21-woelfl-scor demand planning - a revolutionary method to do manufacturing demand forecasting with...

8/8/2019 21-Woelfl-SCOR Demand Planning - A Revolutionary Method to Do Manufacturing Demand Forecasting With SVM - Support Vector Machines

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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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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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Demand Planning

Forecasting based on past demand

Data Mining:

Use of statistical tools

Experience of market experts


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Demand Planning

Forecasting based on partner forecasts

Combine forecasts:

Use of data tools

Experience of market experts


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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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Agenda

1. Demand planning






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Methods for demand planning

Methods based on past demands:

Naive Forecast

Average

Auto Regressive Moving Average (ARMA)

Multiple Linear Regression


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Forecasting Methods

Example: Demand of a certain article during the last year

Real demand


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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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Agenda

1. Demand planning






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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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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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Machine Learning

Image classification example: Testing

What are the categoriesof these test images?

TrainedML-Algorithm

Class 3

Classificationresult


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Machine Learning

Image classification example: Testing

What are the categoriesof these test images?

TrainedML-Algorithm

Class 4

Classificationresult


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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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Machine Learning

Machine Learning methods:

Decision Trees

Bayesian Networks

Neuronal Networks

Support Vector Machines


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Agenda

1. Demand planning






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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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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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Which borderline to use?

Good credit score

Bad credit scoreIncome

Living Space


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Select the borderline which maximizes the marign!

Income

Living Space

MarginWidth A

MarginWidth B

Good credit score

Bad credit score


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What to do now? Good credit scoreBad credit score

Income

Living Space


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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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Agenda

1. Demand planning






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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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DEMAND FORECASTING WITH SVM -SUPPORT VECTOR MACHINES

[email protected]


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SVM-Regression: Learn the demand function

Demand

Time

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