1. Decision Support Systems Design for
Data-Driven Management
PhD Candidate: Ningrong LEI
Supervisor: Dr. Seung Ki MOON
School of Mechanical & Aerospace Engineering
Division of Systems & Engineering Management
DETC2014-34871
ASME 2014 International Design & Engineering Technical Conferences, Buffalo, New York, USA

2. Outline
 Data-driven dilemma.
 Construct the decision support systems.
 Case study and discussion.
 Conclusion and future work.
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DETC2014-34871

3. Data alone will not improve management
decisions
Source: :
www.v1shal.com
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4. Market Segmentation
Source: .
:Cameron Industrials
Platform Strategy Advisory
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5. Decision support methods for market
segmentation
 Non data-driven
– Ground on “common sense” rather than on a solid
empirical base
 Data-driven
– Generally lack of reliability assessments
– Restrict to a few comparison
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6. When data-driven management goes bad…
Source: :
www.v1shal.com
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7. The success of the data-driven management
relies on:
 Quality of the gathered data
 Reliable model
 Effectiveness of data analysis
 Objectiveness of results interpretation
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8. Objectives
Develop a reliable decision support system to:
 Identify market segmentation based on market data
 Determine product positioning
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9. Online system
Block diagram of the proposed DSS
Intrinsic dimension
estimation
Dimension
reduction
Performance
evaluation
Clustering
Dimension
reduction
Automated
classification
Clustering
Market Data
Offline system
Best dimension
Reduction algorithm
Best clustering
algorithm
Best training
model
Product Data
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10. Case study: automobile market
 34 brands,639 models,31 model properties
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11. Online system
Dimension reduction steps
Intrinsic dimension
estimation
Dimension
reduction
Performance
evaluation
Clustering
Dimension
reduction
Automated
classification
Clustering
Market Data
Offline system
Best dimension
Reduction algorithm
Best clustering
algorithm
Best training
model
Product Data
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12. Dimension reduction techniques
Intrinsic dimensionality estimation:
• Correlation Dimension Estimator
• Eigenvalue-Based Estimator
• Maximum Likelihood Estimator
• Geodesic Minimum Spanning Tree
Dimension reduction:
• Principle Component Analysis
• Multidimensional Scaling
• Local Linear Embedding
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13. Online system
Block diagram of the proposed DSS
Intrinsic dimension
estimation
Dimension
reduction
Performance
evaluation
Clustering
Dimension
reduction
Product Positioning
Market
Segmentation
Market Data
Offline system
Best dimension
Reduction algorithm
Best clustering
algorithm
Best training
model
Product Data
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14. Clustering
 K-means
 Fuzzy C-Means
 Hierarchical Clustering
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15. Performance Evaluation
 Statistical tests:
– median and standard deviation
 Silhouette Means:
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16. Performance Evaluation
How accurately a predictive model will perform in
practice?
 Stratified 10-Fold Cross-Validation
 Classification:
– Gentle AdaBoost (GA)
– Nearest Neighbour (NN)
– Support Vector Machine (SVM)
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17. Online decision support system
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18. Clustering and performance evaluation results
Clustering Performance evaluation
Cluster-
ing
Dimension
reduction
Dimension
estimation
Number
of
clusters
Silhouette
mean
GA
in %
NN
in %
SVM
in %
K-
means
PCA
2 2 0.67 25.24 98.25 99.68
3 2 0.61 25.08 96.83 99.52
10 11 0.40 95.50 95.50 96.50
MDS
2 2 0.67 74.13 97.78 99.68
3 2 0.61 25.08 96.03 99.52
10 2 0.48 25.71 94.13 100.00
LLE
2 2 0.89 97.38 99.76 100.00
3 3 0.81 99.02 98.05 99.76
10 2 0.85 0.95 99.76 100.00
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19. Classification accuracy over all clusters
• On average, SVM outperforms NN and GA
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20. Conclusion
 Deliver a blueprint on how to construct a decision
support system.
– Offline system: find the most suitable algorithms structure;
– Online system: deliver objective and reliable decision support.
 Data-driven management: right data, proven statistics,
logical explanations.
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