managing inventory Forecasting

© 2006 Prentice Hall, Inc. 4 – 1

FORECASTINGFORECASTING

Synonyms of the word ‘Forecast’Synonyms of the word ‘Forecast’ PredictPredict

EstimateEstimate

ProjectProject

CalculateCalculate

AnticipateAnticipate

ForetellForetell

GuessGuess

ConjectureConjecture


OutlineOutline

Chapter 4 (Main text book)Chapter 4 (Main text book)

Global Company Profile: Tupperware Global Company Profile: Tupperware CorporationCorporation

What Is Forecasting?What Is Forecasting? Forecasting Time HorizonsForecasting Time Horizons

The Influence of Product Life CycleThe Influence of Product Life Cycle

Types Of ForecastsTypes Of Forecasts


Outline – ContinuedOutline – Continued

The Strategic Importance Of The Strategic Importance Of ForecastingForecasting Human ResourcesHuman Resources

CapacityCapacity

Supply-Chain ManagementSupply-Chain Management

Seven Steps In The Forecasting Seven Steps In The Forecasting SystemSystem



Forecasting ApproachesForecasting Approaches Overview of Qualitative MethodsOverview of Qualitative Methods

Overview of Quantitative MethodsOverview of Quantitative Methods


Outline – ContinuedOutline – Continued Time-series ForecastingTime-series Forecasting

Decomposition of a Time SeriesDecomposition of a Time Series Naïve ApproachNaïve Approach Moving AveragesMoving Averages Exponential SmoothingExponential Smoothing Exponential Smoothing with Trend Exponential Smoothing with Trend

AdjustmentAdjustment Trend ProjectionsTrend Projections Seasonal Variations in DataSeasonal Variations in Data Cyclical Variations in DataCyclical Variations in Data



Associative Forecasting Methods: Associative Forecasting Methods: Regression and Correlation Regression and Correlation AnalysisAnalysis Using Regression Analysis to Using Regression Analysis to

ForecastForecast

Standard Error of the EstimateStandard Error of the Estimate

Correlation Coefficients for Correlation Coefficients for Regression LinesRegression Lines

Multiple-Regression AnalysisMultiple-Regression Analysis



Monitoring And Controlling Monitoring And Controlling ForecastsForecasts Adaptive SmoothingAdaptive Smoothing

Focus ForecastingFocus Forecasting


Learning ObjectivesLearning Objectives

When you complete this chapter, you When you complete this chapter, you should be able to :should be able to :

Identify or Define:Identify or Define:

Forecasting Forecasting

Types of forecasts Types of forecasts

Time horizons Time horizons

Approaches to forecastsApproaches to forecasts


Learning ObjectivesLearning Objectives

When you complete this chapter, you When you complete this chapter, you should be able to :should be able to :

Describe or Explain:Describe or Explain:

Moving averagesMoving averages

Exponential smoothingExponential smoothing

Trend projectionsTrend projections

Regression and correlation analysisRegression and correlation analysis

Measures of forecast accuracyMeasures of forecast accuracy


Forecasting at TupperwareForecasting at Tupperware

Each of 50 profit centers around the Each of 50 profit centers around the world is responsible for world is responsible for computerized monthly, quarterly, computerized monthly, quarterly, and 12-month sales projectionsand 12-month sales projections

These projections are aggregated by These projections are aggregated by region, then globally, at region, then globally, at Tupperware’s World HeadquartersTupperware’s World Headquarters

Tupperware uses all techniques Tupperware uses all techniques discussed in textdiscussed in text


TupperTupperware’s ware’s ProcessProcess


Three Key Factors for Three Key Factors for TupperwareTupperware

The number of registered The number of registered “consultants” or sales “consultants” or sales representativesrepresentatives

The percentage of currently “active” The percentage of currently “active” dealers (this number changes each dealers (this number changes each week and month)week and month)

Sales per active dealer, on a weekly Sales per active dealer, on a weekly basisbasis


Forecast by ConsensusForecast by Consensus

Although inputs come from sales, Although inputs come from sales, marketing, finance, and production, marketing, finance, and production, final forecasts are the consensus of final forecasts are the consensus of all participating managersall participating managers

The final step is Tupperware’s The final step is Tupperware’s version of the “version of the “jury of executive jury of executive opinion”opinion”


What is Forecasting?What is Forecasting?

Process of Process of predicting a future predicting a future eventevent

Underlying basis of Underlying basis of

all business all business decisionsdecisions ProductionProduction

InventoryInventory

PersonnelPersonnel

FacilitiesFacilities

??


We may classify forecasting We may classify forecasting problems along several dimensions:problems along several dimensions: Time horizonTime horizon

Sector or Type – Economic , Sector or Type – Economic , Technological, or demand forecastsTechnological, or demand forecasts

Method (approach) adopted – Method (approach) adopted – subjective (qualitative) or objective subjective (qualitative) or objective (quantitative)(quantitative)

Forecasting DimensionsForecasting Dimensions


Short-range forecastShort-range forecast Up to 1 year, generally less than 3 monthsUp to 1 year, generally less than 3 months Purchasing, job scheduling, workforce Purchasing, job scheduling, workforce

levels, job assignments, production levelslevels, job assignments, production levels

Medium-range forecastMedium-range forecast 3 months to 3 years3 months to 3 years Sales and production planning, budgetingSales and production planning, budgeting

Long-range forecastLong-range forecast 33++ years years New product planning, facility location, New product planning, facility location,

research and developmentresearch and development

Forecasting Time HorizonsForecasting Time Horizons


Distinguishing DifferencesDistinguishing Differences

Medium/long rangeMedium/long range forecasts deal with forecasts deal with more comprehensive issues and support more comprehensive issues and support management decisions regarding management decisions regarding planning and products, plants and planning and products, plants and processesprocesses

Short-termShort-term forecasting usually employs forecasting usually employs different methodologies than longer-term different methodologies than longer-term forecastingforecasting

Short-termShort-term forecasts tend to be more forecasts tend to be more accurate than longer-term forecastsaccurate than longer-term forecasts


Influence of Product Life Influence of Product Life CycleCycle

Introduction and growth require longer Introduction and growth require longer forecasts than maturity and declineforecasts than maturity and decline

As product passes through life cycle, As product passes through life cycle, forecasts are useful in projectingforecasts are useful in projecting Staffing levelsStaffing levels

Inventory levelsInventory levels

Factory capacityFactory capacity

Introduction – Growth – Maturity – Decline


Product Life CycleProduct Life Cycle

Best period to Best period to increase market increase market shareshare

R&D engineering is R&D engineering is criticalcritical

Practical to change Practical to change price or quality price or quality imageimage

Strengthen nicheStrengthen niche

Poor time to Poor time to change image, change image, price, or qualityprice, or quality

Competitive costs Competitive costs become criticalbecome criticalDefend market Defend market positionposition

Cost control Cost control criticalcritical

Introduction Growth Maturity Decline

Co

mp

an

y S

tra

teg

y/Is

sue

sC

om

pa

ny

Str

ate

gy/

Issu

es

InternetInternet

Flat-screen Flat-screen monitorsmonitors

SalesSales

DVDDVD

CD-ROMCD-ROM

Drive-through Drive-through restaurantsrestaurants

Fax machinesFax machines

3 1/2” 3 1/2” Floppy Floppy disksdisks

Color printersColor printers

Figure 2.5Figure 2.5


Product Life CycleProduct Life Cycle

Product design Product design and and development development criticalcritical

Frequent Frequent product and product and process design process design changeschanges

Short production Short production runsruns

High production High production costscosts

Limited modelsLimited models

Attention to Attention to qualityquality

Introduction Growth Maturity Decline

OM

Str

ate

gy

/Issu

es

OM

Str

ate

gy

/Issu

es

Forecasting Forecasting criticalcritical

Product and Product and process process reliabilityreliability

Competitive Competitive product product improvements improvements and optionsand options

Increase capacityIncrease capacity

Shift toward Shift toward product focusproduct focus

Enhance Enhance distributiondistribution

StandardizationStandardization

Less rapid Less rapid product changes product changes – more minor – more minor changeschanges

Optimum Optimum capacitycapacity

Increasing Increasing stability of stability of processprocess

Long production Long production runsruns

Product Product improvement and improvement and cost cuttingcost cutting

Little product Little product differentiationdifferentiation

Cost Cost minimizationminimization

Overcapacity Overcapacity in the in the industryindustry

Prune line to Prune line to eliminate eliminate items not items not returning returning good margingood margin

Reduce Reduce capacitycapacity



Types of ForecastsTypes of Forecasts

Economic forecastsEconomic forecasts Address business cycle – inflation rate, Address business cycle – inflation rate,

money supply, housing starts, etc.money supply, housing starts, etc.

Technological forecastsTechnological forecasts Predict rate of technological progressPredict rate of technological progress

Impacts development of new productsImpacts development of new products

Demand forecastsDemand forecasts Predict sales of existing productPredict sales of existing product


Strategic Importance of Strategic Importance of ForecastingForecasting

Human Resources – Hiring, training, Human Resources – Hiring, training, laying off workerslaying off workers

Capacity – Capacity shortages can Capacity – Capacity shortages can result in undependable delivery, loss result in undependable delivery, loss of customers, loss of market shareof customers, loss of market share

Supply-Chain Management – Good Supply-Chain Management – Good supplier relations and price advancesupplier relations and price advance


Seven Steps in ForecastingSeven Steps in Forecasting

Determine the use of the forecastDetermine the use of the forecast

Select the items to be forecastedSelect the items to be forecasted

Determine the time horizon of the Determine the time horizon of the forecastforecast

Select the forecasting model(s)Select the forecasting model(s)

Gather the dataGather the data

Make the forecastMake the forecast

Validate and implement resultsValidate and implement results


The Realities! (Characteristics The Realities! (Characteristics of Forecasts)of Forecasts)

Forecasts are seldom perfectForecasts are seldom perfect. (They . (They usually appear with error.)usually appear with error.)

Most techniques assume an Most techniques assume an underlying stability in the systemunderlying stability in the system. . (A good forecast is more than a (A good forecast is more than a single number - includes some single number - includes some measure of anticipated error)measure of anticipated error)


The Realities! (Characteristics The Realities! (Characteristics of Forecasts)of Forecasts)

Product family and aggregated forecasts are Product family and aggregated forecasts are more accurate than individual product more accurate than individual product forecastsforecasts

The longer the forecast horizon, the less The longer the forecast horizon, the less accurate the forecast will be. accurate the forecast will be. (tomorrow’s (tomorrow’s prediction of Dow Jones’ index vs next year’s prediction of Dow Jones’ index vs next year’s prediction)prediction)

Forecasts should not be used to the Forecasts should not be used to the

exclusion of known informationexclusion of known information


Forecasting ApproachesForecasting Approaches

Used when situation is vague Used when situation is vague and little data existand little data exist New productsNew products

New technologyNew technology

Involves intuition, judgment and Involves intuition, judgment and experienceexperience e.g., forecasting sales on Internete.g., forecasting sales on Internet

Qualitative MethodsQualitative Methods


Forecasting ApproachesForecasting Approaches

Used when situation is ‘stable’ and Used when situation is ‘stable’ and historical data existhistorical data exist Existing productsExisting products

Current technologyCurrent technology

Involves mathematical techniquesInvolves mathematical techniques e.g., forecasting sales of color e.g., forecasting sales of color

televisionstelevisions

Quantitative MethodsQuantitative Methods


Overview of Qualitative Overview of Qualitative MethodsMethods

Jury of executive opinionJury of executive opinion Pool opinions of high-level Pool opinions of high-level

executives, sometimes augment by executives, sometimes augment by statistical modelsstatistical models

Delphi methodDelphi method Panel of experts, queried iterativelyPanel of experts, queried iteratively


Overview of Qualitative Overview of Qualitative MethodsMethods

Sales force compositeSales force composite Estimates from individual Estimates from individual

salespersons are reviewed for salespersons are reviewed for reasonableness, then aggregated reasonableness, then aggregated

Consumer Market SurveyConsumer Market Survey Ask the customerAsk the customer


Involves small group of high-level Involves small group of high-level managersmanagers

Group estimates demand by working Group estimates demand by working togethertogether

Combines managerial experience with Combines managerial experience with statistical modelsstatistical models

Relatively quickRelatively quick

‘‘Group-think’Group-think’disadvantagedisadvantage

Jury of Executive OpinionJury of Executive Opinion


Delphi MethodDelphi Method

Iterative group Iterative group process, process, continues until continues until consensus is consensus is reachedreached

3 types of 3 types of participantsparticipants Decision makersDecision makers StaffStaff RespondentsRespondents

Staff(Administering

survey)

Decision Makers(Evaluate

responses and make decisions)

Respondents(People who can make valuable

judgments)


Sales Force CompositeSales Force Composite

Each salesperson projects his or Each salesperson projects his or her salesher sales

Combined at district and national Combined at district and national levelslevels

Sales reps know customers’ wantsSales reps know customers’ wants

Tends to be Tends to be overly optimisticoverly optimistic


Consumer Market SurveyConsumer Market Survey

Ask customers about purchasing Ask customers about purchasing plansplans

What consumers say, and what What consumers say, and what they actually do are often differentthey actually do are often different

Sometimes difficult to answerSometimes difficult to answer


Overview of Quantitative Overview of Quantitative ApproachesApproaches

1.1. Naive approachNaive approach

2.2. Moving averagesMoving averages

3.3. Exponential Exponential smoothingsmoothing

4.4. Trend projectionTrend projection

5.5. Linear regressionLinear regression

Time-Series Time-Series ModelsModels

Associative Associative ModelModel


Set of evenly spaced numerical Set of evenly spaced numerical datadata Obtained by observing response Obtained by observing response

variable at regular time periodsvariable at regular time periods

Forecast based only on past Forecast based only on past valuesvalues Assumes that factors influencing Assumes that factors influencing

past and present will continue past and present will continue influence in futureinfluence in future

Time Series ForecastingTime Series Forecasting


Trend

Seasonal

Cyclical

Random

Time Series ComponentsTime Series Components


Components of DemandComponents of DemandD

eman

d f

or

pro

du

ct o

r se

rvic

e

| | | |1 2 3 4

Year

Average demand over four years

Seasonal peaks

Trend component

Actual demand

Random variation



Persistent, overall upward or Persistent, overall upward or downward patterndownward pattern

Changes due to population, Changes due to population, technology, age, culture, etc.technology, age, culture, etc.

Typically several years Typically several years duration duration

Trend ComponentTrend Component


Regular pattern of up and Regular pattern of up and down fluctuationsdown fluctuations

Due to weather, customs, etc.Due to weather, customs, etc.

Occurs within a single year Occurs within a single year

Seasonal ComponentSeasonal Component

Number ofPeriod Length Seasons

Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12Year Week 52


Repeating up and down movementsRepeating up and down movements

Affected by business cycle, political, Affected by business cycle, political, and economic factorsand economic factors

Multiple years durationMultiple years duration

Often causal or Often causal or associative associative relationshipsrelationships

Cyclical ComponentCyclical Component

00 55 1010 1515 2020


Erratic, unsystematic, ‘residual’ Erratic, unsystematic, ‘residual’ fluctuationsfluctuations

Due to random variation or Due to random variation or unforeseen eventsunforeseen events

Short duration and Short duration and non-repeating non-repeating

Random ComponentRandom Component

MM TT WW TT FF


Naive ApproachNaive Approach

Assumes demand in next period is Assumes demand in next period is the same as demand in most the same as demand in most recent periodrecent period e.g., If May sales were 48, then June e.g., If May sales were 48, then June

sales will be 48sales will be 48

Sometimes cost effective and Sometimes cost effective and efficientefficient


MA is a series of arithmetic means MA is a series of arithmetic means

Used if little or no trendUsed if little or no trend

Used often for smoothingUsed often for smoothingProvides overall impression of data Provides overall impression of data

over timeover time

Moving Average MethodMoving Average Method

Moving average =Moving average =∑∑ demand in previous n periodsdemand in previous n periods

nn


JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626

ActualActual 3-Month3-MonthMonthMonth Shed SalesShed Sales Moving AverageMoving Average

(12 + 13 + 16)/3 = 13 (12 + 13 + 16)/3 = 13 22//33

(13 + 16 + 19)/3 = 16(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 (16 + 19 + 23)/3 = 19 11//33

Moving Average ExampleMoving Average Example

101012121313

((1010 + + 1212 + + 1313)/3 = 11 )/3 = 11 22//33


Graph of Moving AverageGraph of Moving Average

| | | | | | | | | | | |

JJ FF MM AA MM JJ JJ AA SS OO NN DD

Sh

ed S

ales

Sh

ed S

ales

30 30 –28 28 –26 26 –24 24 –22 22 –20 20 –18 18 –16 16 –14 14 –12 12 –10 10 –

Actual Actual SalesSales

Moving Moving Average Average ForecastForecast


Used when trend is present Used when trend is present Older data usually less importantOlder data usually less important

Weights based on experience and Weights based on experience and intuitionintuition

Weighted Moving AverageWeighted Moving Average

WeightedWeightedmoving averagemoving average ==

∑∑ ((weight for period nweight for period n)) x x ((demand in period ndemand in period n))

∑∑ weightsweights


JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626

ActualActual 3-Month Weighted3-Month WeightedMonthMonth Shed SalesShed Sales Moving AverageMoving Average

[(3 x 16) + (2 x 13) + (12)]/6 = 14[(3 x 16) + (2 x 13) + (12)]/6 = 1411//33

[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 20[(3 x 23) + (2 x 19) + (16)]/6 = 2011//22

Weighted Moving AverageWeighted Moving Average

101012121313

[(3 x [(3 x 1313) + (2 x ) + (2 x 1212) + () + (1010)]/6 = 12)]/6 = 1211//66

Weights Applied Period

3 Last month2 Two months ago1 Three months ago6 Sum of weights


Moving Average And Moving Average And Weighted Moving AverageWeighted Moving Average

30 30 –

25 25 –

20 20 –

15 15 –

10 10 –

5 5 –

Sa

les

de

man

dS

ale

s d

em

and

| | | | | | | | | | | |


Actual Actual salessales

Moving Moving averageaverage

Weighted Weighted moving moving averageaverage



Increasing n smoothens the Increasing n smoothens the forecast but makes it less sensitive forecast but makes it less sensitive to changesto changes

Do not forecast trends wellDo not forecast trends well

Require extensive historical dataRequire extensive historical data

Potential Problems WithPotential Problems With Moving Average Moving Average


Form of weighted moving averageForm of weighted moving average Weights decline exponentiallyWeights decline exponentially

Most recent data weighted mostMost recent data weighted most

Requires smoothing constant Requires smoothing constant (()) Ranges from 0 to 1Ranges from 0 to 1

Subjectively chosenSubjectively chosen

Involves little record keeping of past Involves little record keeping of past datadata

Exponential SmoothingExponential Smoothing


Exponential SmoothingExponential Smoothing

New forecast =New forecast = last period’s forecastlast period’s forecast+ + ((last period’s actual demand last period’s actual demand

– – last period’s forecastlast period’s forecast))

FFtt = F = Ft t – 1– 1 + + ((AAt t – 1– 1 - - F Ft t – 1– 1))

wherewhere FFtt == new forecastnew forecast

FFt t – 1– 1 == previous forecastprevious forecast

== smoothing (or weighting) smoothing (or weighting) constant constant (0 (0 1) 1)


Exponential Smoothing Exponential Smoothing ExampleExample

Predicted demand Predicted demand = 142= 142 Ford Mustangs Ford MustangsActual demand Actual demand = 153= 153Smoothing constant Smoothing constant = .20 = .20




New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)




New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)

= 142 + 2.2= 142 + 2.2

= 144.2 ≈ 144 cars= 144.2 ≈ 144 cars


Effect ofEffect of Smoothing Constants Smoothing Constants

Weight Assigned toWeight Assigned to

MostMost 2nd Most2nd Most 3rd Most3rd Most 4th Most4th Most 5th Most5th MostRecentRecent RecentRecent RecentRecent RecentRecent RecentRecent

SmoothingSmoothing PeriodPeriod PeriodPeriod PeriodPeriod PeriodPeriod PeriodPeriodConstantConstant (()) (1 - (1 - )) (1 - (1 - ))22 (1 - (1 - ))33 (1 - (1 - ))44

= .1= .1 .1.1 .09.09 .081.081 .073.073 .066.066

= .5= .5 .5.5 .25.25 .125.125 .063.063 .031.031


Impact of Different Impact of Different

225 225 –

200 200 –

175 175 –

150 150 –| | | | | | | | |

11 22 33 44 55 66 77 88 99

QuarterQuarter

De

ma

nd

De

ma

nd

= .1= .1

Actual Actual demanddemand

= .5= .5


Choosing Choosing

The objective is to obtain the most The objective is to obtain the most accurate forecast no matter the accurate forecast no matter the techniquetechnique

We generally do this by selecting the We generally do this by selecting the model that gives us the lowest forecast model that gives us the lowest forecast errorerror

Forecast errorForecast error = Actual demand - Forecast value= Actual demand - Forecast value

= A= Att - F - Ftt


Common Measures of ErrorCommon Measures of Error

Mean Absolute Deviation Mean Absolute Deviation ((MADMAD))

MAD =MAD =∑∑ |actual - forecast||actual - forecast|

nn

Mean Squared Error Mean Squared Error ((MSEMSE))

MSE =MSE =∑∑ ((forecast errorsforecast errors))22

nn


Common Measures of ErrorCommon Measures of Error

Mean Absolute Percent Error Mean Absolute Percent Error ((MAPEMAPE))

MAPE =MAPE =100 100 ∑∑ |actual |actualii - forecast - forecastii|/actual|/actualii

nn

nn

i i = 1= 1


Comparison of Forecast Comparison of Forecast Error Error

RoundedRounded AbsoluteAbsolute RoundedRounded AbsoluteAbsoluteActualActual ForecastForecast DeviationDeviation ForecastForecast DeviationDeviation

TonnageTonnage withwith forfor withwith forforQuarterQuarter UnloadedUnloaded = .10 = .10 = .10 = .10 = .50 = .50 = .50 = .50

11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100



RoundedRounded AbsoluteAbsolute RoundedRounded AbsoluteAbsoluteActualActual ForecastForecast DeviationDeviation ForecastForecast DeviationDeviationTonageTonage withwith forfor withwith forfor

QuarterQuarter UnloadedUnloaded = .10 = .10 = .10 = .10 = .50 = .50 = .50 = .50

11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100

MAD =∑ |deviations|

n

= 84/8 = 10.50

For = .10

= 100/8 = 12.50

For = .50





11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100MADMAD 10.5010.50 12.5012.50

= 1,558/8 = 194.75

For = .10

= 1,612/8 = 201.50

For = .50

MSE =∑ (forecast errors)2

n





11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100MADMAD 10.5010.50 12.5012.50MSEMSE 194.75194.75 201.50201.50

= 45.62/8 = 5.70%

For = .10

= 54.8/8 = 6.85%

For = .50

MAPE =100 ∑ |deviationi|/actuali

n

n

i = 1



RoundedRounded AbsoluteAbsolute RoundedRounded AbsoluteAbsoluteActualActual ForecastForecast DeviationDeviation ForecastForecast DeviationDeviation

TonnageTonnage withwith forfor withwith forforQuarterQuarter UnloadedUnloaded = .10 = .10 = .10 = .10 = .50 = .50 = .50 = .50

11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100MADMAD 10.5010.50 12.5012.50MSEMSE 194.75194.75 201.50201.50

MAPEMAPE 5.70%5.70% 6.85%6.85%



Verify the values for MSE and MAPEVerify the values for MSE and MAPE

as shown for the previous as shown for the previous

example and submit with example and submit with

assignment 2. Use excel.assignment 2. Use excel.


Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment

When a trend is present, exponential When a trend is present, exponential smoothing must be mosmoothing must be modifieddified

Forecast Forecast including including ((FITFITtt)) = = trendtrend

exponentiallyexponentially exponentiallyexponentiallysmoothed smoothed ((FFtt)) + + ((TTtt)) smoothedsmoothedforecastforecast trendtrend


Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment

FFtt = = ((AAtt - 1 - 1) + (1 - ) + (1 - )()(FFtt - 1 - 1 + + TTtt - 1 - 1))

TTtt = = ((FFtt - - FFtt - 1 - 1) + (1 - ) + (1 - ))TTtt - 1 - 1

Step 1: Compute FStep 1: Compute Ftt

Step 2: Compute TStep 2: Compute Ttt

Step 3: Calculate the forecast FITStep 3: Calculate the forecast FITtt == F Ftt + + TTtt


Exponential Smoothing with Exponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example

ForecastForecastActualActual SmoothedSmoothed SmoothedSmoothed IncludingIncluding

MonthMonth((tt)) Demand Demand ((AAtt)) Forecast, FForecast, Ftt Trend, TTrend, Ttt Trend, FITTrend, FITtt

11 1212 1111 22 13.0013.0022 171733 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1





11 1212 1111 22 13.0013.0022 171733 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

F2 = A1 + (1 - )(F1 + T1)

F2 = (.2)(12) + (1 - .2)(11 + 2)

= 2.4 + 10.4 = 12.8 units

Step 1: Forecast for Month 2





11 1212 1111 22 13.0013.0022 1717 12.8012.8033 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

T2 = (F2 - F1) + (1 - )T1

T2 = (.4)(12.8 - 11) + (1 - .4)(2)

= .72 + 1.2 = 1.92 units

Step 2: Trend for Month 2





11 1212 1111 22 13.0013.0022 1717 12.8012.80 1.921.9233 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

FIT2 = F2 + T1

FIT2 = 12.8 + 1.92

= 14.72 units

Step 3: Calculate FIT for Month 2





11 1212 1111 22 13.0013.0022 1717 12.8012.80 1.921.92 14.7214.7233 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

15.1815.18 2.102.10 17.2817.2817.8217.82 2.322.32 20.1420.1419.9119.91 2.232.23 22.1422.1422.5122.51 2.382.38 24.8924.8924.1124.11 2.072.07 26.1826.1827.1427.14 2.452.45 29.5929.5929.2829.28 2.322.32 31.6031.6032.4832.48 2.682.68 35.1635.16




| | | | | | | | |

11 22 33 44 55 66 77 88 99

Time (month)Time (month)

Pro

du

ct d

eman

dP

rod

uct

dem

and

35 35 –

30 30 –

25 25 –

20 20 –

15 15 –

10 10 –

5 5 –

0 0 –

Actual demand Actual demand ((AAtt))

Forecast including trend Forecast including trend ((FITFITtt))


Trend ProjectionsTrend Projections

Fitting a trend line to historical data points Fitting a trend line to historical data points to project into the medium-to-long-rangeto project into the medium-to-long-range

Linear trends can be found using the least Linear trends can be found using the least squares techniquesquares technique

y y = = a a + + bxbx^̂

where ywhere y= computed value of the = computed value of the variable to be predicted (dependent variable to be predicted (dependent variable)variable)aa= y-axis intercept= y-axis interceptbb= slope of the regression line= slope of the regression linexx= the independent variable= the independent variable

^̂


Least Squares MethodLeast Squares Method

Time periodTime period

Va

lue

s o

f D

ep

end

en

t V

ari

able


DeviationDeviation11







Actual observation Actual observation (y value)(y value)

Trend line, y = a + bxTrend line, y = a + bx^̂



Time periodTime period

Va

lue

s o

f D

ep

end

en

t V

ari

able









Actual observation Actual observation (y value)(y value)

Trend line, y = a + bxTrend line, y = a + bx^̂

Least squares method minimizes the sum of the

squared errors (deviations)



Equations to calculate the regression variablesEquations to calculate the regression variables

b =b =xy - nxyxy - nxy

xx22 - nx - nx22

y y = = a a + + bxbx^̂

a = y - bxa = y - bx


Least Squares ExampleLeast Squares Example

b b = = = 10.54= = = 10.54∑∑xy - nxyxy - nxy

∑∑xx22 - nx - nx22

3,063 - (7)(4)(98.86)3,063 - (7)(4)(98.86)

140 - (7)(4140 - (7)(422))

aa = = yy - - bxbx = 98.86 - 10.54(4) = 56.70 = 98.86 - 10.54(4) = 56.70

TimeTime Electrical Power Electrical Power YearYear Period (x)Period (x) DemandDemand xx22 xyxy

19991999 11 7474 11 747420002000 22 7979 44 15815820012001 33 8080 99 24024020022002 44 9090 1616 36036020032003 55 105105 2525 52552520042004 66 142142 3636 85285220052005 77 122122 4949 854854

∑∑xx = 28 = 28 ∑∑yy = 692 = 692 ∑∑xx22 = 140 = 140 ∑∑xyxy = 3,063 = 3,063xx = 4 = 4 yy = 98.86 = 98.86



b b = = = 10.54= = = 10.54xy - nxyxy - nxy

xx22 - nx - nx22

3,063 - (7)(4)(98.86)3,063 - (7)(4)(98.86)

140 - (7)(4140 - (7)(422))

aa = = yy - - bxbx = 98.86 - 10.54(4) = 56.70 = 98.86 - 10.54(4) = 56.70

TimeTime Electrical Power Electrical Power YearYear Period (x)Period (x) DemandDemand xx22 xyxy

19991999 11 7474 11 747420002000 22 7979 44 15815820012001 33 8080 99 24024020022002 44 9090 1616 36036020032003 55 105105 2525 52552520042004 66 142142 3636 85285220052005 77 122122 4949 854854

xx = 28 = 28 yy = 692 = 692 xx22 = 140 = 140 xyxy = 3,063 = 3,063xx = 4 = 4 yy = 98.86 = 98.86

The trend line is

y = 56.70 + 10.54x^



| | | | | | | | |19991999 20002000 20012001 20022002 20032003 20042004 20052005 20062006 20072007

160 160 –

150 150 –

140 140 –

130 130 –

120 120 –

110 110 –

100 100 –

90 90 –

80 80 –

70 70 –

60 60 –

50 50 –

YearYear

Po

wer

dem

and

Po

wer

dem

and

Trend line,Trend line,y y = 56.70 + 10.54x= 56.70 + 10.54x^̂


Seasonal Variations In DataSeasonal Variations In Data

The multiplicative seasonal model can The multiplicative seasonal model can modify trend data to accommodate modify trend data to accommodate seasonal variations in demandseasonal variations in demand

1.1. Find average historical demand for each season Find average historical demand for each season

2.2. Compute the average demand over all seasons Compute the average demand over all seasons

3.3. Compute a seasonal index for each season Compute a seasonal index for each season

4.4. Estimate next year’s total demandEstimate next year’s total demand

5.5. Divide this estimate of total demand by the Divide this estimate of total demand by the number of seasons, then multiply it by the number of seasons, then multiply it by the seasonal index for that seasonseasonal index for that season


Seasonal Index ExampleSeasonal Index Example

JanJan 8080 8585 105105 9090 9494

FebFeb 7070 8585 8585 8080 9494

MarMar 8080 9393 8282 8585 9494

AprApr 9090 9595 115115 100100 9494

MayMay 113113 125125 131131 123123 9494

JunJun 110110 115115 120120 115115 9494

JulJul 100100 102102 113113 105105 9494

AugAug 8888 102102 110110 100100 9494

SeptSept 8585 9090 9595 9090 9494

OctOct 7777 7878 8585 8080 9494

NovNov 7575 7272 8383 8080 9494

DecDec 8282 7878 8080 8080 9494

DemandDemand AverageAverage AverageAverage Seasonal Seasonal MonthMonth 20032003 20042004 20052005 2003-20052003-2005 MonthlyMonthly IndexIndex



JanJan 8080 8585 105105 9090 9494

FebFeb 7070 8585 8585 8080 9494

MarMar 8080 9393 8282 8585 9494

AprApr 9090 9595 115115 100100 9494

MayMay 113113 125125 131131 123123 9494

JunJun 110110 115115 120120 115115 9494

JulJul 100100 102102 113113 105105 9494

AugAug 8888 102102 110110 100100 9494

SeptSept 8585 9090 9595 9090 9494

OctOct 7777 7878 8585 8080 9494

NovNov 7575 7272 8383 8080 9494

DecDec 8282 7878 8080 8080 9494


0.9570.957

Seasonal index = average 2003-2005 monthly demand

average monthly demand

= 90/94 = .957



JanJan 8080 8585 105105 9090 9494 0.9570.957

FebFeb 7070 8585 8585 8080 9494 0.8510.851

MarMar 8080 9393 8282 8585 9494 0.9040.904

AprApr 9090 9595 115115 100100 9494 1.0641.064

MayMay 113113 125125 131131 123123 9494 1.3091.309

JunJun 110110 115115 120120 115115 9494 1.2231.223

JulJul 100100 102102 113113 105105 9494 1.1171.117

AugAug 8888 102102 110110 100100 9494 1.0641.064

SeptSept 8585 9090 9595 9090 9494 0.9570.957

OctOct 7777 7878 8585 8080 9494 0.8510.851

NovNov 7575 7272 8383 8080 9494 0.8510.851

DecDec 8282 7878 8080 8080 9494 0.8510.851




JanJan 8080 8585 105105 9090 9494 0.9570.957

FebFeb 7070 8585 8585 8080 9494 0.8510.851

MarMar 8080 9393 8282 8585 9494 0.9040.904

AprApr 9090 9595 115115 100100 9494 1.0641.064

MayMay 113113 125125 131131 123123 9494 1.3091.309

JunJun 110110 115115 120120 115115 9494 1.2231.223

JulJul 100100 102102 113113 105105 9494 1.1171.117

AugAug 8888 102102 110110 100100 9494 1.0641.064

SeptSept 8585 9090 9595 9090 9494 0.9570.957

OctOct 7777 7878 8585 8080 9494 0.8510.851

NovNov 7575 7272 8383 8080 9494 0.8510.851

DecDec 8282 7878 8080 8080 9494 0.8510.851


Expected annual demand = 1,200

Jan x .957 = 961,200

12

Feb x .851 = 851,200

12

Forecast for 2006



140 140 –

130 130 –

120 120 –

110 110 –

100 100 –

90 90 –

80 80 –

70 70 –| | | | | | | | | | | |


TimeTime

Dem

and

Dem

and

2006 Forecast2006 Forecast

2005 Demand 2005 Demand

2004 Demand2004 Demand

2003 Demand2003 Demand


San Diego HospitalSan Diego Hospital

10,200 10,200 –

10,000 10,000 –

9,800 9,800 –

9,600 9,600 –

9,400 9,400 –

9,200 9,200 –

9,000 9,000 –| | | | | | | | | | | |

JanJan FebFeb MarMar AprApr MayMay JuneJune JulyJuly AugAug SeptSept OctOct NovNov DecDec6767 6868 6969 7070 7171 7272 7373 7474 7575 7676 7777 7878

MonthMonth

Inp

atie

nt

Day

sIn

pat

ien

t D

ays

95309530

95519551

95739573

95949594

96169616

96379637

96599659

96809680

97029702

97239723

97459745

97669766


Trend DataTrend Data



1.06 1.06 –

1.04 1.04 –

1.02 1.02 –

1.00 1.00 –

0.98 0.98 –

0.96 0.96 –

0.94 0.94 –

0.92 – | | | | | | | | | | | |JanJan FebFeb MarMar AprApr MayMay JuneJune JulyJuly AugAug SeptSept OctOct NovNov DecDec6767 6868 6969 7070 7171 7272 7373 7474 7575 7676 7777 7878

MonthMonth

Ind

ex f

or

Inp

atie

nt

Day

sIn

dex

fo

r In

pat

ien

t D

ays 1.041.04

1.021.021.011.01

0.990.99

1.031.031.041.04

1.001.00

0.980.98

0.970.97

0.990.99

0.970.970.960.96


Seasonal IndicesSeasonal Indices



10,200 10,200 –

10,000 10,000 –

9,800 9,800 –

9,600 9,600 –

9,400 9,400 –

9,200 9,200 –

9,000 9,000 –| | | | | | | | | | | |

JanJan FebFeb MarMar AprApr MayMay JuneJune JulyJuly AugAug SeptSept OctOct NovNov DecDec6767 6868 6969 7070 7171 7272 7373 7474 7575 7676 7777 7878

MonthMonth

Inp

atie

nt

Day

sIn

pat

ien

t D

ays


99119911

92659265

97649764

95209520

96919691

94119411

99499949

97249724

95429542

93559355

1006810068

95729572

Combined Trend and Seasonal ForecastCombined Trend and Seasonal Forecast


Associative ForecastingAssociative Forecasting

Used when changes in one or more Used when changes in one or more independent variables can be used to predict independent variables can be used to predict

the changes in the dependent variablethe changes in the dependent variable

Most common technique is linear Most common technique is linear regression analysisregression analysis

We apply this technique just as we did We apply this technique just as we did in the time series examplein the time series example


Associative ForecastingAssociative Forecasting

Forecasting an outcome based on predictor Forecasting an outcome based on predictor variables using the least squares techniquevariables using the least squares technique

y y = = a a + + bxbx^̂

where ywhere y= computed value of the = computed value of the variable to be predicted (dependent variable to be predicted (dependent variable)variable)aa= y-axis intercept= y-axis interceptbb= slope of the regression line= slope of the regression linexx= the independent variable though = the independent variable though to predict the value of the to predict the value of the dependent variabledependent variable

^̂


Associative Forecasting Associative Forecasting ExampleExample

SalesSales Local PayrollLocal Payroll($000,000), y($000,000), y ($000,000,000), x($000,000,000), x

2.02.0 113.03.0 332.52.5 442.02.0 222.02.0 113.53.5 77

4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sal

es

Area payroll



Sales, y Payroll, x x2 xy

2.0 1 1 2.03.0 3 9 9.02.5 4 16 10.02.0 2 4 4.02.0 1 1 2.03.5 7 49 24.5

∑y = 15.0 ∑x = 18 ∑x2 = 80 ∑xy = 51.5

xx = = ∑∑xx/6 = 18/6 = 3/6 = 18/6 = 3

yy = = ∑∑yy/6 = 15/6 = 2.5/6 = 15/6 = 2.5

bb = = = .25 = = = .25∑∑xy - nxyxy - nxy

∑∑xx22 - nx - nx22

51.5 - (6)(3)(2.5)51.5 - (6)(3)(2.5)

80 - (6)(380 - (6)(322))

aa = = yy - - bbx = 2.5 - (.25)(3) = 1.75x = 2.5 - (.25)(3) = 1.75



4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sal

es

Area payroll

y y = 1.75 + .25= 1.75 + .25xx^̂ Sales Sales = 1.75 + .25(= 1.75 + .25(payrollpayroll))

If payroll next year If payroll next year is estimated to be is estimated to be $600$600 million, then: million, then:

Sales Sales = 1.75 + .25(6)= 1.75 + .25(6)SalesSales = $325,000 = $325,000

3.25


Standard Error of the Standard Error of the EstimateEstimate

A forecast is just a point estimate of a A forecast is just a point estimate of a future valuefuture value

This point is This point is actually the actually the mean of a mean of a probability probability distributiondistribution


4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sal

es

Area payroll

3.25



wherewhere yy == y-value of each data y-value of each data pointpoint

yycc == computed value of the computed value of the dependent variable, from the dependent variable, from the regression equationregression equation

nn == number of data pointsnumber of data points

SSy,xy,x = =∑∑((y - yy - ycc))22

n n - 2- 2



Computationally, this equation is Computationally, this equation is considerably easier to useconsiderably easier to use

We use the standard error to set up We use the standard error to set up prediction intervals around the prediction intervals around the

point estimatepoint estimate

SSy,xy,x = =∑∑yy22 - a - a∑∑y - by - b∑∑xyxy

n n - 2- 2



4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sal

es

Area payroll

3.25

SSy,xy,x = = = =∑∑yy22 - a - a∑∑y - by - b∑∑xyxyn n - 2- 2

39.5 - 1.75(15) - .25(51.5)39.5 - 1.75(15) - .25(51.5)6 - 26 - 2

SSy,xy,x = = .306.306

The standard error The standard error of the estimate is of the estimate is $30,600$30,600 in sales in sales


How strong is the linear How strong is the linear relationship between the relationship between the variables?variables?

Correlation does not necessarily Correlation does not necessarily imply causality!imply causality!

Coefficient of correlation, r, Coefficient of correlation, r, measures degree of associationmeasures degree of association Values range from Values range from -1-1 to to +1+1

CorrelationCorrelation


Correlation CoefficientCorrelation Coefficient

r = r = nnxyxy - - xxy y

[[nnxx22 - ( - (xx))22][][nnyy22 - ( - (yy))22]]


Correlation CoefficientCorrelation Coefficient

r = r = nn∑∑xyxy - - ∑∑xx∑∑y y

[[nn∑∑xx22 - ( - (∑∑xx))22][][nn∑∑yy22 - ( - (∑∑yy))22]]

y

x(a) Perfect positive correlation: r = +1

y

x(b) Positive correlation: 0 < r < 1

y

x(c) No correlation: r = 0

y

x(d) Perfect negative correlation: r = -1


Coefficient of Determination, rCoefficient of Determination, r22, , measures the percent of change in measures the percent of change in y predicted by the change in xy predicted by the change in x Values range from Values range from 00 to to 11

Easy to interpretEasy to interpret

CorrelationCorrelation

For the Nodel Construction example:For the Nodel Construction example:

r r = .901= .901

rr22 = .81 = .81


Multiple Regression Multiple Regression AnalysisAnalysis

If more than one independent variable is to be If more than one independent variable is to be used in the model, linear regression can be used in the model, linear regression can be

extended to multiple regression to extended to multiple regression to accommodate several independent variablesaccommodate several independent variables

y y = = a a + + bb11xx11 + b + b22xx22 … …^̂

Computationally, this is quite Computationally, this is quite complex and generally done on the complex and generally done on the

computercomputer


Multiple Regression Multiple Regression AnalysisAnalysis

y y = 1.80 + .30= 1.80 + .30xx11 - 5.0 - 5.0xx22^̂

In the Nodel example, including interest rates in In the Nodel example, including interest rates in the model gives the new equation:the model gives the new equation:

An improved correlation coefficient of r An improved correlation coefficient of r = .96= .96 means this model does a better job of predicting means this model does a better job of predicting the change in construction salesthe change in construction sales

Sales Sales = 1.80 + .30(6) - 5.0(.12) = 3.00= 1.80 + .30(6) - 5.0(.12) = 3.00Sales Sales = $300,000= $300,000


Measures how well the forecast is Measures how well the forecast is predicting actual valuespredicting actual values

Ratio of running sum of forecast errors Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD)(RSFE) to mean absolute deviation (MAD) Good tracking signal has low valuesGood tracking signal has low values

If forecasts are continually high or low, the If forecasts are continually high or low, the forecast has a forecast has a bias errorbias error

Monitoring and Controlling Monitoring and Controlling ForecastsForecasts

Tracking SignalTracking Signal


Monitoring and Controlling Monitoring and Controlling ForecastsForecasts

Tracking Tracking signalsignal

RSFERSFEMADMAD==

Tracking Tracking signalsignal ==

∑∑(actual demand in (actual demand in period i - period i -

forecast demand forecast demand in period i)in period i)

∑∑|actual - forecast|/n|actual - forecast|/n))


Tracking SignalTracking Signal

Tracking signalTracking signal

++

00 MADs MADs

––

Upper control limitUpper control limit

Lower control limitLower control limit

TimeTime

Signal exceeding limitSignal exceeding limit

Acceptable Acceptable rangerange


Tracking Signal ExampleTracking Signal ExampleCumulativeCumulative

AbsoluteAbsolute AbsoluteAbsoluteActualActual ForecastForecast ForecastForecast ForecastForecast

QtrQtr DemandDemand DemandDemand ErrorError RSFERSFE ErrorError ErrorError MADMAD

11 9090 100100 -10-10 -10-10 1010 1010 10.010.022 9595 100100 -5-5 -15-15 55 1515 7.57.533 115115 100100 +15+15 00 1515 3030 10.010.044 100100 110110 -10-10 -10-10 1010 4040 10.010.055 125125 110110 +15+15 +5+5 1515 5555 11.011.066 140140 110110 +30+30 +35+35 3030 8585 14.214.2


CumulativeCumulativeAbsoluteAbsolute AbsoluteAbsolute

ActualActual ForecastForecast ForecastForecast ForecastForecastQtrQtr DemandDemand DemandDemand ErrorError RSFERSFE ErrorError ErrorError MADMAD

11 9090 100100 -10-10 -10-10 1010 1010 10.010.022 9595 100100 -5-5 -15-15 55 1515 7.57.533 115115 100100 +15+15 00 1515 3030 10.010.044 100100 110110 -10-10 -10-10 1010 4040 10.010.055 125125 110110 +15+15 +5+5 1515 5555 11.011.066 140140 110110 +30+30 +35+35 3030 8585 14.214.2

Tracking Signal ExampleTracking Signal ExampleTracking

Signal(RSFE/MAD)

-10/10 = -1-15/7.5 = -2

0/10 = 0-10/10 = -1

+5/11 = +0.5+35/14.2 = +2.5

The variation of the tracking signal The variation of the tracking signal between between -2.0-2.0 and and +2.5+2.5 is within acceptable is within acceptable limitslimits


Adaptive ForecastingAdaptive Forecasting

It’s possible to use the computer to It’s possible to use the computer to continually monitor forecast error and continually monitor forecast error and adjust the values of the adjust the values of the and and coefficients used in exponential coefficients used in exponential smoothing to continually minimize smoothing to continually minimize forecast errorforecast error

This technique is called adaptive This technique is called adaptive smoothingsmoothing


Focus ForecastingFocus Forecasting

Developed at American Hardware Supply, Developed at American Hardware Supply, focus forecasting is based on two principles:focus forecasting is based on two principles:

1.1. Sophisticated forecasting models are not Sophisticated forecasting models are not always better than simple modelsalways better than simple models

2.2. There is no single techniques that should There is no single techniques that should be used for all products or servicesbe used for all products or services

This approach uses historical data to test This approach uses historical data to test multiple forecasting models for individual itemsmultiple forecasting models for individual items

The forecasting model with the lowest error is The forecasting model with the lowest error is then used to forecast the next demandthen used to forecast the next demand


Forecasting in the Service Forecasting in the Service SectorSector

Presents unusual challengesPresents unusual challenges Special need for short term recordsSpecial need for short term records

Needs differ greatly as function of Needs differ greatly as function of industry and productindustry and product

Holidays and other calendar eventsHolidays and other calendar events

Unusual eventsUnusual events


Fast Food Restaurant Fast Food Restaurant ForecastForecast

20% 20% –

15% 15% –

10% 10% –

5% 5% –

11-1211-12 1-21-2 3-43-4 5-65-6 7-87-8 9-109-1012-112-1 2-32-3 4-54-5 6-76-7 8-98-9 10-1110-11

(Lunchtime)(Lunchtime) (Dinnertime)(Dinnertime)

Hour of dayHour of day

Per

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Per

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Documents

managing inventory Forecasting