Lect 04 LSCM Sterl (R0-July 16,09) Forecasting
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Transcript of Lect 04 LSCM Sterl (R0-July 16,09) Forecasting
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Forecasting
N.K.Agarwal
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All supply chain decisions based on estimates offuture demands
Historical demand information can be used to
forecast future demands For push/pull philosophy of supply chain
Push processes are performed in anticipation of demand
Pull processes performed in response to the customerdemand
Dell orders components for computers in anticipation ofcustomer demand, while
Assembly is performed in response to a customerdemand
Forecasting
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When individual stages in the supply chain maketheir independent forecast of demand, there is alwaysa mismatch between the supply and demand
Collaborative forecast for the entire chain partnerstends to be much more accurate
Decisions for functions like Production, Marketing,Finance, Personnel are best taken based oncollaborative forecast
Mature products with stable demand are usuallyeasiest to forecast Staple products like food grains, sugar at superbazars
Forecasting
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Forecasting and accompanying managerial decisionsare extremely difficult when either the supply of rawmaterials or the demand for the finished product is
highly variable Fashion garments, high tech products etc.
Good forecasting is important for products with shortlife cycle, like fashion goods
Products with a long life cycle have less significanteffect from forecasting errors
Forecasting
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Forecasts are always wrong and should include boththe expected value and a measure of the forecasterror
Long term forecasts are usually less accurate thanshort term forecasts
The greater the degree of aggregation , the moreaccurate is the forecast Easier to forecast the GNP in a year of a country within 2%
accuracy than the annual revenue of a company
The greater up the supply chain a company is, thegreater the distortion of information they receive Bullwhip effect
Forecasting- Characteristics
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Bullwhip Effect
Tier 2
Suppliers
Tier 1
SuppliersProducer Distributor Customers
Ordering
Amount ofinventory=
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Companies need to first Identify the factors that influence the future demand, and
then
Ascertain the relationship between these factors and futuredemand
Some of the factors that need to be looked into Past demand
Lead time of products
Planned advertising or marketing efforts State of economy
Planned price discounts
Action competitors have taken
Forecasting- Components
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Demand Forecasting -
Basic Forecasting Six step approach for effective forecasting
Understand the objective of forecasting
Integrate demand planning and forecasting throughout the
supply chain
Understand and identify customer segments
Identify the major factors that influence the demand forecast
Determine the appropriate forecasting technique
Establish performance and error measures for the forecast
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Qualitative Method Qualitative forecasting methods are primarily subjective and
rely on human judgment
Most appropriate when there is little historical dataavailable or when experts have market intelligence that iscritical in making forecast
Used to forecast future demand for long term in a newindustry
Time Series Use historical demand to forecast Method appropriate when the demand pattern does not
vary significantly from one year to the next
Forecasting- Methods
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Causal Method assumes that the demand forecast is highlycorrelated with certain factors in the environment
State of economy, interest rates etc.
Used to determine the impact of price promotions ondemand
Simulation Methods imitate the consumer choices that give rise to
demand to arrive at a forecast
Simulation is used to combine time series and causal
methods to find answers to Impact of price promotion, competitors stores coming upin the vicinity etc.
Forecast demand for higher fare seats when thereare no seats available at economy class fare
Modeling makes use of computers
Forecasting- Methods
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Time Series Forecasting Methods
A time series is a time-ordered sequence ofobservations taken at regular intervals over a periodof time
Data may be measurement of demand, earnings, profits,outputs etc.
Analysis of time series data requires identification ofthe underlying behaviour of the series
Done by plotting the data with time and examining for somepattern
Trend, Seasonal variations, Cycles, and Random orIrregular variations ( errors)
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Trend Refers to gradual, long term, upward or downward
movement in the data over time
Changes in income, population etc.
Seasonality Refers to short term fairly regular variations related to factors
such as weather, holidays, vacations etc.
Variations can be daily, weekly or monthly
Cycles Wave like variations of more than one years duration or
which occur every year
Business cycle related to economic, political oragricultural conditions
Random variations Residual variations which are blips in the data caused by
chance and unusual situations
Time Series Forecasting Methods
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Constant Trend
Seasonal Trend
Demand Patterns
Time
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Pattern continuous when it is constant and does notconsistently increase or decrease
Sales of a product in the mature stage of its life cycle
may show this Linear pattern emerges when demand increases or
decreases from one period to the next
Sales of product in the growth stage of the product lifecycle shows increasing while in the decline stage show
decreasing trend Cyclical pattern pertains to influence of seasonal factors
Demand of woolen wears will be high in winter and lowduring summer
Quantitative Methods
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Forecasts in time series methods based on averagessmoothened through averaging
Three techniques used for Averaging
Naive Forecasts
Simplest method
Assumption of demand for the next period based on theactual demand in the most recent period
Moving Average method Simple moving average
Weighted moving average
Time Series Forecasting Methods
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Simple Moving Average (SMA) Forecasts for the next month is the arithmetic average of the
actual sales for a specific number of recent past time periods
SMA =Sum of demands for all periods/Chosen number ofperiods
SMA = in
=1/n =(D1+D2+D3Dn)/n,
where , n=the chosen number of periods,
i= 1 is the oldest period in the n-period average
i= n is the most recent period
D1= the demand in the i th period
Time Series Forecasting Methods
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Weighted Moving Average (WMA)
A weighted average of past sales is the forecast for the nexttime period
A WMA allows for varying, not equal weightage of olddemands
WMA= in
=1 Ci Di ,
where Di is the demand during time period i, Ci is theweight given to that demand and n is the chosen
number of periods
Also 0 Ci 1 , and in
=1 Ci =1
Time Series Forecasting Methods
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Exponential Smoothing Models Forecasted sales for the last period modified by information
about the forecast error of the last periods
Modification of the last years forecasts are the forecast forthe next time periods
Weight assigned to a previous periods demanddecreases exponentially as that data gets older
Recent demand data receive a higher weight than doesthe older demand data
Normally only three items of data are required
This periods forecast, the actual demand for this periodand which is referred to as smoothening constant andhaving a value between 0 and 1
Time Series Forecasting Methods
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Formula used is
Next periods forecast = This period's forecast + ( thisperiods actual demand this periods forecast)
Or Ft =Ft-1 + ( At-1 Ft-1)
Where Ft = Forecast for this period (t)
Ft-1 = Forecast for the previous period (t-1)
At-1
= Actual demand for the previous period ( t-1)
= Smoothening constant
Smoothening constant selection is a matter of judgment
Commonly used values range between 0.05 0.5
Time Series Forecasting Methods
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Regression Analysis A forecasting technique that establishes a relationship
between variables- one dependent and others independent
Only one independent variable in simple regression
Population, advertising expenses affecting sales More than one independent variable in multiple regression
Population, income and sales force affecting sales
It involves fitting a straight line equation ( in simple linearregression analysis) to explain sales fluctuations in terms ofrelated and presumable causal variables
Three major steps in regression analysis
Identifying variables which are causally related to thefirms sales
Determine / estimate the values of these variablesrelated to sales
Derive the sales forecast from these estimates
Common Time Series Models
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A linear regression assumes the relationship betweendependent and independent variables a straight line( known a simple linear regression analysis)
A curvilinear relationship is a non-linear regressionproducing a curve
Common Time Series Models
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Forecasting- Adaptive Method
Adaptive method uses more sophisticated approachcompared to static methods
Popular models used in this method
Holts Model
This is a Trend corrected Exponential smoothened model
Appropriate when demand is assumed to have a level and atrend but no seasonality
Systematic component of demand = Level + Trend In period t, given estimate of level Lt and trend Tt, the
forecast for future periods is expressed as
Ft+1 = Lt + Tt and Ft+n = Lt+ nTt
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Forecasting- Adaptive Method
After observing for Period t, the estimate for level and trendis corrected as
Lt+1 = Dt+1 + (1- )(Lt + Tt)
Tt+1 = (Lt+1 Lt) + (1- )Tt , Where is a smoothening constant for the level, and is
a smoothening constant for trend and varies from 0 to 1like
Winters Model
Trend and Seasonality Corrected Exponential Smoothenedmodel
Method appropriate when the demand is assumed to have alevel, trend and a seasonal factor
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Systematic component of the demand= ( Level + trend) x seasonal factor
Assume periodicity of demand to be p, initial estimates oflevel L0, trend T0 and seasonal factors ( S1.Sn)
In period t, the forecast for future periods is given by
Ft+1 = (Lt + Tt)* St+1, and Ft+l = (Lt + lTt)*St+l On observing the demand for period t+1, the estimates for
level, trend and seasonal factors are revised as
Lt+1 = (Dt+1 /St+1) + (1- )(Lt-Tt)
Tt+1 = (Lt+1 Lt) + (1- )*Tt St+p+1 = (Dt+1 / Lt+1) + (1- )*St+1,
Where is a smoothening constant for seasonal factorvarying from 0 -1
Forecasting- Adaptive Method
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Measure of Forecasting Errors
Managers perform a thorough error analysis on aforecast to
Determine whether the current forecasting method is
accurately predicting the systematic components of demand A method consistently giving positive error can indicate
over prediction by the method and manager can makenecessary corrections
Estimate forecast error as any contingency plan must
account for such an error
Contracting with an outsource agency , even thoughmore expensive, to supply shortfalls in the order onurgent basis
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Measure of Forecasting Errors
Forecasting Error is simply the difference betweenthe forecast and actual demand for a given period et = Ft At , where et = forecast error for the period t,
At = actual demand for period t, and Ft = the forecast for theperiod t
Mean Error (ME) = 1/n n
t=1 et Cumulative Sum or Error (CFE) = nt=1 et CFE is useful in measuring the bias in a forecast
Mean Absolute Deviation (MAD) = 1/n nt=1 | et | MAD is merely the average error for each forecast.
Popular because it is easy to understand
Mean Squared Error (MSE) = 1/n nt=1 et2
Used as an estimate of the variance of the random error etwhich is 2
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Mean Absolute Percentage Error (MAPE)
=1/n t=1 ( |et| / At) X100
MAPE is useful for putting forecast performance in the
proper perspective Forecast error of 100 when the actual demand is 200
units results in larger percentage error than the erroroccurring when the demand was 1000 units
Measure of Forecasting Errors
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Qualitative or Judgemental
Methods Not based on quantitative numbers exclusively
Based on judgment about the causal factors that underlinethe sales of particular products or services, and
On opinions about the relative likelihood of these causalfactors being present in the future
Useful when historical data are not available
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Executive Committee Consensus A committee of executives from different departments
constituted and entrusted with the responsibility ofdeveloping a forecast
Uses inputs from all parts of organisation and analystsanalyse data as required
Such forecasts tend to be compromised ones, not reflectingthe extremes that might be present
Most commonly used method of forecast
Qualitative or Judgemental
Methods
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The Delphi Method Method seeks to remove the undesirable consequences of
group thinking existing in committees
Committee consists of experts from within and outside the
organisation Expert in one aspect of the problem and no one
conversant with all aspects of the issue
Each expert makes independent predictions in the form ofbrief statements
Coordinator edits and clarifies these statements Coordinator provides a series of questions in writing to the
experts that includes feedback supplied by other experts
Above repeated several times till consensus reached
Qualitative or JudgementalMethods
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Survey of Salesforce/ Field Expectation Method Individual members of the salesforce required to submit
sales forecasts of their respective regions
These combined to form total estimate of sales Estimates transformed into sales forecasts to ensure realistic
estimates
A popular method for companies having goodcommunication system and salesforce directly selling tocustomers
Qualitative or JudgementalMethods
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Survey of Customers/Users Expectation Method
Estimates of future sales obtained directly from customersthrough survey
Sales forecast determined by combining individualcustomers responses
Method useful where customers are limited in number
Qualitative or Judgemental
Methods
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Historical Analogy
Estimates of future sales of product tied to knowledge of asimilar products sales
Knowledge of one products sales during various stages ofits product life cycle applied to estimates of sale for a similarproduct
Method useful for a new product
Qualitative or Judgemental
Methods
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Market Surveys
Questionnaires, telephone talks or field interviews form thebasis for predicting market demand for products
Normally preferred for new products or existing products innew markets
Qualitative or Judgemental
Methods
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Demand Forecasting
Forecasting is a key driver of virtually every designand planning decision made in both an enterpriseand a supply chain
Collaborative forecasting taking all partners in thesupply chain give benefits an order of magnitudehigher than the cost
Value of data depends upon where one is in thesupply chain
Demand is not the same as sales
True demand can be obtained by making adjustments for theunmet demands due to stock outs, competitors actions,
pricing, promotions etc.
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References
Supply Chain Management : Chopra / Meindl
Logistics and Supply Chain Management :K. Shridhara Bhat
Supply Chain Management : Rahul V. Altekar
Google web site
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Thank You
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