BATTLESPACE LOGISTICS READINESS & SUSTAINMENT RESEARCH TRS Delivery Order #26 Kickoff Meeting

1. BATTLESPACE LOGISTICS READINESS & SUSTAINMENT RESEARCH TRS Delivery Order #26 Kickoff Meeting 29 September 2003 Mr. Jerry Baker AFRL/HESR Mr. John Jacobs Northrop Grumman IT Dr. John English University of Arkansas

2. AGENDA • Introduction of the Team • Technical Approach • Summary of Benefits • Risks • Travel • Programmatics • Questions

3. CELDi The Team • Northrop Grumman IT, Advanced Systems Division • John Jacobs, David Snyder • TLI/CELDi, University of Arkansas • John English • C. Richard Cassady • Manuel Rossetti • Heather Nachtmann • Ed Pohl • Justin Chimka • Scott Mason • Ray Hill (WSU)

4. Military Logistics University of Arkansas Mission The vision for the center is to provide integrated solutions to logistics problems, through modeling, analysis and intelligent systems technologies

5. Strategic Areas Supply Chain Management Supply Chain Design Material Handling/Shop Floor Logistics Warehousing & Inventory Control Reverse Logistics Transportation Intermodal operations Intelligent systems Vehicle/Bridge ITS Data mining/fusion Trucking Waterways Enterprise Performance Scalable Systems CELDi Core Competencies • Techniques • OR/Simulation/Optimization • Supportability • Management • Information Technology • Sensors and Communications • Human Factors/Ergonomics • Robotics/Automation • Quality/Metrics

6. CELDi Research Focus Areas • Battlefield Simulation and Intelligent Tracking • BSIT0301 Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics Planners • Statistical Methods and Modeling • SMM0301Maintenance Decision-Making under Prognostic and Diagnostic Uncertainty • MathematicalModeling • MM0303 Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities • MM0302 Multi-State Selective Maintenance Decisions • Performance Measurement Development • PMD0302 Quantification of Logistics Capabilities • Advance Technology Applications • no current projects

7. TECHNICAL APPROACH

8. BSIT0301 Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics Planners One of the primary performance measures for US Air Force fighter wing logistics organizations is the ability to successfully launch aircraft on time and in the proper configuration. The goal of this project is to develop simulation and mathematical modeling methodologies that will assist logistics managers in analyzing the effects of different resource allocation policies and identify potential risks in logistics plans. • PI: Manuel D. Rossetti • Co-PI: Raymond R. Hill, WSU and Dr. Narayanan

9. BSIT0301 Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics Planners • Tasks: • Logistics planning review and documentation (taking advantage of prior work in this area) • Compilation of compendium of logistics models with a focus on planning tasks • Development of sample scenario(s) in which to focus planning/re-planning work • Specification of planning/re-planning tool(s) • Prototype algorithm(s) and user interface • Functional use testing and system refinement • System specification and delivery. Process descriptions will likely be built to specify the functional tasks within the planning/re-planning process. Too long the focus has been on the tools versus tools that support the user. Coupled with the process flow descriptions will be an examination of the viability of legacy planning approaches. It is anticipated that a hybrid approach will be required, one combining features of legacy approaches with new, advanced methods for planning using simulation and optimization.

10. SMM0301 Maintenance Decision-Making under Prognostic and Diagnostic Uncertainty A key challenge faced by USAF maintenance personnel is the uncertainty associated with the information provided by prognostic and diagnostic tools. This uncertainty makes it difficult for maintenance technicians to choose an appropriate course of action. This can potentially cause omission of necessary maintenance actions, performance of unnecessary tasks, and additional delays in returning aircraft to operational status. The goal is to develop a methodology based on mathematical modeling that can be used to provide a more reliable recommendation to the technician. • PI: C. Richard Cassady • Co-PI: Heather Nachtmann and Ed Pohl

11. SMM0301 Maintenance Decision-Making under Prognostic and Diagnostic Uncertainty • Tasks: • Define the system structure and the reliability and maintainability characteristics of each component in the system. • Identify the characteristics of the prognostic and diagnostic tools applied to the system. This identification will include a description of the precision and accuracy in prognostic and diagnostic information. • Develop a mathematical model which synthesizes the prognostic and diagnostic information and provides an estimated assessment of the system. This model will be based primarily in probability theory. However, it will also include learning capability so that patterns in prognostic and diagnostic information can be identified and the underlying probability model can be updated based on the results of the corresponding maintenance actions. • Utilize maintenance history from an actual USAF fleet to assess the potential for this methodology for incorporation into a decision-support environment.

12. MM0303 Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities • The objective is to develop a methodology based in mathematical modeling for analyzing the impacts of improvements to prognostic/diagnostic capabilities. • What impact do prognostic and diagnostic errors have on fleet readiness and the associated requirements for spare parts? • Given a specific investment in prognostic and diagnostic improvements, what will the impact be on fleet readiness and spare parts inventory measures? • Given a limited budget for prognostic and diagnostic improvements, how should the funds be allocated to optimize fleet readiness and spare parts inventory measures? • PI: C. Richard Cassady • Co-PI: Ed Pohl

13. MM0303 Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities • Tasks: • Define the system structure and the reliability and maintainability characteristics of each component in the system. • Identify the characteristics of the prognostic and diagnostic tools applied to the system. This identification will include a precise description of the potential for imperfections in prognostic and diagnostic information. • Develop a mathematical model which can be used to measure the performance of the fleet with and without prognostic and diagnostic errors. This will permit the assessment of the impact of prognostic and diagnostic imperfections on fleet readiness and spare parts inventory investment. • Use this model to explore the impact of specific investments in prognostic and diagnostic tools. This will permit evaluation of the cost-effectiveness of potential prognostic and diagnostic improvement actions. • Incorporate this model into a decision-support environment that is designed to allocate investments in prognostic and diagnostic improvements. This will permit the decision-maker to fund cost-effective prognostic and diagnostic improvement efforts that optimize fleet performance.

14. MM0302 Multi-State Selective Maintenance Decisions All military organizations depend on the reliable performance of repairable systems for the successful completion of operational missions. Maintenance cannot be performed during missions; therefore, the decision-maker must decide which systems to repair prior to the next mission. The primary objective of this project is to develop multi-state selective maintenance models that incorporate multi-state component status and multiple measures of system performance. • PI: C. Richard Cassady • Co-PI: Scott Mason and Ed Pohl

15. MM0302 Multi-State Selective Maintenance Decisions • Tasks: • Define the system structure and appropriate status measures for each component in the system. • Identify the resources consumed by maintenance actions, the impact on component status of each potential maintenance action, and the quantity of each resource consumed by each maintenance action. • Identify the relevant measures of mission performance and develop functions which capture these measures in terms of the component status values. • Develop several alternative mathematical formulations of the selective maintenance problems. We will consider multi-objective formulations, as well as single-objective formulations where the additional performance measures are used as constraints. • Develop solution procedures for solving the selective maintenance problems. We will define enumerative solution strategies for smaller problems and search-based heuristic strategies for larger problems.

16. PMD0302 Quantification of Logistics Capabilities • PI: Heather Nachtmann • Co-PI: Manuel D. Rossetti and Justin R. Chimka The project objective is to provide the groundwork for an established and accepted system of measurement that assigns value to logistics capabilities based upon each capability’s contribution to Air Force operational effectiveness. Specifically: (1) Develop a common language to describe logistics system requirements; (2) Enable logistics requirements to compete more equally with system hardware and operational requirements in the acquisition process; and (3) Improve operational effectiveness through enhanced logistics capability.

17. PMD0302 Quantification of Logistics Capabilities Tasks: 1) Select USAF system for study 2) Review value focused thinking techniques 3) Data acquisition 4) Development of value focused algorithm/business rules 5) Development of acceptance plan 6) Technology transfer.

18. SUMMARY OF BENEFITS

19. Summary of Benefits • BSIT0301 Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics Planners At the end of this project, the AFRL will have value-stream mapping of the current sortie generation processes used to launch aircraft for daily Air Tasking Orders. The research will identify process improvement areas for this process and develop recommendations for enhancing or automating the process for unit level logistics managers. Finally, the project will provide mathematical and simulation models to assist unit level managers during the sortie generation process. These models can be used by AFRL to further develop integrated software solutions for improving the efficiency of these processes.

20. Summary of Benefits • SMM0301 Maintenance Decision-Making under Prognostic and Diagnostic Uncertainty This project will result in a methodology based on mathematical modeling that can be used to synthesize prognostic and diagnostic information and provide a recommended course of action to technicians. This methodology potentially could be incorporated into a decision-support tool for technicians.

21. Summary of Benefits • MM0303 Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities The project is will result in a methodology based in mathematical modeling for analyzing the impact of prognostic and diagnostic errors/improvements on fleet readiness and the associated requirements for spare parts investment. In addition, the project will provide results of the initial investigation into optimal allocation of funds for prognostic and diagnostic improvements.

22. Summary of Benefits • MM0302 Multi-State Selective Maintenance Decisions This project will result in a set of multi-state selective maintenance models that incorporate multi-state component status and multiple measures of system performance. These models will provide a more realistic portrayal of the maintenance resource allocation process.

23. Summary of Benefits • PMD0302 Quantification of Logistics Capabilities • The research conducted in this project will lead to: • (1) Improved capability of logistics requirements to compete with system hardware and operational requirements in the acquisition process • (2) Common language to describe logistic system requirements • (3) Improved operational effectiveness through enhanced logistics capability

24. POTENTIAL RISKS

25. Potential Risks • RISK: Access to Necessary Data --Quality Research Requires Timely Access to Good Data --Even Small Delays can Suboptomize the Project --Logistics Data Notoriously Hard to Acquire • ABATEMENT: Early Identification of Requirements --Priority for PIs to Determine Needed Data Early in Projects --Emphasize Planning for Effective Data Gathering Visits --Identify Data Shortfalls to Lab Quickly

26. TRAVEL

27. Travel • AR to WPAFB Dayton, OH • Kick-off, Interim Review, Final Program Review • Potential trips for data/information collection • AR to WPAFB, Dayton, OH • AR to Little Rock AFB, Little Rock, AR • AR to Mountain Home AFB, ID • AR to Grand Forks AFB, ND • AR to Hill AFB, Ogden, UT • AR to Eglin AFB, Ft. Walton Beach, FL • Invitation to AFRL and NGIT • CELDi Conferences • 14-15 October 2003 • 12-13 April 2004 Further travel requirements may be performed within contract funding.

28. PROGRAMMATICS

29. ProgrammaticsSchedule of Milestones

30. ProgrammaticsSchedule of Milestones

31. ProgrammaticsMan-Hours and Types TypeHours Principal Investigator 1110 Co-PI 1722 Senior Logistics Analyst 591 Research Assistant 6545 Undergraduate Res. Asst. 4778 Administrator 207

32. ProgrammaticsDeliverables TypeSubmit Status Report Monthly Funds and Man-Hour Rpt Monthly Presentation Material As Required Technical Report (Draft) 29 November 2004 Technical Report (Final) 24 January 2005 Contractor Billing Voucher Monthly

33. Contact Information

34. QUESTIONS?