NPS MDP Study
Outbrief Schedule, 1 JUN 2005
0800-0815
0815-0915
0930-1015
1030-1130
Introductions
Background/Results
Cargo Inspection System (Land)
Cargo Inspection System (Sea)
1130-1230 LUNCH
1230-1330 Sensor System
1345-1445 C3I System
1500-1600 Response Force System
NPS MDP Study
Land Inspection Group
LT William Westmoreland, USN
MDP System
Operational Architecture
Land Inspection
System
[Detect?]
External
Intel?
Manifest
Info?
Sensor System
Y
[Track COI?]
Sea Inspection
System
[Detect?]
Anomaly?
C3I System
Force System
Y
[Action Taken?]
N
GOOD
OUTCOME
Y
[Action Success?]
N
N
BAD
OUTCOME
Land Inspection Agenda
•
•
•
•
•
•
•
•
System Insights
Objectives/Requirements
Functional Decomposition
Alternatives Generation
Model Overview
Model Assumptions and Factors
Results
Conclusions/Insights
NPS MDP Study System Insights
Land Cargo Inspection
• Effective Cargo Inspection requires
industry cooperation
Sea Cargo Inspection
• Enroute at-sea Cargo Inspections can be
effective using current handheld sensor
technology
Land System Group Objectives
• Characterize cargo security and inspection
process
• Identify methods to improve container security
and inspection efficiency
• Develop model for land inspection system
• Determine driving factors for land inspection
system
• Recommend system alternatives to improve land
inspection performance
Land System Requirements
• Implement within five years
• Maximize detection of hazardous materials
(CBRNE)
• Minimize delay
• Screen, target, and inspect cargo containers
• Provide information about containers, shippers,
and carriers
Land System Objectives
• Increase the number of containers inspected
• Communicate results
• Dedicated resources for analysis of sensor data
• Improve intermodal security of containers
• Flexible
Functional Decomposition
Land Inspection
Maintain
Accountability
Target
Detect
Communicate
Port of Singapore “As-Is”
• Container Security Initiative participant
• Five container terminals – Mostly “Hub”
transfer traffic
• Utilizes Free Trade Zones (FTZ)
• Only 1.4 % of containers inspected
• Limited chemical/biological detection capability
• Use x-ray & gamma ray imagers, radiological
detection pagers, and canines
Land Inspection “As-Is”
Singapore
Bound
In
Bound
Singapore
Customs
Port of
Singapore
MDP
Area of
Regard
Non-US
Out Bound
US Out
Bound
Land System Alternatives
Generation
TARGET
Container History
DETECT
Nuclear/Radiation
Chem./Bio.
Gamma-Ray Imager
Optical
Verification Method
Origin
Human Intelligence
Route
Surveillance System
Destination
Explosives
Contents
Flash
Manifest
Chromatography
Shipping
Vapor Company
Detector
Smart Container
Foreign Objects
Canine
X-Ray Machine
Manifest Comparison
Robotic/Mobile
Density
Imager
Inspection
Scintillation Counter
Flame Emission Photometry
Molecular Locator
Shape Recognition
COMMUNICATE
Human Inspection
Neutron Generator
Photo Ionization
X-Ray
Visual
Receive/Transmit
Record/Display
Tamper Seals
ACCOUNTABILITY
Laser MAINTAIN
Infrared Fluorescence
Infrared Spectrometer InfraredNeutron Activation Computer
AcousticDatabase
Attached Sensors
Track
Semiconductor Detector
Chemical Sensitive
Canine
Radio (UHF, etc.)
Voice/Video Recorder
Unmanned Vehicles
Electronics
Encoded Laser
Monitors
Automated
Interpretation
Check Points with
Thermo Luminescence
Surface Acoustic Wave
Cable/Fiber
Audio/Visual
Sensors Alarms
Sensor Mapping
Human Search
LIDAR
Ion Mobility Spectrometry
Satellite
Density Screening Unit
Data Analysis
Imaging Display
Flow Cytometry
Communications
Wideband Radar
Smart Container
Intelligence
Cell Phones
Divers
RFID
Sickness Locality
Internet
High Purity Germanium
Temper Resist Seal
Arrays
Verbal/Visual
Land System Alternatives
Overview
As-Is System
• Implement CSI concept
ALT 1: “Port-Centric”
• Inspections occur in ports
• Intelligence limited
ALT 2: “Trusted Agent”
• Enhanced security measures
• Heavy reliance on
intelligence
Supply Chain
Port of
Origin
Supply Chain
Port of
Origin
Supply Chain
Port of
Origin
ALT 1 -Port Centric Inspection
• Layered security integrating passive/active
sensors
• Inspections occur during normal container
operations
• Intelligence limited
• Port-centric security
ALT-1 Port-centric Inspection
Storage Area
Customs
Or
Or
Active Team
Passive
Mover
Passive
Crane
Passive
In
Bound
Random
Active
Search
Into Port
Mover
Staging
Area
Crane
Passive
Passive
Passive
Alert
Team
MDP
Area of
Regard
Out Bound
ALT 2 – Trusted Agent
• Layered security integrating passive/active sensors
• Inspections occur during normal container operations
• Targeting or selection of searched containers based on:
– Container seals
– Manifest Discrepancies
– Certified Shippers
– 2-3% randomly inspected
• Hybrid of port-centric inspection and supply chain
security
Trusted Agent
• Procedural Security
• Physical Security
• Personnel Security
• Education and Training
• Access Controls
• Manifest Procedures
• Transportation Security
SENSORS CONSIDERED
1
1.Gas Chromatography /
Ion Mobility Spectrometer
2
3
4
5
6
7
2. Radiation Pager
3. X-Ray Detector
4.Gamma-Ray Detector
5.Pulsed Fast Neutron
Analyzer
6.High Purity Germanium
Detector
7.Flow Cytometry
Overarching Modeling Plan
Pr(detect)
Pr(Defeat)
MOE1
Performance
FY05$
MOE2
Risk (Attack
Damage)
Land Insp
Sea Insp
Delay
time
FY05$
Attack
Damage
Model
Force
Sensors
C3I
Cost Models
Land Insp
Sea Insp
Force
C3I
Sensors
Performance Models
FY05$
Shipping
Delay Cost
Model
FY05$
M1
Commercial
Impact
M2
MDP System
Cost
Model Overview
•
•
Approach
– Performance and Delay Cost
Models Used
– EXTEND v6 Model
– Excel
Land System Model Assumptions
• Based on Port of Singapore:
– 2004 Port operations procedures.
– 2004 port statistics.
– Percentage of containers sent to temporary
storage
– Average container value of $25 K
– Inspection times based on port operations
Land Inspection Factors
• Number of inspection
teams
• Number of sensors
• Percentage inspected
randomly
• P(d) & P(fa) for sensors
• Container throughput
per month
• Inspection time per
sensor
** Varied factors in red
• Number of cranes
and movers
• Percentage of
containers in storage
• Days in storage
• Probability of given
threat
• Container value
• Number of ports
Land Performance Model Overview
Input Variables
•Number of Sensors
•Sensor Pd
•Number of Containers
•% Random Inspected
•Number of Active Teams
Outputs
Land
Inspection
Model
•P(Detection)
•Delay Time
•Commercial Cost
•System Cost
Land System Model Results
Number of Teams vs Delay Cost (Trusted Agent)
Number of Teams vs Delay Cost (Port-Centric)
3500
3500
3000
3000
Delay Cost ($M)
Delay Cost ($M)
2500
2000
1500
1000
2500
2000
1500
1000
500
500
0
0
2
5
10
15
20
25
35
40
Number of Teams
50
60
80
100
2
5
10
15
50 teams minimize delay cost
20
25
35
40
Number of Teams
50
60
80
100
Land System Inspection Results
Main Effects Plot for Delay Cost
Active Sensor Pd
400
Passive Sensor Pd
Sensor Pd’s have minimal effect on delay
Mean of Delay Cost M$
300
200
100
0
0.50
0.99
Passive Sensor FA
0.1
0.6
Number of Sensors
400
300
200
Passive False Alarm
drives delay cost
100
0
0.01
0.15
5
100
Land System Inspection Results
Main Effects Plot For System Probability of Detection
Active Sensor Pd
Passive Sensor Pd
0.8
Mean of Pdefeat
0.6
Passive detection
drives performance
0.4
0.2
0.50
0.99
Number of Sensors
0.1
0.6
Passive Sensor FA
0.8
0.6
0.4
Minimal effect on system detection
0.2
5
100
0.01
0.15
Land Inspection System
Variable Values
Values
Factors
Values Evaluated
As-Is
Alt 1
Alt 2
Number of Sensors
2-100
5
50
50
Active P(detection)
.3, .4, .5, .6, .85, .99
0.99
0.85
0.85
Active P(false alarm)
0.01
0.01
0.01
0.01
Passive P(detection)
.1, .6
0
0.6
0.6
.01, .15
0
0.01
0.01
Passive P(false alarm)
Land System Single Port Use
Case: Port of Singapore
System confined to
the Port of Singapore
Land System Results
Single Port*
MOE / Metric
‘As-Is’ ALT 1
ALT 2
Percent Cargo Inspected
6%
99%
99%
P(Detect I Inspect)
99%
87%
93%
P(Detect)
6%
87%
93%
Comm. Delay Cost ($M)
~0
1,921
1,688
Comm. Cost ($M)
0
0
1,753
Land System Cost ($M)
38
1,143
1,150
Total System Cost ($M)
38
3,064
4,591
* Modeled after the Port of Singapore
China
Shandong
Zhejiang
Shanghai
Shenzhen
Jiangsu
Guangdong
Picture/diagram
Japan
Yokohama
Nagoya
Chiba
U.S.A
Seattle
Tacoma
Oakland
Los Angeles
Long Beach
Thailand
Bangkok
Laem Chabang
Port of Singapore
System includes 16 High-Volume Ports that export to
the Port of Singapore.
Land System Results:
Inspections in 16 Highest-Volume Ports-of-Origin
MOE / Metric
‘As-Is’ ALT 1
ALT 2
% Inbound Cargo Inspected
2%
47%
74%
P(Detect I Inspect)
99%
88%
94%
P(Detect) all inbound cargo
2%
41%
56%
Comm. Delay Cost ($M)
~0
30,730
27,019
Comm. Cost ($M)
0
0
1,753
Land System Cost ($M)
608
36,677
33,841
Total System Cost ($M)
608
67,407
62,613
Overall Results
Single Port 10-Year Cost vs. P(Detect)
100%
P(Detect)
ALT 2
ALT 1
80%
60%
40%
20%
As-is
0%
0
1,000
2,000
3,000
4,000
5,000
10-Year Cost ($M)
Port system performance
increases with cost
16 High-Volume Ports 10-Year Cost vs. P(Detect)
100%
P(Detect)
80%
ALT 2
60%
40%
ALT 1
20%
As-is
0%
0
10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000
10-Year Cost ($M)
CONCLUSIONS
• Current System is inadequate in defeating an
attack:
– Container Volume
– Detection Capabilities Limited
– Costs Associated with Delay and False Alarm
• Best performance achieved through a layered
defense of ‘Port Centric’ and ‘Intelligence’
systems
CONCLUSIONS
•
Passive sensor P(d) drives system
•
Passive sensor P(fa) impacts delay cost
•
Effective supply chain security measures can
reduce delay cost
•
Increase in security measures will act to deter
illicit trade which may result in lower system
costs
RECOMMENDATIONS
• Invest in passive sensor technologies
• Continue development of sensor technologies
with penetration capabilities
• Offer incentives to industry
RECOMMENDATIONS
• Develop a method to test security measures
• More inspectors needed at domestic and
international ports
• Countries would benefit from implementation of
C-TPAT
• Research methods to decrease time to unload
containers for inspection
Questions?
• LT William Westmoreland, USN
• LT Micah Kelley, USNR
• 1st LT Hasan Gungor, TuAF
• ENS Jared Wilhelm, USNR
Back-Up Slides
Original Model – Port of LA
• Considers single WMD
• Simple, baseline model
Second Generation Model – Port of LA
Expanded
• Considers Nuclear, Biological and Chemical WMD
Third Model – Port of LA Expanded
w/ Layered Sensors
• Considers Nuclear, Biological and Chemical WMD
•Adds in realistic sensing layers and alert team
Port of Singapore “As-Is” Model
•Nuclear, Biological, Chemical and Explosive WMD capability
•Develop to represent current system as close as possible using
realistic, researched numbers
Excel Input File
Excel Output File
Alternative 1 Model
•Layered passive and active system
•Targets based on minimal intelligence… Port Centric
Excel Input File
Excel Output File
Alternative 2 Model
•Layered passive and active system
•Tagets based on intelligence, manifests and container seals
Excel Input File
Excel Output File