This presentation utilizes Hierarchical Cluster Analysis, Principal Component Analysis and Data Envelopment Analysis in the identification of clusters of provinces in the YRD Region in China.

1. By: Dr. Kang Mun Arturo Tan
Management Sciences Dept
YUC R&D Unit
Date: April 28, 2015 – Tuesday
Time: 4:30 PM
Place: Jabria-2, Main Auditorium

2. Acknowledges the support of the following:
Dr. Abdulmoniem Alzalabani, DMD-AA
MIS 440 - Students
INSPIRATION FOR FURTHER DEVELOPING
THE MIS440 COURSE.`
SUPPORTED THIS RESEARCH IN MANY WAYS.

3. This presentation is an extract of the paper:
The Design of Logistics Infrastructure Network
and Logistics Information Platform in the Yangtze
River Delta: A Comparative Review
by: Dr. Kang Mun Arturo Tan, Jr.

4. 1 The Importance of the Yangtze River Delta to China
2 Role of Logistics in Integrating/Boosting the Economy
3 Logistics Efficiency, Effectiveness and Differentiation
4 Identifying Key Areas for Logistics Network Infrastructure
5 Data Preparation and Analysis
Tools used:
Hierarchical Cluster Analysis
Data Envelopment Analysis
Principal Component Analysis
6 Logistics Information Platform Design based on Data Mining
7 Conclusion

7. The Importance of the Yangtze River Delta to China
The YRD accounts for 20% of China’s GDP.
The YRD economic zone refers to 16 cities in Shanghai, southern Jiangsu,
eastern and northern Zhejiang.
Shanghai, Nanjing,
Suzhou, Wuxi,
Changzhou, Yangzhou,
Zhenjiang, Nantong,
Taizhou (North), Hangzhou,
Ningbo, Huzhou,
Jiaxing, Shaoxing,
Zhoushan Taizhou (South).
2% of China’s Land Area
7.7% of China’s Population
YRD 210,700 KMSQ 105,000,000 PERSONS
CHINA 9,597,000 KMSQ 1,357,000,000 PERSONS
AS OF 2014

8. Role of Logistics in Integrating/Boosting the Economy
Availability of Logistics brings about the following:
1_ One Province-One Product Specialization
2_ Employment of all local skills from Muscles to Brains
3_ Significantly lowers cost of production
4_ Attracts foreign direct investments substantially
Paraphrased from:
Logistics Clusters – Yossi Sheffi

9. Logistics Efficiency, Effectiveness and Differentiation
(Fugate, Mentzer and Stank, 2010)
Efficiency, Effectiveness and Differentiation
can be pursued simultaneously.
Organizational
Performance`
Logistics
Performance
Effectiveness Efficiency Differentiation

10. Overall economy level I1 (GDP)
Total investment I2 (TIFA)
Disposable Income I3 (DIUH)
Consumption Expenditure I4 (CEUH)
Industry Developing Level I5 (NSSE)
Gross Industrial Output Value I6 (GIDV)
Total Retail Sales I7 (TRSCG)
Foreign Direct Investment I8 (FDI)
Economic Development Level
Accessibility of trucks I9 (PCMV)
Accessibility of roads I10 (HWD)
Transportation Accessibility
Input Variables

11. Capability of railway O1 (RTVFT)
Capability of roads O2 (HTVFT)
Capability of waterway O3 (WTVFT)
Turnover volume of freight traffic
Output Variables

12. SerialNo City I1x10-8/Yuan I2x10-8/Yuan
I3/(Yuan/person-
1)
I4/(Yuan/Person-
1)
I5
I6x10-
6/Dollar
I7x10-
8/Yuan
I8x10-
6/Dollar
I9
I10/(m.km-
2)
O1x10-8/(t-
km)
O2x10-
8/(t.km)
O3x10-8/(t-
km)
1 Hangzhou 251.5 120.2 14,565 11,213 5,627 414.9 70.4 14.1 410.8 406 48.0 1,311.7 528.9
2 Ningbo 215.8 110.4 15,882 11,283 8,263 381.5 59.6 21.0 266.2 600 120.0 989.0 473.4
3 Wenzhou 140.3 50.7 17,727 14,212 5,401 188.6 58.8 2.1 233.9 464 5.0 827.7 239.9
4 Jiaxing 105.1 63.5 14,693 10,689 4,860 174.4 32.5 10.2 118.2 527 9.6 236.0 546.3
5 Huzhou 59.1 36.6 13,487 9,380 2,348 83.3 20.8 6.1 67.6 522 20.0 468.3 726.9
6 Shaoxing 131.4 62.9 15,642 10,608 3,876 251.4 33.5 8.2 132.9 513 99.9 813.2 117.6
7 Jinhua 97.8 51.8 13,910 9,879 3,288 113.8 36.2 4.3 175.4 632 33.3 1,064.9 23.6
8 Quzhou 28.4 20.3 11,477 8,284 650 23.6 10.5 .2 31.2 350 44.3 641.1 .3
9 Zhoushan 17.2 12.8 13,747 9,835 450 22.2 8.8 .2 15.6 655 .0 143.9 399.1
10 Taizhou(Zhejiang) 117.4 47.9 16,113 12,130 3,825 135.3 30.3 3.0 171.2 438 .0 536.2 348.7
11 Lishui 26.5 17.6 11,892 8,686 670 24.8 11.9 .2 39.6 273 17.6 245.0 38.2
12 Xuzhou 109.6 44.5 9,840 6,669 959 95.2 27.4 3.0 115.9 868 153.5 522.5 66.2
13 Changzhou 110.1 58.9 12,868 9,878 3,161 201.3 32.4 5.4 128.7 811 36.1 433.4 95.2
14 Suzhou 345.0 155.5 14,451 9,783 5,044 730.8 62.5 46.5 355.1 760 16.8 694.2 189.2
15 Nantong 122.6 60.7 10,937 7,768 2,715 160.3 38.4 10.2 107.9 1,086 2.6 629.6 153.7
16 Lianyungang 41.6 24.6 8,872 6,218 552 28.8 14.1 2.3 50.2 644 124.7 332.8 25.1
17 Huai'an 50.1 27.6 8,209 5,704 885 45.1 15.6 .9 53.1 569 5.3 195.3 169.9
18 Yancheng 87.1 37.5 9,362 6,566 1,795 88.9 25.6 1.4 62.3 512 1.5 302.7 414.5
19 Yangzhou 78.8 33.0 9,851 6,509 1,773 113.0 22.8 7.5 67.2 823 .4 419.7 131.7
20 Zhenjiang 78.1 32.1 10,858 7,374 1,531 107.3 19.2 5.6 67.0 790 79.4 428.3 34.9
21 Taizhou(Jiangsu) 70.5 30.6 9,695 6,318 1,492 99.7 20.1 3.8 61.7 798 .0 203.2 334.2
22 Xiuqian 33.6 19.7 6,378 4,403 516 14.8 8.8 .1 35.8 715 1.5 156.1 35.0
23 Shanghai 745.0 308.5 16,683 12,631 12,316 1,288.5 245.5 65.4 2,028.5 1,231 681.4 3,155.4 3,014.8
24 Nanjing 191.0 120.2 11,602 8,350 2,165 328.5 71.1 15.1 247.0 1,243 98.7 974.1 620.6
25 Wuxi 235.0 111.4 13,588 9,518 4,543 457.5 57.9 19.5 234.5 866 18.6 617.4 107.0
National Bureau of Statistics of China (2007), China Statistical Yearbook (2007),
China City Statistical Yearbook (2007) and
the China Statistical Yearbook for Regional Economy (2007)

13. Z-score = ( x – Avg ) / stdDev

14. Rui Zhang – ZheJiang GongShang University, China – ( IEEE, 2008)
Using the Hierarchical Cluster Analysis (and with more current data)
‘generated four clusters.
‘Later, we will discuss the PCA-DEA tool.
But did not separate the concept of efficiency and effectiveness.

15. Rui Zhang – ZheJiang GongShang University, China – ( IEEE, 2008)

16. Rui Zhang- recomputation
Cluster 1: Regions Shanghai, Ningbo, Wenzhou
Cluster 2: Regions Hangzhou, Jiaxing, Huzhou
Shaoxing, Jinhua,TaishouZhe,
Changzhou, Suzhou, Wuxi
Cluster3:Regions Xuzhou, Lianyungang, Huaian
Yancheng, Yangzhou, TaizhouJia
Xiuqian
Cluster4: Regions Quzhou, Zhoushan, Lishui
Nantong, Zhenjiang, Nanjing

17. Four Categories of Logistics Facility in China
Hub
Central Distribution Center
Regional Distribution Center
Distribution Center
Rui Zhang -
Customer
Customer
Customer
DC
DC
DC
RDC
RDC
RDC
CDCHub
TRATRA
DIS
DEL
TRA
(2008, IEEE) Uses Hierarchical Cluster Analysis

18. Ju, Jiang, Chen – Shanghai Jiaotong University and Springer-Verlag (2012)
- combined
Principal Components Analysis
- BASED ON VARIABLE REDUCTION
‘and the
Data Envelopment Analysis
- BASED ON RATIO ANALYSIS

19. In their paper, Ju-Jiang-Chen defined
Cp - efficiency (implies a focus on aggregated view of the
economic vibrancy of that city)
‘while
DEA – effectiveness (implies a focus on cost-effectiveness)
Ju, Jiang, Chen – Shanghai Jiaotong University and Springer-Verlag (2012)

20. Cp (Vibrancy)
DEA ( Capability / Vibrancy ) = Cost Effectiveness
1.0
0.9
0.5
0.6 12-4
* Shanghai
* Wuxi *Suzhou
* Taishou(Zhejiang)
* Xiuqian
“Benchmark
Quadrant/Cluster”
“Efficiency
Quadrant/Cluster”
“Weakness
Quadrant/Cluster”
“Effectiveness
Quadrant/Cluster”

21. Ju, Jiang, Chen – Shanghai Jiaotong University and Springer-Verlag (2012)
Benchmark Shanghai Ningbo Hangzhou Wenzhou Nanjing
Efficiency Suzhou Wuxi
Effectiveness Shaoxing Taizhou(zh) Jinhua Huzhou Xuzhou
Zhoushan Yancheng Quzhou Lianyungang
Weakness Jiaxing Changzhou Nantong Zhenjiang Yangzhou
Taizhou(Jia) Lishui Huaian Xiuqian

22. Logistics Information Platform Design based on Data MiningPeihua Fu and Xiaoli Gu
Logistics Information Platform plays a vital role in the economic development
of the YRD. – paraphrased.
Further, Benjamin S. Blanchard, in Logistics: A Total Systems Approach, identified
information systems as necessary ingredient in the overall concept, as shown:
Logistic
Support
Maintenance
and Support
Planning
Technical Data,
Information
Systems/ Databases
Computer
Resources
(Hardware/Software)
Training and
Training Support
Packaging, Handling,
Storage/Whsg &
Transportation/Distn
Maintenance
and Support
Planning
Maintenance
Facilities
(Utilities)
Test, Measurement,
Handling, & Support
Eqp/Resources
Supply Support
Spare/Repair Parts
and Inventories

23. Logistic
Information
Platform
Carriers Seller Government
Agents
(Supply Side)
Buyers
Other
Logistics
Services
Agents
(Demand Side)
E-government
System
Fu-Gu : Fig 48.1 Logistics Information Platform
Requirements Analysis Chart

24. Participants
From Logistics Information
Platform participants:
From participants to
Logistics Information
Platform
Carriers Transport Request Goods information
Logistics service bid
Seller Logistics services Logistics services
Procurement bidding
Publishing information
Government Posted industry mgt std
Implementation of
industry mgt
Access to transaction data

25. Participants
From Logistics Information
Platform to participants:
From participants to
Logistics Information
Platform
Agents (Supply Side) Storage Request Inventory records
Bid
Storage investment
Agents (Demand Side) Accept Agent Release Agent Info
Logistics services Logistics services
Buyers Logistics Services Logistics Services
Get product information Procurement Info
Other logistics services Info Distn Services
Logistics Services
Accept the request Accept the request

26. Wu, Liu, Jin, Guo – The Design and Analysis of Logistics Information System Based on Data Mining
- Springer-Verlag 2012
The competitiveness of the
enterprise
will be greatly enhanced with
the inclusion of data mining
activity within the information
system platform.
The following diagram shows
this:
database database database
Monitor Monitor Monitor
Integration
DATAWAREHOUSE
Data Mining
Algorithm

27. Conclusion:
Logistics Network Infrastructure – based on the:
Economic Vibrancy and Transport Capacity
Resources allocated on clusters based on the following tools:
Hierarchical Cluster Analysis
Principal Component Analysis
Data Envelopment Analysis
The corresponding Logistic Information Platform should
be able to support:
Logistics Transactions
Data mining Activities

28. Thank You
for
Your Attendance