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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 1 | 25 October 2010
A Revolutionary Method to do
Manufacturing Demand Forecasting
with SVM - Support Vector Machines
Dr. Thomas Wlfl, TM3 Software
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 2 | 25 October 2010
Agenda
1. Demand planning
2. Methods for demand forecasting
3. Artificial intelligence and machine learning
4. Support vector machines (SVM)
5. Example and results
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 4 | 25 October 2010
Demand Planning
Forecasting based on past demand
Data Mining:
Use of statistical tools
Experience of market experts
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 5 | 25 October 2010
Demand Planning
Forecasting based on partner forecasts
Combine forecasts:
Use of data tools
Experience of market experts
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 6 | 25 October 2010
Demand Planning
Combine both ways: Use past demands
Use customer forecasts
Add external information influencing demand
Market figures, seasonal factors,
Learn the experience of maket experts
Use Artifical Intelligence (AI) andMachine Learning (ML) technology
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 7 | 25 October 2010
Agenda
1. Demand planning
2. Methods for demand forecasting
3. Artificial intelligence and machine learning
4. Support vector machines (SVM)
5. Example and results
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 8 | 25 October 2010
Methods for demand planning
Methods based on past demands:
Naive Forecast
Average
Auto Regressive Moving Average (ARMA)
Multiple Linear Regression
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 9 | 25 October 2010
Forecasting Methods
Example: Demand of a certain article during the last year
Real demand
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 10 | 25 October 2010
Forecasting Methods
Demand forecast usingAuto Regressive Moving Average (ARMA)
Problem: ARMA parameter selection
Real demand Forecast for the next period: 75 piecesARMA forecast
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 12 | 25 October 2010
Agenda
1. Demand planning
2. Methods for demand forecasting
3. Artificial intelligence and machine learning
4. Support vector machines (SVM)
5. Example and results
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 13 | 25 October 2010
Machine Learning
Discipline of Artificial Intelligence (AI)
Machine Learning (ML): An algorithm automaticallylearns in order to make accurate predictions based onpast observations (Rob Schapire)
Applications: Face detection: find faces in images
Disease diagnosis: detect cancer in blood count
Spam filtering
Fraud detection Weather prediction
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 14 | 25 October 2010
Machine Learning
Image classification example: Training
Given: training images and their categories
Class 1
Class 2
Class 3
Class 4
Class 5
Class 6
Untrained
ML-Algorithm
Features oftraining
data
[Verbeek, 2009]
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 15 | 25 October 2010
Machine Learning
Image classification example: Testing
What are the categoriesof these test images?
TrainedML-Algorithm
Class 3
Classificationresult
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 16 | 25 October 2010
Machine Learning
Image classification example: Testing
What are the categoriesof these test images?
TrainedML-Algorithm
Class 4
Classificationresult
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 17 | 25 October 2010
Machine Learning
ML-Regression: Similar to classification, but the output
has the form of one or more real numbers
Learns a function (e.g. the demand function)
Example: predict gas mileage of a car based on numberof cylinders or doors, weight, horsepower, year, etc.
[Verbeek, 2009]
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 18 | 25 October 2010
Machine Learning
Machine Learning methods:
Decision Trees
Bayesian Networks
Neuronal Networks
Support Vector Machines
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 19 | 25 October 2010
Agenda
1. Demand planning
2. Methods for demand forecasting
3. Artificial intelligence and machine learning
4. Support vector machines (SVM)
5. Example and results
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 21 | 25 October 2010
Support Vector Machines
SVM-Demo: Regression for demand forecasting
Input Variables:
Average demand of last period
Forecast of this period
Seasonal factor
Regression:
Learn the demand function
-s 3 -t 2 -c 10 -g 1000 -p 0
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 22 | 25 October 2010
Support Vector Machine
SVM concept: Learn a separating borderline between
the given data
Income
Living Space
Good credit score
Bad credit score
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 23 | 25 October 2010
Support Vector Machine
Which borderline to use?
Good credit score
Bad credit scoreIncome
Living Space
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 24 | 25 October 2010
Support Vector Machine
Select the borderline which maximizes the marign!
Income
Living Space
MarginWidth A
MarginWidth B
Good credit score
Bad credit score
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 25 | 25 October 2010
Support Vector Machine
What to do now? Good credit scoreBad credit score
Income
Living Space
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 26 | 25 October 2010
Support Vector Machine
Use a non-linear separation function Add a new feature: living space, income, age
Income
Living Space
Age
Living Space
Income
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 27 | 25 October 2010
Agenda
1. Demand planning
2. Methods for demand forecasting
3. Artificial intelligence and machine learning
4. Support vector machines (SVM)
5. Example and results
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 29 | 25 October 2010
SVM demand forecastingkey facts
SVM is a field-proven method, e.g. used in medical informatics it-security
SVM filters outliers and noise in demand data
SVM handles multiple dimensions (features), large amount ofinput data and non-linear relations
Experiments show that SVM has a better forecast quality(MAPE) then traditional methods
SVM forecast quality is easy to measure: compare forecastwith historical data
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 30 | 25 October 2010
DEMAND FORECASTING WITH SVM -SUPPORT VECTOR MACHINES
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2010 Supply Chain Council. ALL RIGHTS RESERVED. | < filename > | Slide 31 | 25 October 2010
Support Vector Machine
SVM-Regression: Learn the demand function
Demand
Time