7_demand forecasting & collaborative planning

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Supply Chain Management

Demand Forecasting & CollaborativePlanning

Topic VIITopic VII



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Forecasting & Supply ChainForecasting & Supply Chain

Management Management Need f or f orecastingNeed f or f orecasting

Estimate of future demandEstimate of future demand

Basis f or planningBasis f or planning Basis f or decision makingBasis f or decision making

Goal of f orecasting technique :Goal of f orecasting technique : Minimum deviation inMinimum deviation in

Forecasted Demand Vs. Actual DemandForecasted Demand Vs. Actual Demand



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Forecasting & Supply ChainForecasting & Supply Chain

Management Management For developing effective f orecast, oneFor developing effective f orecast, one

needs t o studyneeds t o study Fact ors that influence demandFact ors that influence demand Impact of fact orsImpact of fact ors

Will these fact ors continue t o influenceWill these fact ors continue t o influencedemanddemand

For this purpose buyers & sellers shouldFor this purpose buyers & sellers should Share relevant inf ormationShare relevant inf ormation

Generate single consensus f orecast Generate single consensus f orecast



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Benefits of better f orecastsBenefits of better f orecasts

Reduction in invent ories levelReduction in invent ories level

Reduction in st ock out Reduction in st ock out

Smoother production plansSmoother production plans

Improved Cust omer ServicesImproved Cust omer Services

Overall reduction in costsOverall reduction in costs



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CASE STUDIESCASE STUDIES Sony PS IISony PS II

Launched in 2000.Launched in 2000. Sony website crashedSony website crashed Hits exceeded 50,000Hits exceeded 50,000 Advance booking Advance booking Sony unable t o predict t remendous response f rom PS ISony unable t o predict t remendous response f rom PS I

cust omerscust omers Defending their market share against Defending their market share against

NintendoNintendo

Microsoft Microsoft X X--BoxBox

Procter & GambleProcter & Gamble Retailers lose cust omers 41% if timeRetailers lose cust omers 41% if time Groceries st oresGroceries st ores sales loss estimated up t o $6 billionsales loss estimated up t o $6 billion

annually due t o out of st ock items.annually due t o out of st ock items.



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Losses due t o poor communicationLosses due t o poor communication

Poor communication leads t o Poor communication leads t o

Inaccurate f orecastsInaccurate f orecasts

Bull whip effect Bull whip effect St ock OutsSt ock Outs

Lost SalesLost Sales

High invent ory costsHigh invent ory costs

Material ShortageMaterial Shortage

Poor response t o market needsPoor response t o market needs

Poor profitabilityPoor profitability



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Supply & Demand VariationSupply & Demand Variation

Manufacturers / BuyersManufacturers / Buyers Pull environment Pull environment

From suppliersFrom suppliers Product quantity and timeProduct quantity and time

RetailerRetailer Quantity, Price, TimeQuantity, Price, Time

St ock out St ock out Sales reductionSales reduction

Profit lossProfit loss

Cust omer relationshipCust omer relationship



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Conventional methods of matchingConventional methods of matching

supply & demandsupply & demand To hold plenty of st ockTo hold plenty of st ock

Maximum Sales revenueMaximum Sales revenue

Higher level of costsHigher level of costs

Write downs at end of seasonWrite downs at end of season

Flexible PricingFlexible Pricing Price go up during heavy demand periodPrice go up during heavy demand period

Lost sales due t o competitionLost sales due t o competition

St ock out St ock out

Price discount during low demand period with excessivePrice discount during low demand period with excessiveinvent oryinvent ory Lo profitabilityLo profitability

NonNon--partnership f riendly approachpartnership f riendly approach



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Sales Forecast Sales Forecast

estimate of sales (dollars/physical units) of specifiedestimate of sales (dollars/physical units) of specifiedcompanycompany

f or a future periodf or a future period

under particular marketing programmeunder particular marketing programme

under assumed set of economic fact orsunder assumed set of economic fact ors

could be f or a single product / entire product linecould be f or a single product / entire product line

could be f or entire marketing department or f or subcould be f or entire marketing department or f or sub--

divisiondivision short term (in nature) quarterly, annuallyshort term (in nature) quarterly, annually

also termed as operating sales f orecasts.also termed as operating sales f orecasts.



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Forecasting TechniquesForecasting Techniques

Improvement in f orecast byImprovement in f orecast by

Qualitative TechniquesQualitative Techniques based onbased on

opinions,opinions,

intuition,intuition,

when data not available,when data not available,

low cost,low cost,

skill and expertise of f orecasting is important.skill and expertise of f orecasting is important.



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Forecasting TechniquesForecasting Techniques

QuantitativeQuantitative

mathematical models,mathematical models,

analysis of hist orical data such asanalysis of hist orical data such as Time seriesTime series

Associative Models such as moving averages and simple Associative Models such as moving averages and simplet rend.t rend.



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Qualitative TechniquesQualitative Techniques



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Executive Board OpinionExecutive Board Opinion

Senior Management group makes the salesSenior Management group makes the salesf orecast as they have the knowledge of f orecast as they have the knowledge of

Indust ry outlookIndust ry outlook Companys position /capabilityCompanys position /capability

Future marketing programme of companyFuture marketing programme of company

Suitable whenSuitable when

quick and easy methodquick and easy method

suitable especially if no hist orical data availablesuitable especially if no hist orical data available

pooling of experiencepooling of experience



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Delphi MethodDelphi Method

Similar t o the above methodSimilar t o the above method

Internal and external experts are surveyedInternal and external experts are surveyed

who are not physically present who are not physically present Answers accumulated and then sent t o each Answers accumulated and then sent t o each

expert expert

Each participant can modify their response onEach participant can modify their response on

other members opinionsother members opinions Especially useful f or high risk and largeEspecially useful f or high risk and large

projects and new product int roductionprojects and new product int roduction



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LimitationsLimitations

But these methods has some limitationsBut these methods has some limitations

based on opinionbased on opinion

one member can dominate the boardone member can dominate the board

lack of factual evidencelack of factual evidence

workload on executives increaseworkload on executives increase



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Sales f orce Composite / Poll of Sales f orce Composite / Poll of

sales f orce opinionsales f orce opinion Individual sales person f orecast f or their respectiveIndividual sales person f orecast f or their respective

territ ory combined and modified as per management territ ory combined and modified as per management perceptionperception

Responsibility of f orecasting assigned t o those who areResponsibility of f orecasting assigned t o those who arerequired t o produce the result required t o produce the result

Sales f orce operate closely t o market conditionsSales f orce operate closely t o market conditions

Have more confidence in meeting sales targets basedHave more confidence in meeting sales targets based

on this f orecast on this f orecast Easy t o breakdown this f orecast int o per product andEasy t o breakdown this f orecast int o per product and

per territ oryper territ ory



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LimitationsLimitations

Sales f orce usually is not t rained enough t o calculate the f orecast Sales f orce usually is not t rained enough t o calculate the f orecast Sales f orce estimate could be overly optimistic or pessimistic. ThereSales f orce estimate could be overly optimistic or pessimistic. There

could be individual biases and hidden motives t o reach target morecould be individual biases and hidden motives t o reach target moreeasilyeasily

Sales f orce are usually unaware of broad economic changes whichSales f orce are usually unaware of broad economic changes whicheffect sales business greatlyeffect sales business greatly

Hence there is a need t o ad just this f orecast f or biases and t rain theHence there is a need t o ad just this f orecast f or biases and t rain thesales f orce.sales f orce.

But high turnover tendencies among sale people makes this difficult.But high turnover tendencies among sale people makes this difficult. However this method can still be used very beneficially as anHowever this method can still be used very beneficially as an

alternative t ool t o benchmark the f orecast arrived at through other alternative t ool t o benchmark the f orecast arrived at through other methods.methods.



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Survey of Cust omer Buying PlanSurvey of Cust omer Buying Plan

This method is based on the assumption that cust omers knowThis method is based on the assumption that cust omers knowwhat what

they are going t o buy (i.e. future buying plans are already f ormed)they are going t o buy (i.e. future buying plans are already f ormed) once made these plans will not changeonce made these plans will not change

more useful f or indust rial marketer wheremore useful f or indust rial marketer where there are limited number of cust omers and prospectsthere are limited number of cust omers and prospects substantial amount of sales is made t o individual accountssubstantial amount of sales is made t o individual accounts ma jority of sales are made directly t o usersma jority of sales are made directly t o users who are concent rated in few geographical areaswho are concent rated in few geographical areas

Input taken f rom cust omers include future buying plans, newInput taken f rom cust omers include future buying plans, newproduct ideas and opinions /feedback about existing products of product ideas and opinions /feedback about existing products of the company.the company.



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Survey of Cust omer Buying PlanSurvey of Cust omer Buying Plan

If the number of cust omers is large then it is not If the number of cust omers is large then it is not economical enough t o conduct the survey f or everyeconomical enough t o conduct the survey f or everycust omer.cust omer.

Instead a sample needs t o be taken which then arisesInstead a sample needs t o be taken which then arisesthe issue of selection of those respondents who reflect the issue of selection of those respondents who reflect the entire cust omer base accurately.the entire cust omer base accurately.

This issue can create non sampling error.This issue can create non sampling error. Moreover the f orecast derived f rom this method needs t o Moreover the f orecast derived f rom this method needs t o

be ad justed with thebe ad justed with the specialised knowledge of market business conditions andspecialised knowledge of market business conditions and changes in the marketing programme of the company and itschanges in the marketing programme of the company and its

competit ors.competit ors.



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Quantitative MethodsQuantitative Methods



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Quantitative MethodsQuantitative Methods

projection of next periods salesprojection of next periods sales

based on hist orical data (ext rapolatingbased on hist orical data (ext rapolatingpast int o future)past int o future)

assumption is that future is an extensionassumption is that future is an extensionof past of past



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Time Series AnalysisTime Series Analysis

statistical procedure f or studying hist orical sales datastatistical procedure f or studying hist orical sales data

involves isolating and measuring 4 chief types of salesinvolves isolating and measuring 4 chief types of salesvariations or componentsvariations or components

These areThese are

Trend VariationsTrend Variations increasing or decreasing movements over many years due t o increasing or decreasing movements over many years due t o

fact ors such asfact ors such as population growth / shiftspopulation growth / shifts

cultural and lifestyle changescultural and lifestyle changes

income level shiftsincome level shifts



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Time Series AnalysisTime Series Analysis

Cyclical VariationsCyclical Variations wave like movements which are usually longer than 1 year.wave like movements which are usually longer than 1 year.

These are influenced by macro economic and political fact ors.These are influenced by macro economic and political fact ors.

Example : 2004Example : 2004--05R

eal estate boom, Sept 2001 terrorist attack,05R

eal estate boom, Sept 2001 terrorist attack,1997 Asian economies crises.1997 Asian economies crises.

Seasonal VariationsSeasonal Variations these are the highs (peaks) and lows (valleys) which repeat after these are the highs (peaks) and lows (valleys) which repeat after

consistent interval such as hours/days/weeks/monthsconsistent interval such as hours/days/weeks/months

Example restaurant business has peak hours during breakfast,Example restaurant business has peak hours during breakfast,lunch, hilunch, hi--tea, dinner and local holidays or weekends.tea, dinner and local holidays or weekends.

Random VariationsRandom Variations variations due t o unexpected and unpredictable events such asvariations due t o unexpected and unpredictable events such as

natural disasters, hurricanes, tsunami, earthquakes.natural disasters, hurricanes, tsunami, earthquakes.



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Time Series Forecasting ModelsTime Series Forecasting Models

Simple moving averages f orecasting modelSimple moving averages f orecasting model This can be done by calculating the mean of theThis can be done by calculating the mean of the

previous periods salesprevious periods sales F t+1 =F t+1 = A t / n A t / n Although it is a simple t o use and easy t o understand Although it is a simple t o use and easy t o understand

methodmethod This method has some limitations:This method has some limitations:

random event or variation in 1 period can effect the averagerandom event or variation in 1 period can effect the average

adverselyadversely this method is more responsive only when fewer data points arethis method is more responsive only when fewer data points are

usedused inability t o respond t o t rend changes quicklyinability t o respond t o t rend changes quickly



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Weighted moving averages f orecasting modelWeighted moving averages f orecasting model

Weights are assigned t o different data point f or different Weights are assigned t o different data point f or different periods and weighted average is calculatedperiods and weighted average is calculated

F t+1 =F t+1 = w i A i / nw i A i / n w i = 1w i = 1 The sum of all weights assigned t o different periods is equal t o The sum of all weights assigned t o different periods is equal t o

1.1.

this method makes its possible t o put more emphasis on recent this method makes its possible t o put more emphasis on recent datadata

assigning of weights is however based on experience of assigning of weights is however based on experience of f orecasterf orecaster

again t rend changed not t racked comprehensivelyagain t rend changed not t racked comprehensively

f orecast still lags demand because of averaging effect f orecast still lags demand because of averaging effect



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Exponential Smoothing MethodExponential Smoothing Method is a weighted moving average sales f orecastingis a weighted moving average sales f orecasting

techniquetechnique sales f orecast f or next periods is estimated by ad justingsales f orecast f or next periods is estimated by ad justing

the current periods f orecast by f raction of differencethe current periods f orecast by f raction of differencebetween current periods actual sales and its f orecast i.e.between current periods actual sales and its f orecast i.e.

F t+1 = F t +F t+1 = F t + (A t (A t -- F t )F t ) near 1near 1 means greater emphasis on recent datameans greater emphasis on recent data

far 1far 1 more weight on past datamore weight on past data Summing up this method is a partial ad justment t o most recent Summing up this method is a partial ad justment t o most recent

f orecast error.f orecast error. However it still may lag any t rend present in actual dataHowever it still may lag any t rend present in actual data



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Associative Forecasting Models Associative Forecasting Models

Under this heading we will discuss the most common techniquesUnder this heading we will discuss the most common techniques

Simple RegressionSimple Regression It is calculated through the equationIt is calculated through the equation

^̂ Y = bo + Y = bo + bb1 x1 x Where bo is intercept valueWhere bo is intercept value b1 is slopeb1 is slope ^̂ Y is dependent variable (f or e.g. Sales) Y is dependent variable (f or e.g. Sales) bb 1 is independent variable ( f or e.g. Advertising)1 is independent variable ( f or e.g. Advertising)

This method will determine a linear relationship between a dependent variable andThis method will determine a linear relationship between a dependent variable andan independent variable e.g. between advertising and sales. So this method will findan independent variable e.g. between advertising and sales. So this method will findout one dollar increase in advertising will lead t o how much increase in sales.out one dollar increase in advertising will lead t o how much increase in sales.

For example sales of tires can depend on sales of aut omobiles.For example sales of tires can depend on sales of aut omobiles.



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Multiple RegressionMultiple Regression If there are 2 or more variables then multipleIf there are 2 or more variables then multiple

regression can be used. The equation will beregression can be used. The equation will be^̂

Y = bo + Y = bo + bb 1 x1 +1 x1 + bb2 x22 x2 So this can determine and measure a companySo this can determine and measure a company

sales in relation t o other variables.sales in relation t o other variables. Such an equation can help explain salesSuch an equation can help explain sales

fluctuations in terms of related and casualfluctuations in terms of related and casualvariables.variables.



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To sum upTo sum up

Forecaster needs inf ormation about Forecaster needs inf ormation about

competit ors plans t o launch new and improvedcompetit ors plans t o launch new and improvedproductsproducts

advertising and selling plans of the companyadvertising and selling plans of the company

pricing st rategies of the companypricing st rategies of the company

In the above methods we need t o examine bothIn the above methods we need t o examine both Past and future t rendsPast and future t rends

Impending changes in competitive relationshipsImpending changes in competitive relationships



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Limitations of Quantitative MethodsLimitations of Quantitative Methods

Quantitative f orecasts rely on past Quantitative f orecasts rely on past demand datademand data

All quantitative methods become less All quantitative methods become lessaccurate as f orecast time horizonaccurate as f orecast time horizonincreases.increases.

R

ecommendationR

ecommendation f or long term timef or long term timehorizon a combination of qualitative andhorizon a combination of qualitative andquantitative techniques may be used.quantitative techniques may be used.



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7_demand forecasting & collaborative planning

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