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OMSAN LOJİSTİK

Forecasting: Principles and Practices

Inventory Planning and Management

Latin America Logistics Center

Logistics Management Series -

Contents

• Introduction

• Reasons for Forecasting Errors

• Forecasting Key Performance Indicators

• Principles, Methods and Best Practices of Forecasting

• Forecasting Systems Review

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Safety Stock

Inventory Carrying Cost

Forecasting & Safety Stock

Introduction to Forecasting

• Intelligent Forecasting

• Types of Forecasting

• Forecasting Methods

• Demand Forecast during Lead Time

• Typical Demand Patterns

Intelligent Forecasting

• The Forecast will have Error

• Is Better 50% Error than 100%.

• Forecast Accuracy Should Permanently Improve

• We shall Forecast Better than Competitors

• Closer Data suits better for Forecast than Data that is too old

Types of Forecasts

• Long Term (3-5 years horizon)– Increasing Plant Capacity

• Medium Term (1-2 years horizon)– Long Lead-Times of Materials– Seasonal Products

• Short Term (3-6 months)– Economic Order Quantities– Production Planning.

• Near Future (Days or Weeks)– Assembly– Finish Goods Inventory to the Market

Demand Projections

• Statistical AnalysisRegression, Time Series, etc.

• Market Research

• Conceptual Models

• Experts JudgementComplementarios … no mutualmente exclusivos

Forecasting Techniques

QuantitativeQualitative

NumbersJudgement

• Used when there is not much data available– New Products– New Technology

• Intuition, experience

• e.g., Internet Sales

Qualitative MethodsQualitative Methods

• Used when Historical Data Exists– Current Products– Proved Technology

• Mathematical Techniques / Statistics.

• e.g., Color Television

Quantitative MethodsQuantitative Methods

Forecasting Techniques

• Statistical Methods– Trend– Time Series– Smoothing Techniques

• Quantitative Methods– Macro & Microeconomic Forecast– Market Strategy– Competitors– Market Research

Macroeconomic Forecasting

• Macroeconomic Research considers variables such as inflation, unemployment, GNP Growth, Interest Rates

• There are so many complex Interdependencies in the Economy

Economic Indicators of Business

• Average worked hours per week (+) • Average weekly claims of Unemployment Benefits (-)• New orders of equipment and materials (+) • Performance of Suppliers in Lead Times (-) • New contracts and durable goods orders (+) • Construction Index (+)• Changes to prices of raw materiales (e.g., construction

materiales) (+)• Stock Price Index (e.j., S&P's 500) (+)

Microeconomic Environment Forecast

• Microeconomic forecast considers the effects of a industry in particular to new product, substitutes, markets, and other companies

• Microeconomic forecast is less complex than macroeconomic forecast, and therefore more accurate

Market Research Techniques

• The Survey is and instrument through which

managers, end consumers, and government

are approached to research their future

plans

• Two common sources of survey research in

USA are the Department of Commerce and

the Conference Board (a private industrial

organization)

Qualitative Forecast

• Qualitative Forecasting is a research technique in which people’s experience is considered to understand economic trends

• Expert views are acquired through Focus Groups• Focus groups information may be biased by the

informants psychographics• The Delphi Method is a Qualitative Technic through

which the information of a group of experts is used to forecast in situations with very low information available


Extrapolate

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NumbersJudgement

Forecast Techniques


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NumbersJudgement

Forecast Techniques

Disaggregate Top – Down

Industry

Category

Product

Item

“Tyranny of the 100”

Gaining Market Share takes from the Specific Competitor’s market

share (whom most likely will react)

Which Competitors? Why? How?


Extrapolate

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NumbersJudgement

Forecast Techniques

Aggregation Bottom-up

Customer1

Item

Customer2

Customer3

Item Item Item


Extrapolate

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Forecast Techniques

0 1 2 3 4 5 6 7 8 9 10

Years

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%Time Series Analysis

Actual Projected

0 1 2 3 4 5 6 7 8 9 10

Years

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Similar Product

New Product

Time Series AnalysisSimilar Products


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Forecast Techniques

ILLUSTRATIVE L TRANSLATION PROSPECTS PERCENT

WEIGHT PROFILE BUYERS

Definitely 90% 10% 9%

Probably 40% 20% 8%

Might or might not 10% 20% 2%

Probably not 0 15% 0

Definitely not 0 35% 0

19%

Translation of a Intention Model

Source: Thomas, p.206

YY XXii ii aa bb

• Denotes the linear relation between dependent and independent variables– Example: Nappies & # Babies (not time)

Dependent Variable Dependent Variable (reaction)(reaction)

Independent Independent Variable (causal)Variable (causal)

SlopeSlopeY-interceptY-intercept

^̂

Linear Regression Models

Problems with Regression

• False Correlation– No real cause effect

• Nonsense Coefficients– Unexplainable variance

Sequential Factors

Total TVHouseholds

BaseballFanatics

Wired ForCable

Cable Homes

Cable/Baseball

Population

PremiumServiceBuyers

BaseballPay Per View

Market

* A.K.A. “Factor Decomposition”, “Factor Analysis”

For Example …

How much Dog food is currently sold in U.S.?

Show your answer in $$$$

Sequential Factors How Much Dog Food?

• How many people?• How many houses?• Houses with dogs?• Dogs per house?• Proportion big to small dogs?• Daily use? (ounces)• Ounces per can?• Unit price per can?

# Big

# Little

Little Eats

# Dogs Homes

% Dogs

Homesw/ dogs

Dogs /Home

Big/little split

Big Eats

Popul-ation

People/ House

DogFood

How Much Dog Food?

Demand ProjectionsIncorporating Market Factors

MARKETPOTENTIAL

SALES

MARKETSHARE

MARKETPENETRATION

MARKETSIZE

Market ProjectionsTime Dimension

Trend Analysis

• A Trend is a long-term established pattern of change

• Trend Analysis is based on the recognition that past and present

patterns repeat in the feature

• Trend Analysis uses data from time series, which are observations of

a variable during time

• Time series data are subject to shocks (unanticipated deviations) and

cyclical fluctuations caused by external factors beyond observed

pattern

• Seasonality and business cycles are examples of cyclical fluctuations

Forecast and Replenishment Time

Raw Materials Supply

Parts Manufacturing

Product Assembly

Delivery Industry

Allowable Lead Time

(Doesn’t need Product Forecast)

Durable Goods, Airplanes, Ships, Trains

Allowable Lead Time

(Needs Raw Materials Forecast)

Special Orders, Workshops, Fine Chemicals

Allowable Lead Time

(Raw Materials and Parts Forecast)

Machinery, Electronics, Special Assemblies

Allowable Lead Time

(Raw Materials, Parts and Final

Product Forecast)

Automotive parts, Fast moving goods, Stock Sales

Source: Plossl, page 66

Typical Demand Patterns

• Linear Demand with Random Fluctuations

• Trend Demand with Random Fluctuations

• Seasonal Demand with Random Fluctuations

• Trend Seasonal Demand with Random Fluctuations

Typical Demand Patterns

Linear Demand with Random Fluctuations

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Trend Demand with Random Fluctuations

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Seasonal Demand

Seasonality

• Cycles generated for climate changes, rain and dry seasons, for country

• Cold weather areas vs. Construction clycles

• Effects of Christmas season on food, toys, electric appliances sales

• Companies like Coca-Cola use the weather report to adjust their forecasts

Seasonal Demand with Random Fluctuations

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Trend Seasonal Demand with Random Fluctuations

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Causes for Forecast Errors

• Bias– Confusion between Forecast and Sales Target– Lack of connection between forecast and sales targets, making

the forecast a target itself

• Ignorance– Unawareness of the available information, including commercial

information and market intelligence and plans of customers– “Do not guess what somebody else already know”

• Poor Data Integration– Data files not updated (poor maintenance), many names for the

same item, K Mart = Kmart = KMRT– Data registry with errors

Causes for Forecast Errors

• Bullwhip Effect– Forecast on basis of previous forecasts increase the error

thourghout the Supply Chain

• Long Lead Times– The longer the Lead Time the greater the forecast error

• Promotions– Price Fluctuations and Promotions cause unpredictable changes

in demand

• Shortage Gaming– Customers order more than they need and cancel when they

have inventory surpluses

El Efecto “Dominó”

BullWhip Effect

Real DemandWholesalerForecast of

DemandWholesalerOrders toDistributor

DistributorsOrders toBarilla DC

Barilla DCOrders to

Plant

WholesalerDistributorBarilla

DCBarilla Plant

End consumer

Typical Retail Supply Chain

Component

Mfg.

PC Assembler

Distributor

Forecast & ComponentQuality Info.

Components Sales Info.

FinishedAssemblies

Retailer

Sal

es I

nfo

.

FinishedAssemblies

Quality Info.

Consumer FinishedAssemblies

Demand

Typical Retail Supply Chain

CONTROL OF VARIANCE INCONTROL OF VARIANCE INDISTRIBUTION SYSTEMSDISTRIBUTION SYSTEMS

Transportation Discounts Discounts per Volume Promotional Actitivies Minimum and Maximum Quantities Product Proliferation Limited time to fill large orders Poor Customer Service Reward Systems Based on Sales Poor Communication

Causes and Excuses for Deficient Forecasts

• Lack of Visibility of Unsatisfied Demand• Extra Work

– We are forecasting when we really do not need it

• Negativism– Obsolete Forecasts– “The next forecast will be better”

• Inconsistent and Intermittent Demand– 80% demand is in 20% of items, 80% of items have intermittent

demand– This needs a large amount of data to obtain a valid distribution

(e.g., normal, lognormal etc).

• Blurry– The more detailed the forecast, the bigger the error

• The forecast of a large group of items is more accurate than the forecast of a single item

ExampleForecast life expectancy of women against forecast Anna’s life expectancy

Aggregate Forecasts are more Accurate than Individual

Forecasts

FamilyActual

DemandActual Mix% Forecast

Forecast Mix% Mix Error

Algebraic Deviation

Absolute Deviation MAD%

Housewares 21,230$ 11% 24,100$ 10% -1% 2,870$ 2,870$ 14%Sporting Goods 13,150$ 7% 21,690$ 9% 2% 8,540$ 8,540$ 65%Paint Products 19,300$ 10% 19,280$ 8% -2% (20)$ 20$ 0%

Lumber 7,720$ 4% 4,820$ 2% -2% (2,900)$ 2,900$ 38%Fasteners 17,370$ 9% 26,510$ 11% 2% 9,140$ 9,140$ 53%

Lawn & Garden 34,740$ 18% 50,610$ 21% 3% 15,870$ 15,870$ 46%Tools 28,950$ 15% 31,330$ 13% -2% 2,380$ 2,380$ 8%

Electrical 17,370$ 9% 19,280$ 8% -1% 1,910$ 1,910$ 11%Plumbing 15,440$ 8% 24,100$ 10% 2% 8,660$ 8,660$ 56%Heating 17,370$ 9% 19,280$ 8% -1% 1,910$ 1,910$ 11%

Total 192,640$ 100% 241,000$ 100% 0% 48,360$ 54,200$ 25%Tracking Signal: 8.92Average MAD: 30%

Causes and Excuses for Deficient Forecasts

• The Guilty!– It is not a team work – Nobody does the work appropriately– We need our own forecast, theirs is wrong!

• Too Much Democracy– All Items are equal

• Multicultural Forecast– Sales people should be Optimist– Production & Logistics should be Realists– Senion Managers should be Judicious with the Financial Plan

• Lost of Memory– Nobody pays Attention– All Trust it is OK.– Lets Forget Precision

Simple Models of Forecasting

• Periods Previous to Demand

• Average of n previous periods to demand

• Weighted Average of n previous periods to demand

• Last Year’s Demand x (1 +/- Trend)

• Best Fit Models

Averages

• Continuous...F = [D(1) + D(2) + D(3) + … + D(n) + D(n+1) - D(1)]/n

• Weighted AverageF = D(1) + D(2) + D(3) + … + aD(n-1) + bD(n) + cD(n+1) - D(1)]/n

?

Exponential Smoothing• First Order

F(new) = F(old) + actual- F(old)]

• Second OrderF(new) = 2*A(new) - B(new)A(new) = F(old) + a[actual-F(old)]B(new) = B(old) + a[A(new)-B(old)]

?

Forecasting Rules of Thumb

• Sales growth should be....– F = Q4*b

• Same sales than the previous year….– F = Q1

• Same sales than 3 months ago…– F = Q4

• Average of the last year firs half– F = (Q1+Q2)/2

• Average of the second half...– F = (Q3+Q4)/2 (most recent observation)

• Forecast of the Salesman– F = S

Models of Best Fit Forcast

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1000

1 2 3 4 5 6 7 8 9 10 11 12 13

Period

Un

its

Old Forecast

Actual Demand

Exponential Smoothing

Running Average

Weighted Average

Average Demand

Demand Trends and Forecasting Methods

Demand Trend

Moving Averages First Order Exponential Smoothing

Second Order Exponential Smooting

Base Indexes

Flat with some variance

Good Few changes for many

periods – unstable with too few periods

Large data bases Rigid

Good Insensitive with low

alphas – unstable with high alphas

Few data storage Flexible

Poor Interprets

variance as trend

Intermittent (no trend, large variance)

Good Better if periods of zero

demand are ignored

Poor unless zero demand is ignored

Not good

Consistent trend increasing or decreasing

Not good Poor unless smooth trend

Needs a very high alpha

Good Specially designed to trend

Seasonal (Annual cycles with some variance)

Good, designed to this demand

Source: Plossl, page 89

Key Points for Success

• Be Practical

• Structured Methodology

• Multiple Methods

• Interactive Convergence

Demand ProjectionsGeneral Principles

• Errors will occur

• Aggregate Series are the most stables

• There is a tendency to overcorrect (specially in the short term)