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Effective Strategies for Forecasting a Product Hierarchy Strategies for Forecasting a Product...
Transcript of Effective Strategies for Forecasting a Product Hierarchy Strategies for Forecasting a Product...
Effective Strategies for Forecasting a
Product Hierarchy
Presented by Eric Stellwagen Vice President & Cofounder Business Forecast Systems, Inc. [email protected]
Business Forecast Systems, Inc. 68 Leonard Street Belmont, MA 02478 USA (617) 484-5050 www.forecastpro.com
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
On-Demand Webinars & Materials
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A recording of today’s Webinar will be posted next week
on forecastpro.com along with the slide set (in .pdf
format)
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available for viewing on-demand at forecastpro.com
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© 2018, Business Forecast Systems, Inc. www.forecastpro.com
Eric Stellwagen
Served on the board of directors of the International Institute of Forecasters for 12 years. Is currently serving on the practitioner advisory board of Foresight: The International Journal of Applied Forecasting.
President, CEO & Co-founder of Business Forecast Systems, Inc.
Co-author of Forecast Pro product line.
More than 33 years of dedicated business forecasting experience.
.
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
Sarah Darin
20 years of experience with statistical consulting, sales forecasting, regression modeling and marketing analytics. Experience across a broad range of
industries, including Consumer Packaged Goods, Telecommunications, Technology, Retail, Automotive and Finance. Undergraduate degree in Applied Mathematics from Harvard University and a Master’s of Science in Statistics from the University of Chicago, where she also served as a Lecturer.
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What We’ll Cover
Introductions
Basic Concepts
Constructing the Hierarchy
Examples
Q&A
Reconciliation Techniques
Summary
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
A Sample Hierarchy
Total Sales
Brand A
Retail
Cans
6-Pack 12-Pack
Bottles
Commercial
Brand B Brand C
Corporations typically deal with multiple levels of aggregation and
require consistent forecasts at all levels.
Item Level
Group Levels
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What Are the Forecasts Used For?
Item-level forecasts
Drive production, purchasing and inventory control
Short horizons are important
Intermediate-level forecasts
Drive marketing and revenue planning
Medium horizons are important
Upper-level forecasts
Guide “big picture” decision making
Medium-to-long horizons are important
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Should You Strive for One-Number Forecasts?
Depends on the environment.
The larger the company, the harder this is to achieve.
Communication among the different functional
groups is very important. This is a primary goal of an
S&OP process.
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A Very Simple Hierarchy
Total Cans
6-Pack 12-Pack
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Common Reconciliation Techniques
Bottom-Up
Create model-based forecasts at the lower level. Construct upper-level
forecasts by summing nested lower-level forecasts.
Top-Down
First, create a model-based forecast at the top level by forecasting the
aggregated history. Then, create model-based forecasts at the lower levels.
Finally, apply proportionality factors to lower-level forecasts so that the
forecasts sum to the top-level forecast.
Proportional Allocation
Disaggregate higher-level forecasts by applying proportionality factors.
This method would be appropriate when proportions are constant and known.
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Reconciliation Techniques Example
Total Cans
6-Pack 12-Pack
In a hierarchy with 3 or more levels, combinations of top-down
and bottom-up approaches can be applied (i.e., middle-out).
Model-Based
6-Pack 70
12-Pack 30
Total Cans 120
Proportions
(50-50)
60
60
120
Top-Down
84
36
120
Bottom-Up
70
30
100
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Choosing a Reconciliation Approach
Group-level data is higher volume and often exhibits more
structure than lower-level data—this argues for top-down.
If two items exhibit distinctly different behaviors, then a single
model is unlikely to perform well—this argues for bottom-up.
If relationships between levels are not changing in time, then
forecasting based on history does not add value—this argues for
proportional allocation.
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Hierarchical Data
Total Sales
Brand A
Retail
Cans
6-Pack 12-Pack
Bottles
Commercial
½ Kegs Full Kegs
Brand B Brand C
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Choosing a Reconciliation Approach
Start by determining if proportional allocation should be used.
Try to think like your consumers – how do they view the items?
At times, lack of structure at the lower levels may force you to
use top-down approaches.
Measuring empirical performance is always an option.
Most multiple-level forecasts should combine top-down and
bottom-up approaches.
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Setting up the Hierarchy
The units of measure need to be comparable.
The hierarchy should be as simple as possible.
Discontinued items may need to be included to ensure group
totals are correct.
The structure of the hierarchy should be selected for forecasting,
NOT for reporting.
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
Two Alternative Hierarchies
Total
State 1
Business
Main Other
Residential
Basic Addline Other
Public
Coin Access
State 2 Etc.
Total
Business
Main
State 1 State 2 Etc.
Other
State 1 State 2 Etc.
Residential Public
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
Summary
Many organizations forecast hierarchical data and
require forecasts that are consistent across levels.
Defining the hierarchy to facilitate accurate
forecasting is important.
Selection of an appropriate reconciliation strategy is
also important and directly impacts forecast accuracy.
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
On-Demand Webinars & Materials
S
A recording of today’s Webinar will be posted next week
on forecastpro.com along with the slide set (in .pdf
format)
All previously presented Webinars are archived and
available for viewing on-demand on forecastpro.com
Attendees will receive an email notifying them when the
recording and materials are available
© 2018, Business Forecast Systems, Inc. www.forecastpro.com
Our Next Webinar
The Ins and Outs of Using Dynamic Regression Models for Forecasting
July 12, 2018 @ 1:30 pm EDT
Presented by Eric Stellwagen, CEO and Sarah Darin, Senior Consultant, Business Forecast Systems, Inc.
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