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Agent-Based Joint Theater Logistics Management

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Title: Agent-Based Joint Theater Logistics Management


1
Agent-Based Joint Theater Logistics Management
  • Dr. Thomas E. Potok
  • Collaborative Technologies Research Center
  • Computer Science and Mathematics Division
  • Oak Ridge National Laboratory
  • Lockheed Martin Energy Research

2
Collaborative Technologies Research Center (CTRC)
  • Computer Science and Mathematics Division
  • Pioneering research in
  • Agent technology
  • Information integration
  • Cluster analysis
  • Software engineering
  • We have successfully developed systems for
    Lockheed Martin, the Department of Energy, and
    the Defense Logistics Agency.
  • Approach
  • Small Entrepreneurial team of researchers and
    software developers
  • Broad range of collaborators, including
  • LM, DLA, DOE, University of Tenn, NIST, CMU,
    NCSU, NTRC

3
CTRC Key Projects
  • SURGE - Supplier Utilization through Responsive
    Grouped Enterprises
  • DLA funded to drastically reduce cost/delivery
    time for military spares
  • Software agents and grouping technology used to
    define part families
  • MABES - Manufacturing Agent Based Emulation
    System
  • LMTAS to rapidly model fundamental changes to
    manufacturing systems
  • Software agents to analyze the impact of changes
    to manufacturing lines
  • CME - Collaborative Management Environment
  • DOE funded to provide significant improvement in
    research funding
  • Information integration used to gather, search
    over, and report on heterogeneous information
    from a number of national laboratories

4
Future Technology Trend
AGENTS
Internet
Telephone
Face to Face
5
Successful Projects
Supply Chain Management Agent System
  • We have extensive expertise in agent development
  • Began working with agent technologies in 1980s

Manufacturing Emulation Agent System
Collaborative Decision Support System
Neural Nets for Recovery Boiler Control
Neural Nets for Bankruptcy Prediction
Neural Nets for Spring-back Prediction
Collaborative Design System
Neural Nets for Resistance. Spot Welding
Neural Nets for Material Mix Optimization
Genetic Algorithms for Chemical Synthesis
  • Over 10 successful projects within the last 5
    years
  • Collaborations with leading agent experts

Knowledge-based Systems - Manufacturing Advisors
Knowledge-based Systems for Constructability
Design and Analysis of Computer Experiments
Knowledge-based Computer Systems Calibration
1985
1990
1995
2000
6
Recent Accomplishments
  • Guest researcher at NIST for standardization of
    agent frameworks
  • Delivered a multi-agent part grouping system for
    DLA
  • Press Release Lockheed Martin Completes First
    Phase in Applying 'Agent-Based' Software to JSF
  • Papers and Presentations
  • Invited presentation to MITs Lean Aerospace
    Initiative Forum
  • An invited paper to the IEEE Internet Computing
    Journal
  • Presented two multi-agent papers at the ISAS'99
    conference
  • Paper accepted by International Journal of
    Flexible Automation and Integrated Manufacturing.
  • Paper accepted by Flexible Automation and
    Intelligent Manufacturing Conference

7
What are Agents?
...Software entities that assist people and act
on their behalf ...Software robots
Proactive detect changes in their environment and
react to those in a timely manner by answering
to events and initiating actions
Traditional Software Object
Goal-driven have a purpose and act in accordance
with that purpose until it is fulfilled
Behavior
State
Agent Technology
Communicative able to interact and communicate
with users and other agents
Autonomous can have control over their own
actions and be able to work and launch actions
independent of the user or other actors
Learning have the ability to learn from
experiences in their environment
8
Simple Agent Example
Agent, find me the book War and Peace, and I
need it tomorrow
Amazon
2 Days 18.50
Barnes and Nobel
1 Day 21.75
Borders
NA
...
Does the agent understand buying books?
Form a plan to buy the book
Execute the plan
Order the book
B. Dalton
1 Day 20.25
Learn for next time
9
Oak Ridge Technology
SURGE Optimize Logistics Minimize wait time
MABES Total Asset Visibility Supply Chain Model
CME Integrated, Collaborative, Distributed
Information
10
SURGE
11
Supply Chain Overview
How is it made? What can the supplier build? When
and how much is needed?
History
Future
Demand
Part
Optimal Part Family
Smooth Demand
Optimal Supplier
Supplier Capability
12
SURGE Grouping Agents
Part Families
Agent Group Themselves
Agent Mediator
Parts represented by Agents
13
Results on C-130 Parts
Input Data
Grouping Results
Group 1
Group 5
Group 2
Group 4
Group 3
14
C130 Grouping Results
Why two groups?
Common Processes
Group 3 Processes
Group 5 Processes
Why are extrusions spread over two groups?
15
Results
Cell 1
Group 2
Group 1
Group 2
Group 3
Cell 2
Group 1
Group 3
Cell 3
Wire Harness Data forms 3 groups
Possible cell layout
16
SURGE 1st Phase Results
  • Investment
  • Initial investment 3.3M
  • Agent investment 812K of the 3.3M
  • 1st phase duration of 9 months
  • Return
  • 7,832 of 130K spare parts grouped, 4400 Parts
    Bid
  • 7.0M savings in Inventory Reduction (30)
  • 5.8M Savings in reduced pricing (23)
  • 58 Reduction in lead times (from 220 to 93 days)
  • Total
  • 12.8M in savings
  • Significant reduction in lead times

Total Savings
Agent Investment

17
Forecasting Direction
Part family with key supplier
Traditional low bid part
Small Variance
Large Variance
Higher Volumes, Reduced Inventory Potential for
large savings
18
Neural Network Forecasting
Partial Training Set
Forecast
Full Training Set
19
SURGE Summary
  • An advanced logistics optimization system
  • Significant research breakthroughs in clustering
    technology
  • Provides significant savings and lead-time
    reductions to DLA

20
MABES
21
Process Overview
Projected or Actual Parts Need
Group Parts
Form Lean Cells
Optimize Cells
22
Traditional Methods Consume Time and Effort
Cell Optimization
Experts
Management
Technology
Foreman
Information
Weeks or Months
23
Value of Agent Systems
Fast Flexible Collaborative
Experts
Management
Agent System
Technology
Foreman
Minutes
24
MABES Analytic Model
Minimization
Manufacturing Rules
Throughput
At 17 Planes Per month - Machine utilization
85 - Cost is 65M - Span time is 48 Days
Activity Metrics
Process Metrics
25
MABES Dynamic Model

Queues and Task Centers
  • Identify
  • Bottlenecks
  • Network Sensitivity
  • Outages

Pull/Push/Takt Animation
26
Visibility into the Supply Chain
Manufacturing Operations
Theater Operations
Supply Chain
PARTNER
SUPPLIER
SUPPLIER
SISTER DIVISION
3rd TIER SUPPLIER
2nd TIER SUPPLIER
PARTNER
FABRICATION - SUBASSEMBLY - FINAL - DELIVERY
1st TIER SUPPLIER
How does a problem here affect operations here
27
MABES Summary
  • An advanced supply chain decision support system
  • Provides asset visibility into the logistics
    supply chain
  • Two patents filed on this technology
  • Deployed on the F-16 manufacturing line

28
The Collaborative Management Environment
Ames Lab Berkeley Lab Fermi Lab Los Alamos
Lab Sandia Lab Livermore Lab Oak Ridge Lab
Database Management Human Computer
Interaction Meta-modeling Languages Object-oriente
d Technologies Scalable Algorithms Security
Collaborative Management Environment
Information Integration XML
Software Engineering
29
Current situation
  • Problem
  • Field Work Proposals (FWPs) submitted to DOE are
    in paper books
  • Weeks and thousands of dollars are spend in
    collating, copying, binding, and shipping these
    books
  • The books provide very limited query and search
    capability
  • Approach
  • Developed an FWP ontology for several national
    laboratories
  • Pioneered use of the Extended Markup Language
    (XML) as a means of storing, querying, and
    presenting FWP information.
  • Simple data storage technology
  • Very low costs to the labs, integration work done
    by CME team
  • Very well received article at XML98, InForum99,
    InterLab99.

30
Sample FWP
31
Same XML Data Type Definition
Tag Definitions
Tagged Document
32
CME System
33
CME Summary
  • One common picture to DOE
  • Integrated, collaborative, and distributed
    information in a secure web-based environment
  • Innovation
  • Use of Extended Markup Language (XML) for
    low-cost information integration
  • Staged Schema migration CME information model
  • DOE evaluating CME to be a corporate system

34
Joint Theater Logistics Management
SURGE Optimize Logistics Minimize wait time
MABES Total Asset Visibility Supply Chain Model
CME Integrated, Collaborative, Distributed
Information
35
Summary
  • We have expertise and experience with developing
  • Advanced Logistics systems
  • Collaborative decision support systems
  • We are pioneering in agent and information
    integration technologies
  • We can help transform joint theater logistics
    management to a real-time logistics information
    system
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