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OMSAN LOJİSTİK
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Forecasting: Principles and Practices
Inventory Planning and Management
Latin America Logistics Center
Logistics Management Series -
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Contents
• Introduction
• Reasons for Forecasting Errors
• Forecasting Key Performance Indicators
• Principles, Methods and Best Practices of Forecasting
• Forecasting Systems Review
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$2,800
$14,000
$28,000
$40,000
$58,000
$70,000
$88,000
$108,000
0
50
100
150
200
250
300
10% 20% 30% 40% 50% 60% 70% 80%
Average Percent Error
Saf
ety
Sto
ck
$-
$20,000.00
$40,000.00
$60,000.00
$80,000.00
$100,000.00
$120,000.00
Inve
nto
ry C
arry
ing
Co
st
Safety Stock
Inventory Carrying Cost
Forecasting & Safety Stock
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Introduction to Forecasting
• Intelligent Forecasting
• Types of Forecasting
• Forecasting Methods
• Demand Forecast during Lead Time
• Typical Demand Patterns
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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
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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
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Demand Projections
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• Statistical AnalysisRegression, Time Series, etc.
• Market Research
• Conceptual Models
• Experts JudgementComplementarios … no mutualmente exclusivos
Forecasting Techniques
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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
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Forecasting Techniques
• Statistical Methods– Trend– Time Series– Smoothing Techniques
• Quantitative Methods– Macro & Microeconomic Forecast– Market Strategy– Competitors– Market Research
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Macroeconomic Forecasting
• Macroeconomic Research considers variables such as inflation, unemployment, GNP Growth, Interest Rates
• There are so many complex Interdependencies in the Economy
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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) (+)
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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
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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)
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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
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QuantitativeQualitative
Extrapolate
Model
Aggregate
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgement
Forecast Techniques
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QuantitativeQualitative
Extrapolate
Model
Aggregate
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgement
Forecast Techniques
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Disaggregate Top – Down
Industry
Category
Product
Item
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“Tyranny of the 100”
Gaining Market Share takes from the Specific Competitor’s market
share (whom most likely will react)
Which Competitors? Why? How?
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QuantitativeQualitative
Extrapolate
Model
Aggregate
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgement
Forecast Techniques
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Aggregation Bottom-up
Customer1
Item
Customer2
Customer3
Item Item Item
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QuantitativeQualitative
Extrapolate
Model
Aggregate
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgement
Forecast Techniques
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0 1 2 3 4 5 6 7 8 9 10
Years
80
70
60
50
40
30
20
10
Pen
etra
tion
%Time Series Analysis
Actual Projected
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0 1 2 3 4 5 6 7 8 9 10
Years
80
70
60
50
40
30
20
10
Pen
etra
tion
%
Similar Product
New Product
Time Series AnalysisSimilar Products
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QuantitativeQualitative
Extrapolate
Model
Aggregate
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgement
Forecast Techniques
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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
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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
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Problems with Regression
• False Correlation– No real cause effect
• Nonsense Coefficients– Unexplainable variance
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Sequential Factors
Total TVHouseholds
BaseballFanatics
Wired ForCable
Cable Homes
Cable/Baseball
Population
PremiumServiceBuyers
BaseballPay Per View
Market
* A.K.A. “Factor Decomposition”, “Factor Analysis”
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For Example …
How much Dog food is currently sold in U.S.?
Show your answer in $$$$
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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?
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# 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?
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Demand ProjectionsIncorporating Market Factors
MARKETPOTENTIAL
SALES
MARKETSHARE
MARKETPENETRATION
MARKETSIZE
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Market ProjectionsTime Dimension
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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
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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
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Typical Demand Patterns
• Linear Demand with Random Fluctuations
• Trend Demand with Random Fluctuations
• Seasonal Demand with Random Fluctuations
• Trend Seasonal Demand with Random Fluctuations
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Typical Demand Patterns
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Linear Demand with Random Fluctuations
70
75
80
85
90
95
100
105
110
115
120
Jan-
95
Mar
-95
May
-95
Jul-9
5
Sep
-95
Nov
-95
Jan-
96
Mar
-96
May
-96
Jul-9
6
Sep
-96
Nov
-96
Jan-
97
Mar
-97
May
-97
Jul-9
7
Sep
-97
Nov
-97
Series1
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Trend Demand with Random Fluctuations
100
105
110
115
120
125
130
135
140
Jan-
95
Mar
-95
May
-95
Jul-9
5
Sep-
95
Nov
-95
Jan-
96
Mar
-96
May
-96
Jul-9
6
Sep-
96
Nov
-96
Jan-
97
Mar
-97
May
-97
Jul-9
7
Sep-
97
Nov
-97
Series1
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Seasonal Demand
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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
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Seasonal Demand with Random Fluctuations
75
95
115
135
155
175
195
215
Jan-
95
Mar
-95
May
-95
Jul-9
5
Sep-
95
Nov
-95
Jan-
96
Mar
-96
May
-96
Jul-9
6
Sep-
96
Nov
-96
Jan-
97
Mar
-97
May
-97
Jul-9
7
Sep-
97
Nov
-97
Series1
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Trend Seasonal Demand with Random Fluctuations
0
50
100
150
200
250
300
350
400
Jan-
95
Mar
-95
May
-95
Jul-9
5
Sep
-95
Nov
-95
Jan-
96
Mar
-96
May
-96
Jul-9
6
Sep
-96
Nov
-96
Jan-
97
Mar
-97
May
-97
Jul-9
7
Sep
-97
Nov
-97
Series1
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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
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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
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El Efecto “Dominó”
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BullWhip Effect
Real DemandWholesalerForecast of
DemandWholesalerOrders toDistributor
DistributorsOrders toBarilla DC
Barilla DCOrders to
Plant
WholesalerDistributorBarilla
DCBarilla Plant
End consumer
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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
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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
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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
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• 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
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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%
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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
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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
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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
?
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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)]
?
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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
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Models of Best Fit Forcast
300
400
500
600
700
800
900
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
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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
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Key Points for Success
• Be Practical
• Structured Methodology
• Multiple Methods
• Interactive Convergence
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Demand ProjectionsGeneral Principles
• Errors will occur
• Aggregate Series are the most stables
• There is a tendency to overcorrect (specially in the short term)