Lect 04 LSCM Sterl (R0-July 16,09) Forecasting

8/2/2019 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



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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



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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



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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



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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



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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



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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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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



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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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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



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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


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


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


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


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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Documents

Lect 04 LSCM Sterl (R0-July 16,09) Forecasting