Forecast Pro Automatic Forecasting Doesn't Work.pdf

7/27/2019 Forecast Pro Automatic Forecasting Doesn't Work.pdf

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What Now? How to Proceed Wh

Automatic Forecasting Doesnt W

Presented by

Eric Stellwagen

Vice President & Cofounder

[email protected]

Business Forecast System

68 Leonard Street

Belmont, MA 02478 USA

(617) 484-5050

www.forecastpro.com


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2013 Business Forecast Systems, Inc. www.forecastpro

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



Evolution of Forecasting Process

Custom

Approa

Automatic Time

Series Approaches

Judgment &

Spreadsheets

Phase 1 Phase 2 Phase



The Role of Judgment



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.




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




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.



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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Pros:

Simple to understand and explain

Widely accepted and used

Often quite accurate

Adaptive

Easy to apply


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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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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


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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Temperature is an important driver.



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Time series models cannot capture adequate response to temperat



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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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Forecast Pro Automatic Forecasting Doesn't Work.pdf

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