Download Artificial Intelligence for Data Mining in the Context of

Artificial Intelligence
for Data Mining in the
Context of Enterprise
Systems
Thesis Presentation by
Real Carbonneau
Overview
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Background
Research Question
Data Sources
Methodology
Implementation
Results
Conclusion
Background
Information flow in the extended supply chain
$
$
Collaboration Barrier
$
Order
Manufacturer
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Order
Order
Wholesaler
Order
Retailer
Distributor
Information distortion in the supply chain
Difficult for manufacturers to forecast
Customer
Current solutions
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Exponential Smoothing
Moving Average
Trend
Etc..
Wide range of software forecasting solutions
M3 Competition research tests most forecasting
solutions and finds the simplest work best
Artificial Intelligence
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Universal Approximators
Artificial Neural Networks (ANN)
 Recurrent Neural Networks (RNN)
 Support Vector Machines (SVM)
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Theorectically should be able to match or
outperform any traditional forecasting approach.
Neural Networks
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Learns by adjusting weights of connections
Based on empirical risk minimization
Generalization can be improved by:
Cross Validation based early stopping
 Levenberg-Marquardt with Bayesian Regularization
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Support Vector Machine
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Learns be separating data in a different feature
space with support vectors
Feature space can often be a higher or lower
dimensionality space than the input space
Based on structural risk minimization
Optimality guaranteed
Complexity constant controls the power of the
machine
Support Vector Machine CV
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10-fold Cross Validation based optimization of Complexity
Constant
More effective than NN because of guaranteed optimality
Support Vector Machine Cross Validation Error for Com plexity Constant
8.60E+04
8.40E+04
8.20E+04
Demand
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8.00E+04
7.80E+04
7.60E+04
7.40E+04
8
8
7
7
6 0 6 0 5 0 4 0 4 0 3 0 3 0 2 0 2 0 1 00 00 01 01 02
-0 -0 -0 -0 -0
E
E
E
E
E
E
E
E
E
E
E
E
E
E E+ E+ E+ E+ E+
00 .80 .44 .48 .08 .90 .00 .14 .32 .64 .24 .37 .99 .42 .30 .93 .87 .10 .70
.
1
3
1
5
2
7
3
1
4
1
6
2
8
3
1
4
1
7
2
Com plexity Constant
SVM Complexity Example
SVM Complexity Constant optimization based
on 10-Fold Cross Validation
Support Vector Machine Forecasts w ith varying Com plexity Constants
3.50E+05
3.00E+05
2.50E+05
Actual
2.00E+05
High Complexity
Low Complexity
1.50E+05
Optimal Complexity
1.00E+05
5.00E+04
Period
23
21
19
17
15
13
11
9
7
5
3
0.00E+00
1
Demand
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Research Question
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For a manufacturer at the end of the supply
chain who is subject to demand distortion:
H1: Are AI approaches better on average than
traditional approaches (error)
 H2: Are AI approaches better than traditional
approaches (rank)
 H3: Is the best AI approach better than the best
traditional
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Data Sources
3.
Dem and for Top Product
600,000.00
500,000.00
400,000.00
300,000.00
200,000.00
100,000.00
20
00
1
20 0
01
0
20 1
01
0
20 4
01
0
20 7
01
1
20 0
02
0
20 1
02
0
20 4
02
0
20 7
02
1
20 0
03
0
20 1
03
0
20 4
03
0
20 7
03
1
20 0
04
0
20 1
04
0
20 4
04
07
2.
Chocolate Manufacturer (ERP)
Toner Cartridge Manufacturer (ERP)
Statistics Canada Manufacturing Survey
Demand
1.
Year and Month
Methodoloy
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Experiment
Using top 100 from 2 manufacturers and
random 100 from StatsCan
Comparison based on out-of-sample testing set
Implementation
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Experiment programmed in MATLAB
Using existing toolbox where possible (eg, NN,
ARMA, etc)
Programming missing ones
SVM implemented using mySVM called from
MATLAB
Experimental Groups
CONTROL GROUP
Traditional Techniques
Moving Average
Trend
Exponential Smoothing
Theta Model (Assimakopoulos &
Nikolopoulos 1999)
Auto-Regressive and Moving Average
(ARMA) (Box and al. 1994)
Multiple Linear Regression (AutoRegressive)
TREATMENT GROUP
Artificial Intelligence Techniques
Neural Networks
Recurrent Neural Networks
Support Vector Machines
Super Wide model
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Time series are short
Very noisy because of supply chain distortion
Super Wide model combined data from many
products
Much larger amount of data to learn from
Assumes similar patterns occur in the group of
products.
Result Table (Chocolate)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Cntrl./Treat.
Treatment
Treatment
Control
Treatment
Control
Control
Control
Control
Control
Control
Control
Control
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Control
Control
Control
MAE
0.76928454
0.77169699
0.77757298
0.79976471
0.82702030
0.83291872
0.83474625
0.83814324
0.85340016
0.86132238
0.87751655
0.90467127
0.92085160
0.93065086
0.93314457
0.93353440
0.94270139
0.98104892
0.99538663
1.01512843
1.60425383
8.19780648
Method
SVM CV_Window
SVM CV
MLR
ANNBPCV
ES Init
ES20
Theta ES Init
MA6
MA
ES Avg
Theta ES Average
MLR
ANNLMBR
RNNLMBR
ANNLMBR
SVM CV
SVM CV_Window
ANNBPCV
RNNBPCV
ARMA
TR
TR6
Type
SuperWide
SuperWide
SuperWide
SuperWide
SuperWide
Results Table (Toner)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Cntrl./Treat.
Treatment
Treatment
Control
Control
Control
Treatment
Control
Control
Treatment
Control
Control
Control
Control
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Control
Control
Control
MAE
0.67771156
0.67810404
0.69281237
0.69929521
0.69944606
0.70027399
0.70535163
0.70595237
0.72214623
0.72443731
0.72587771
0.73581062
0.76767181
0.77807766
0.80899048
0.81869933
0.81888839
0.84984560
0.88175390
0.93190430
1.60584233
8.61395034
Method
SVM CV
SVM CV_Window
ES20
MA6
ES Init
SVM CV_Window
MA
MLR
SVM CV
Theta ES Init
ES Avg
Theta ES Average
MLR
ANNLMBR
RNNBPCV
RNNLMBR
ANNLMBR
ANNBPCV
ANNBPCV
ARMA
TR
TR6
Type
SuperWide
SuperWide
SuperWide
SuperWide
SuperWide
Results Table (StatsCan)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Cntrl./Treat.
Treatment
Treatment
Control
Treatment
Treatment
Control
Control
Control
Control
Control
Control
Control
Treatment
Treatment
Control
Treatment
Treatment
Treatment
Treatment
Control
Control
Control
MAE
0.44781737
0.45470378
0.49098436
0.49144177
0.49320980
0.50517910
0.50547172
0.50858447
0.51080625
0.51374179
0.53272253
0.53542068
0.53553823
0.53742495
0.54834604
0.58718750
0.64527015
0.80597984
0.82375877
1.36616951
1.99561045
20.89770108
Method
SVM CV_Window
SVM CV
MLR
SVM CV_Window
SVM CV
Theta ES Init
ES Init
ES Average
MA
Theta ES Average
MLR
MA6
RNNLMBR
ANNLMBR
ES20
ANNBPCV
ANNLMBR
RNNBPCV
ANNBPCV
ARMA
TR
TR6
Type
SuperWide
SuperWide
SuperWide
SuperWide
SuperWide
Results Discussion
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AI provides a lower forecasting error on average.
(H1=Yes)
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Traditional ranked better than AI. (H2=No)
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However, this is only because of the extremely poor
performance of trend based forecasting
Extreme trend error has no impact on rank.
SVM Super Wide performed better than the best
traditional (ES). (H3=Yes)
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However, exponential smoothing was found to be the best
and no non-super-wide AI technique reliably performed
better.
Results SVM Super Wide details
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SVM Super Wide performed better than all others
Isolated to SVM / Super Wide combination only
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Other Super Wide did not reliably perform better than ES
Other SVM models did not perform better than ES
Dimensionality augmentation/reduction (non-linearity)
is important
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Super Wide SVM performed better than Super Wide MLR
Conclusion
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When unsure, us Exponential Smoothing it is
the simplest and second best.
Super Wide SVM provides the best performance
Cost-benefit analysis by a manufacturer should
help decide if the extra effort is justified.
If implementations of this technique proves
useful in practice, eventually it should be built
into ERP systems. Since it may not be feasible
to build for SME.
Implications
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Useful for forecasting models which should
include more information sources / more
variables (Economic indicators, product group
performances, marketing campaigns) because:
Super Wide = More observations
 SVM+CV = Better Generalization
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Not possible with short and noisy time series on
their own.