Weighted Moving Averages(2)-example
31
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

32
Time Series Patterns(1)
33
Time Series Patterns(2)
34
Time Series Patterns(3)
35
Time Series Patterns(4)
36
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

37
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

Dependent Variable P
.

Gradient r ( gt0)
r(lt0)
Intercept q
Independent variable S
38
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

39
Linear Regression example(contd.)
40
Linear Regression example(contd.)
41
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

42
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
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