Page 1
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Universität Hamburg Institut für Wirtschaftsinformatik
Prof. Dr. D.B. Preßmar
Dr. Sven F. Crone
Dr. Nikolaos Kourentzes
Predictive Analytics for New Product Forecasting
More Statistics and less Judgment:More Analytics and less Judgment: a Case Study
New Product = Gambling?
Forecasting
Page 2
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Statistics? Judgment?
Both?
Statistics vs. Judgment in New Product Forecasting?
e.g. Delphi
Szenarios
Market Tests
Prediction Markets
e.g. Norton-Bass
Classification
Clustering
Social-media analysis
…
How to forecast high-tech products (CPU,RAM) at Intel? Product sourcing & production lead times are longer than sales
period every product is “new”
Often requires building new production facilities (or even plants) high costs for under & overforecasting
100s to 1,000s of new products per season
2-4 innovation cycles (selling seasons) per year Requires semi-automated & standardized process
How to they do New product forecasting?
The Challenge
Page 3
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Sales of products with continuous repeat purchasing?
Norton-Bass Model
Estimatetotal marketvolume fromfirst 3 sales
observations
Common Problem
Catalogue Retailers
order from China / India
3-6 months lead time
4 catalogues per year
Fashion Retailers
order from China / India
3-6 months lead time
3-12 seasons per year
Sourcing lead times are
longer than sales period
(for many products)
many products are “new”
Page 4
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
CDs, DVDs, BlueRays, Video Games …
Product launch & intial sales before, during & after go-live (i.e. pipeline-filling) are most important
Common Problem
Common Problem
… check out the scaled thumbnails of the first 8 weeks
of sales time series plots for 100 new DVD releases
Page 5
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Few things are completely new!
Delphi
Szenarios
Market Tests
Prediction Markets
Forecast using Analogies
How to find similar products from history (aka Analogies) automatically?
Finding Analogies: you try it!
Now
find:
Cluster 1: Cluster 2:
aka statistical technique called „Clustering“
Page 6
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Finds clusters of typical initial sales behavior
5 10 15 20 250
200
400
600
800
1000
1200
Time series with 27 months of observations
Hard to extract useful information!
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30
200
400
600
800
1000
1200
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30
200
400
600
800
1000
1200
No additional information
Find time series that
behave similarly
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30
200
400
600
800
1000
1200
… using Kmeans clustering
1 2 30
500
1000
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30
200
400
600
800
1000
1200
1 2 30
500
1000
1 2 30
500
1000
1 2 30
500
1000
Focus on the first three months (new products)
Analogies in Time Series?
Need to be careful in specifying SIMILIARITY !
New product to be launched
Only one initial order Match to most similar cluster
1 2 30
500
1000
1 2 30
500
1000
1 2 30
500
1000
1 2 30
500
1000
(plus 10s of product attributes etc.)
Analogies in Time Series?
Compare
similarity
Multiple observations …
Take average shape of
cluster as forecast!
Page 7
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
What is “Similarity”?
What is “Similarity”?
Page 8
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
What is “Similarity”?
What is “Similarity”?
Page 9
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
What is “Similarity”?
What is “Similarity”?
Page 10
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Distance
0.98
0.07
0.21
0.43
Rank
4
1
2
3
Database
n datapoints
n
iii sqSQD
1
2,
S
Q
Euclidean Distance between
two time series Q = {q1, q2, …, qn}
and S = {s1, s2, …, sn}
Find similar
shapes in
time series
pn
i
p
ii cqCQD 1
,
Manhattan
(p=1)
Max (p=inf) MahalanobisWeighted
Euclidean
Similarity in Time Series?
Empirical
Testing!
Theory?
Frost!
Tongue!
Not a
good
idea
How do we know that it works?
Page 11
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
• Curtains
• Tiebacks
• Cushion covers
• Valances
in various fabrics, linings, sizes, colours …
Case Study Task(s)
Clustering on the
first three observations
Clustering on the
first six observations
Clustering Approach Objectives?
Initial behaviour matching
Very short history
Longer history
Recalibrate matching
Clustering of the
1st pre-order
& product attributes
Forcast inital pipline filling
Without sales history
after the 6th month the history is long enough to use time series methods
support complete launch process!allocate to clusters on more data
Page 12
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Sample new product – First 52 weeks of deliveries
Pipeline filling Normal behaviour
How to forecast with zero weeks of sales?
Know initial
delivery volume
Automatic forecasting by structured analogy
Products0 10 20 30
20
40
60
80
100
120
140
160
Sales history
0 10 20 3020
40
60
80
100
120
140
160
0 10 20 3020
40
60
80
100
120
140
160
X
5 10 15 200
50
100
150
200
Classification to
repository
5 10 15 200
50
100
150
200
Adapt pattern
to new product
Repository of
clusters (with
archetypical new
product sales)
Initial known orders
(interview, trial etc.)
3000 units
75000 units
45 units...
Calculate
safety stock
and use!
Stage I - Clustering
Stage II - Forecast
time series clustering
New Product Clustering
Page 13
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Clustering Results3 first observations
I. 262 products used with history up to 27 months
II. Each product → Only first three months used
III. 8 clusters → Optimal number
IV. Each cluster → Average behaviour found
V. Average behaviour → New products will be matched with
1 1.5 2 2.5 3
200
400
600
800
1000
Cluster 1 Members: 14
1 1.5 2 2.5 3
200
400
600
800
1000
Cluster 2 Members: 22
1 1.5 2 2.5 3
200
400
600
800
1000
Cluster 3 Members: 56
1 1.5 2 2.5 3
50
100
150
200
Cluster 1 Members: 46
1 1.5 2 2.5 3
50
100
150
200
Cluster 2 Members: 9
1 1.5 2 2.5 3
50
100
150
200
Cluster 3 Members: 18
1 1.5 2 2.5 3
50
100
150
200
Cluster 4 Members: 30
1 1.5 2 2.5 3
50
100
150
200
Cluster 5 Members: 67
Cluster 1 Cluster 2 Cluster 3 Cluster 4
Cluster 5 Cluster 6 Cluster 7 Cluster 8
Business intelligence can eliminate/correct clusters → Cluster 5 case
Clustering Results3 first observations
Page 14
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Forecasting
Using the estimation for the first month value and the product type
find the correct cluster using the three first observations clusteringI.e.g. First value = 150, Unlined curtain Cluster 7!
Forecasting
Using the estimation for the first month value and the product type
find the correct cluster using the three first observations clusteringI.Use the % change of the average shape of the cluster to form the
2nd and 3rd valueII.
Second Value (Month):
150 * (1-0,4823) = 77.65
Third Value (Month):
77.65 * (1+0,2044) = 93.52
Page 15
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Forecasting
Using the estimation for the first month value and the product type
find the correct cluster using the three first observations clustering.I.Use the % change of the average shape of the cluster to form the
2nd and 3rd value.II.For the 4th-6th month forecasts use the clustering of the first 6 months
to find the product membership (similar procedure!).III.
Update the forecasted values with real whenever they are available.
If possible re-evaluate membership when new values are available.IV.
To forecast after the sixth value/month use a statistical model. The
product history is now long enough!V.
Time series clustering always outperforms statistical approach
Time series clustering outperformed human demand planner
How accurate is it?
Evaluate on time series of the given data
Statistical ForecastingProposed Approach
1 1.5 2 2.5 30
200
400
600
800
1000
1200
Test1
Test2
Test3
Forecast1
Forecast2
Forecast3
1 1.5 2 2.5 30
200
400
600
800
1000
1200
Test1
Test2
Test3
Forecast1
Forecast2
Forecast3
New Product ForecastingExperimental Evaluation
Page 16
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
5 10 15 20 250
100
200
300
400
500
600
700
New Product Clustering
5 10 15 20 250
100
200
300
400
500
600
700
5 10 15 20 250
100
200
300
400
500
600
700
5 10 15 20 250
100
200
300
400
500
600
700
5 10 15 20 250
100
200
300
400
500
600
700
... ...
Historical Data
Clustering Profiles
Allows us to
measure safety stock!
5 10 15 20 250
100
200
300
400
500
600
700
Empirical EvaluationInventory Performance
Inventory Performance Results
150 200 250 30020
40
60
80
100
120
140
160Sales
Inventory
Sto
ck-o
ut
150 200 250 30020
40
60
80
100
120
140
160Sales & Features
Inventory
Sto
ck-o
ut
S1: Features
S2: Initial Orders
S3: Features & I. Orders
S4: Features & Sales (2)
S5: Features & Sales (6)
Clustering on
Page 17
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Outcome: simple MS EXCEL application that can be automated
Clustering Results
A simple Forecasting Tool
Take aways
Many industries specialize onNew Product Forecasting opportunities to learn (steal fire)
Clustering creates insightful groups of similar products helpful to planners to improve judgment
Allows large-scale automation of new product forecasting
Predictive Analytics provides new solutions to forecasting challenges
… room for improvement![LCF ISF09, SAS New Product Presentation ISF 2008]
Looking for
companies for a
free-of-charge
pilot study
(to present at IBF?)
Page 18
New Product Forecasting with Analytics© 2016 by Sven F. Crone - all rights reserved.
Dr. Sven F. CroneAssistant Professor, Director
Lancaster University Management School
Research Centre for Forecasting
Lancaster, LA1 4YX
Tel. +44 1524 5-92991
Questions?
Dr. Sven F. CroneAssistant Professor, Director
Lancaster University Management SchoolResearch Centre for Forecasting
Lancaster, LA1 4YXTel. +44 1524 5-92991
Copyright © All rights reserved, Dr. Sven F. Crone
These slides are from a presentation on forecasting.
You may use these slides for internal purpose only, so long as they
are clearly identified as being created and copyrighted
© by Sven F. Crone.
All rights are reserved! You may not use the text and images in a
paper, tutorial or external training, duplicate or replicate or alter
them, or make them accessible as is, without explicit prior
permission from the author.