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What Now? How to Proceed Wh
Automatic Forecasting Doesnt W
Presented by
Eric Stellwagen
Vice President & Cofounder
Business Forecast System
68 Leonard Street
Belmont, MA 02478 USA
(617) 484-5050
www.forecastpro.com
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On-demand Webinars and Handout
Todays webinar along with a pdf version of the slide
be posted on our Website next week.
Previously presented Webinars are available for view
www.forecastpro.com.
Participants will receive an email confirming availabiafter the Webinar and slide set is posted.
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Eric Stellwagen
Vice President & Cofounder of
Business Forecast Systems, Inc.
Coauthor of Forecast Pro product l
Over 27 years in forecasting.
Currently serving on the board of dthe International Institute of Forecon the practitioner advisory board Foresight: The International JournaForecasting.
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What Well Cover
Overview
The Role of Judgment
Exponential Smoothing
New Product Forecasting
Automatic Forecasting
Top-down Approaches
Multivariate Approaches
Summary
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Common Forecasting Methods
Simple Time Series Methods:
Moving Averages
Same as Last Year
Percentage Growth
Multivariate Methods:
Event-index ModelDynamic Regressio
Statistical Time Series Methods:Exponential Smoothing
Box-Jenkins (ARIMA)
Crostons Intermittent Demand
Model
Judgmental Approaches
New Product Methods:
Forecast by Analog
Assumption-based
Diffusion Models (e
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Evolution of Forecasting Process
Custom
Approa
Automatic Time
Series Approaches
Judgment &
Spreadsheets
Phase 1 Phase 2 Phase
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The Role of Judgment
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Judgmental Forecasting
Pros:Does not require statistical expertise.
Allows forecaster to incorporate domain know
This knowledge can come from many sources i
experience with similar products, feedback fro
staff, customer surveys, focus groups, etc.
Does not require historical data.
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Judgmental Forecasting
Cons:Is subjective.
Can be biased by company politics, sales goals
Is difficult to monitor performance and fine tufuture forecasts.
Is not automaticcan be very time consuming
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Judgmental Forecasting
Judgment often plays an important role in forecasting, particularly
products, short product-life-cycle products, rapidly changing envir
and instances where the forecasters domain knowledge is not ca
statistical forecasting model.
A strong recommendation is to add judgment in the form of an ove
statistically generated base-line forecast. This practice provides th
track the effectiveness of the judgmental override and introduces
accountability into the process.
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Automatic Time Series Approache
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3 Months of Data
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12 Months of Data
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24 Months of Data
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36 Months of Data
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6 Years of Data
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Automatic Time Series Approache
Pros:
Simple to understand and explain
Widely accepted and used
Often quite accurate
Adaptive
Easy to apply
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Automatic Time Series Approache
Cons:
Requires adequate demand history
Assumes continuity between past and future
Does not capture response to noncalendar-base
(e.g., promotions)
Does not capture response to explanatory varia
Implementations vary and some are poor
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Rejecting Automatic Models
Judgmentally override the forecasted values.
Dictate that a different forecasting model be used
Reconfigure the input data.
When you disagree with the forecasts generated using a
automatic time series approach you should reject them
Generally there are three ways to do this:
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Why is it Wrong?
Domain knowledge
Your knowledge of the future leads you to rejecforecast.
The solution is most often to judgmentally adju
forecast.
Might be done informally or as part of a structu
process (e.g., S&OP)
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Adding Judgment
Best practices:
Retain statistical forecast and adjusted forecast for comparison
Document reason for changes
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Why is it Wrong?
Chose wrong time series model Often a case of misidentifying trends and/or se
patterns.
Dictating an appropriate exponential smoothinmodel is often a good solution.
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Exponential Smoothing
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Main Characteristics of Exponential Smoothin
A family of models.
Models three data componentslevel, trend,and seasonali
Assumes that each component is changing in time.
Assumes that there is random variation (noise).
Uses weights to reflect the relative emphasis given to the rec
the distantpast.
Estimatesfinal values of the components and uses them to c
forecasts.
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Holt-Winters Exponential Smoothing
NonseasonalAdditive
SeasonalMultiplicative
Seasonal
ConstantLevel
Linear
Trend
DampedTrend(0.95)
Exponential
Trend(1.05)
(SIMPLE)
(WINTERS)(HOLT)
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Why is it Wrong?
Inadequate data Data too short (new product)
Data too low level (not enough structure)
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New Product Forecasting
and Top-down Approaches
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There are different types ofnew products including:
Replacement Products
Product Line Extensions
New-to-Company
New-to-World
The type of new product you are trying to forecast will often di
approaches should be considered.
Types of New Products
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When forecasting replacement products or product line extens
you will often want to leverage the data that exist for thepredecessor products.
Approaches can include:
Judgment and Market Research
Item Supersession (i.e., mapping histories)
Top-down Forecasting
Replacement Products and Product-Line Exte
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Item Supersession
You create a forecast history for the new product using the
histories of predecessor products andthe new product.
For a replacement product this may be as simple as merging
products history with the new products history.
More complex mapping may be necessary depending on the
circumstances.
S i
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Old Product New Produ
Item Supersession
I S i
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Forecast History
Item Supersession
T d F i
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Group-level data are higher volume, will often exhibit more structure and
have a longer demand history than the product line extensions and
replacement products.
To generate a top-down forecast, you first forecast at the group level usin
aggregated history. Then, you forecast at the lower levels. Finally, you ap
proportionality factors to lower-level forecasts so that the forecasts sum
top-level forecast.
Cough Syrup
SKU 1 SKU 2 Etc.
Top-down Forecasting
N P d t F ti
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When forecasting new-to-companyor new-to-world products yobviously dont have internal dataexternal data may or may n
be available.
Approaches can include:
Judgment and Market Research
Forecasting by Analogy (looks-like)
Assumption-based Models
Market Share Forecasting
Diffusion Models
New Product Forecasting
Wh i it W ?
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Why is it Wrong?
Inadequate data
Data too low level (not enough structure)
Simplify the hierarchy
Forecast top-down
Aggregate the time buckets (e.g., switch from weeks to mont
Use bucket conversions
B k t C i
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It is not uncommon for a company that needs
or weekly forecast to discover that the data casupport statistical modeling at these periodicit
One solution is to forecast at an aggregated le
allocate down to the lower level. This process sometimes referred to as bucket conversion
Bucket Conversion
B k t C i
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S
Flat allocation
Proportional allocation based on historical pro
Proportional allocation based on forecasts (bu
synchronization)
Bucket Conversion
Bucket conversion can take different forms:
Why is it Wrong?
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Why is it Wrong?
Time series model is not appropriat
when demand is driven by:
Events that move around the calendar
Explanatory variables that can change abrupt
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Multivariate Methods
Common Events
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Common Events
Product promotions
Moveable holidays (e.g., Easter, Rosh Hashanah,Ramadan)
Catastrophes (e.g., earthquakes, hurricanes, 9/11
Labor strikes
Acquisitions
New legislation or regulations
Forecasting Event Driven Data
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Forecasting Event-Driven Data
Judgmentally adjust history to remove imp
Separate base demand from event-driven
Use a time series extension model (e.g., ev
index model, ARIMA intervention model, e
Use a multivariate model (e.g., regression)
What is an Event Index Model
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What is an Event-Index Model
An extension of exponential smoothing.
An index-based approach.
The model introduces an additional smoot
weight and updating equation.
The model requires an event schedule.
Example: Sales of Mouthwash
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Example: Sales of Mouthwash
Demand for mouthwash is not seasonal and for this brand not trended.
Price promotions by both the manufacturer and competitors introduce significant peak
The timing of promotions is similar from year to year (but not exactly the same), and th
domain knowledge the data appear to be seasonal.
The introduction of EDLP with Wal-Mart changes the response to promotions.
Why is it Wrong?
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Why is it Wrong?
Time series model is not appropriate
when demand is driven by:
Events that move around the calendar
Explanatory variables that can change abruptl
Dynamic Regression
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Pros:
sAllows for the introduction of explanatory variab
Lends insight into relationships between variable
Allows for what if scenarios.
Can exploit leading indicators.
Dynamic Regression
Dynamic Regression
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Dynamic Regression
Cons:
Is not automatic, requires considerable expertise
Will produce poor forecasts of the dependent va
there are difficulties in forecasting the explanato
variables.
Requires large amounts of data.
Independent Variables
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Internal Variables
Prices
Promotion
External Variabl
Weather
Economy
Competition
Demographics
Independent Variables
Example: Sales of Electricity
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Example: Sales of Electricity
Temperature is an important driver.
Example: Sales of Electricity
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Example: Sales of Electricity
Time series models cannot capture adequate response to temperat
Example: Sales of Electricity
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Example: Sales of Electricity
Dynamic regression models capture response to temperature and wo
Summary
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Summary
Judgment is always important and is best applied as an adjus
statistically generated base-line forecast.
Automatic time series methods work well when you have ade
and are generally superior to spreadsheet models.
When you reject an automatic time series forecast, your opti
Judgmentally overriding the forecasted values.
Dictating that a different forecasting model be used.
Reconfiguring the input data.
Forecast Training and Workshops
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Forecast Training and Workshops
S
BFS offers forecasting webinars and product train
workshops.
On-site, and remote-based (via WebEx) classes ar
available.
Learn more at www.forecastpro.com
Forecast Pro
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Examples from todays Webinar used Forecast Pro.
To learn more about Forecast Pro:
Request a live WebEx demo for your team (subm
your request as a question right now)
Visit www.forecastpro.comCall us at 617-484-5050
Our Next Webinar
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Tracking Accuracy: An Essential Step to Improve You
Forecasting Process, April 11, 2013 1:30 p.m. EDT
Eric Stellwagen, Vice President of Business Forecast
Highlights include how to apply best practices, the p
cons of different error measurements and how to spo
performance.
Visit www.forecastpro.com to sign up!
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Questions?
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Thank you for attend