August 31, 2004 2:30 CELDi Overview 3:00 Technical Review – Dr. Heather Nachtmann

1. Center for Engineering Logistics and Distribution (CELDi) CELDi August 31, 2004 2:30 CELDi Overview 3:00 Technical Review – Dr. Heather Nachtmann 4:30 Tour Industrial Engineering facilities 6:00 Dinner at Ella’s Restaurant September 1, 2004 - Technical Review 8:30am Dr. Ed Pohl 9:00 Dr. Steve Johnson 9:30 Dr. C.S. Nam 10:30 Dr. Manuel Rossetti 11:00 Dr. Richard Cassady 11:30 Mr. Thomas Yeung

2. CELDi Center for Engineering Logistics and Distribution (CELDi) A National Science Foundation sponsored Industry/University Cooperative Research Center (I/UCRC) • TheCenter for Engineering Logistics and Distribution (CELDi)is a multi-university, multi-disciplinary National Science Foundation sponsored Industry/University Cooperative Research Center (I/UCRC). Research endeavors are driven and sponsored by representatives from a broad range of member organizations, including manufacturing, maintenance, distribution, transportation, information technology, and consulting. CELDi emerged in 2001 from The Logistics Institute (1994) to provide integrated solutions to logistics problems, through research related to modeling, analysis and intelligent-systems technologies. • Research Program • The Center helps industry partners excel by leveraging their supply chain to achieve a distinguishable, sustainable difference. Through basic research, collaborative applied research with industry, technology transfer, and education, the Center is a catalyst for developing the engineering logistics methodology necessary for logistics value chain optimization. • Value-adding processes that create time and place utility (transportation, material handling, and distribution) • Value-sustaining processes that prolong useful life (maintenance, repair, and rework) • Value-recovering processes that conserve scarce resources and enhance societal goodwill (returns, refurbishment, and recycling) • Academic Partners • University of Arkansas, *Center Headquarters • University of Oklahoma • University of Louisville • Oklahoma State University • Lehigh University • University of Florida

3. CELDi CELDi Overview • Center research provides creative, leading edge solutions to real-world problems • Sponsors collaborate with research teams • Benefit from shared research solutions • Employment of a systems perspective and an engineering approach

4. 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 CoreCompetencies • Techniques • OR/Simulation/Optimization • Supportability • Management • Information Technology • Sensors and Communications • Human Factors/Ergonomics • Robotics/Automation • Quality/Metrics • Probability/Statistics

5. DoD Related Logistics Partners • Defense Logistics Agency • Naval Supply Command Systems • Pine Bluff Arsenal • Raytheon Systems Company • Red River Army Depot

6. Commercial Logistics Partners • ABF Freight System Inc. • AT&T • Avaya Inc. • Cargill Inc. • Cobb-Vantress • ConAgra Foods • E & J Gallo • Global Concepts Inc. • Hytrol Conveyor Co. • Tyson Foods, Inc. • Wal-Mart Stores, Inc. • United States Postal Service • Yellow Freight System

7. Sample Projects Sponsor: Defense Logistics Agency Title: Inventory Integrity Modeling and Benchmarking Researchers: Terry R. Collins TLI 1999-2000 Title: The Analysis of Inter-modal Choice Combinations and Pre-Positioning Strategies for Military Supplies and Materiel Researchers: Manuel D. Rossetti, Erhan Kutanoglu, Terry Collins, Nancy Sloan, Yeu-San Tee, and Mee-Ching Chow Project #: TLI-AR 00-2 Title: Stock Positioning for Personal Equipment and Spare Parts Inventory in Military Logistical Systems Researchers: Manuel D. Rossetti, Erhan Kutanoglu, Nancy Sloan, Yeu-San Tee Project #: UA 01-05b Title: DLA-DDC Analysis of Military Processing Center Operations Researchers: Manuel D. Rossetti Project #: UA 03-DLA

8. Sample Projects Sponsor: Naval Supply Systems Command Title: Readiness Based Customer Wait Time Sparing within Manugistics Advanced Planning System Researchers: Manuel D. Rossetti Project #: UA 03-NAVSUP Title: Evaluating Intermittent Spare Parts Forecasting Techniques in Military Supply Chains Researchers: Manuel D. Rossetti Project #: UA04-NAVSUP

9. Sample Projects Sponsor: Pine Bluff Arsenal Title: Decision Support System for the Pine Bluff Arsenal Researchers: Joshua Buchanan, Cammie Harp, John English, G. Don Taylor Project #: TLI-AR 97-5 Title: Model Development of a Total Integrated Maintenance System Researchers: Julia A. Watson, Earnest W. Fant, and Mia Petre-Ungerank Project #: TLI-AR 00-3a Title: Recommended Process Design Changes; Identification and Resolution of Materials Management Software Tracking Problems and Inventory Reconciliation Researchers: Manuel D. Rossetti, Andres Angulo, Geoffrey Skinner, and Ravi H. Kurgund Project #: TLI-AR 00-3b Title: Development of Productivity-Based Selective Maintenance Strategies Researchers: Earnest W. Fant, C. Richard Cassady, Julia A. Watson, Kellie Schneider, Pingjian Yu Project #: TLI-AR 01-03 Title: Grenade Line Reliability and Maintainability Performance Researchers: C. Richard Cassady Project #: UA 02-04 Title: Incorporating Lean Thinking into Grenade Line Maintenance Researchers: C. Richard Cassady Project #: UA 03-PBA

10. Resulting DoD Collaboration within I/UCRC

11. Federal Appropriation: Background • Based on research experience and collaboration with current/past I/UCRC members • Pursued multi-year appropriation to support military logistics research • History: • Congressional office visits • Beginning in mid-90’s • UA representatives • Chancellor, Provost, John English, Rick Malstrom, Van Scoyoc Associates, Inc. • Resulting in sufficient support for appropriation request

12. AFRL Effort • Military Logistics • 2002 Appropriation 1 - $1M • 2003 Appropriation 2 - $1M • 2004 Appropriation 3 - $1M • 2005 Proposed Appropriation - $1M • Movement of Funds: 1st Year • DoD Appropriation: Approved January 2002 • Pentagon: Found good fit in AFRL • Human Effectiveness Directorate in AFRL • Subsequent Appropriations • Direct to AFRL • Year 3: MIPR to NSF • Year 4: Anticipated MIPR to NSF

13. Programmatic Areas • Statistical Methods and Modeling (SMM): UA is developing new statistical methods needed to drive novel statistical thinking as excessively large or very small data sets are identified in relationship to resource management and optimization of complex communication and logistics networks. • Battlefield Simulation and Intelligent Tracking (BSIT): UA is exploiting the power of computer simulation, fuzzy modeling and fuzzy inference to support DoD’s needs for computationally efficient approaches for battlefield simulation and intelligent tactical and logistical information systems. • Advance Technology Applications (ATA): UA is utilizing technologies, such as smart chip/card technologies, data mining of large logistics data sets, data visualization concepts and tools, and site planning and scheduling capabilities, to develop, identify, and deploy information technology into the management of logistics operations. Furthermore, these applications will dramatically improve world-wide command and control of logistics management. • Mathematical Modeling (MM): To accurately model the complex interaction of humans and machines common to Air Force transportation systems, manufacturing, command and control systems, and battlefield management, UA is developing new mathematical models that have the flexibility to handle incomplete, conflicting and overlapping criteria. These models will reflect the total life cycle to aid in optimal repair/replace decisions for weapons systems by providing more accurate methods of system diagnosis and prediction of component failures. This will support the Air Force’s ability to meet Air Expeditionary Force requirements. • Performance Measurement Development (PMD): We are improving the integration of the Department’s strategic plan to mission outcomes by creating holistic qualitative and quantitative goals and measures for the Department’s annual performance report. Such improvements will provide immeasurable improved efficiency to the Department. UA is uniquely qualified to support DoD in designing an optimal performance measurement system that integrates high-level objectives with operational and tactical metrics that will drive new decision support aids and support real-time process tracking mechanisms.

14. Year 1Delivery Order #23 UA03-AFRL Task 1-7 MM0206 Quantifying the Impact of Aircraft Cannibalization PMD0204 Decision Models in Collaborative Integrated Solutions System Development MM0202 Fleet-Level Selective Maintenance and Aircraft Scheduling BSIT0204 Multi-Mission Selective Maintenance Decisions MM0205 Quantifying the Effect of Commercial Transportation Practices in Military Supply Chains BSIT0201 Hybrid Simulation/Analytic Models for Military Supply Chain Performance Analysis ATA0201 Commercial Practices as Applied to Total Asset Visibility (TAV)

15. Quantifying the Impact of Aircraft Cannibalization Task 1 - MM0206 Principal Investigator: C. Richard Cassady, Ph.D., P.E. Co-Principal Investigators: Scott J. Mason, Ph.D., P.E. and Justin R. Chimka, Ph.D. Graduate Research Assistants: Kellie Schneider and Stephen Ormon Undergraduate Research Assistants: Chase Rainwater, Mauricio Carrasco, Jason Honeycutt • Project objectives • to develop a mathematical modeling methodology for assessing the impact of cannibalization on fleet performance • to identify policies for making cost-effective, dynamic cannibalization decisions • to study the impact of these policies on management of the spare parts supply chain • Current activities • generic scenario definition • generic simulation modeling • application • future work ASC PA 03-2420 9/15/03

16. Decision Models in Collaborative Integrated Solutions System Development Task 2 - PMD-0204 Problem objective • To formulate a balanced scorecard (BSC) to identify a set of mission critical performance measures that will not only evaluate existing balanced scorecard perspectives, but will investigate other metrics related to flightline scheduling activities. Current activities • Identify Flightline Activities • Balanced Scorecard Development • Validation • Reporting Principal Investigator: Heather Nachtmann, Ph.D. Research Assistants: Bryan Hill, Justin Hunter, David Rieske ASC PA 03-2419 9/15/03

17. Fleet-Level Selective Maintenance and Aircraft Scheduling Task 3 - MM0202 Project objective • to investigate the use of a mathematical modeling methodology for managing the dynamic maintenance planning and sortie scheduling issues Activities • define system structure • static scenario formulation • static scenario solution and analysis • dynamic scenario formulation • dynamic scenario solution and analysis Status Project Complete Principal Investigator: C. Richard Cassady, Ph.D., P.E. Co-Principal Investigators: Scott J. Mason, Ph.D., P.E. and Justin R. Chimka, Ph.D. Graduate Research Assistants: Kellie Schneider, Stephen Ormon, Undergraduate Research Assistants: Chase Rainwater, Mauricio Carrasco, Jason Honeycutt ASC PA 03-2422 9/12/03

18. Multi-Mission Selective Maintenance DecisionsTask 4 - BSIT0204 Project objective • to develop a modeling-based methodology for managing selective maintenance decisions when the planning horizon is more than one future mission Current activities • system definition • multi-mission selective maintenance modeling • multi-mission solution procedures • numerical analysis • documentation Principal Investigator: C. Richard Cassady, Ph.D., P.E. Co-Principal Investigator: Heather Nachtmann, Ph.D. Graduate Research Assistants: Kellie Schneider, Alejandro Mendoza Undergraduate Research Assistants: Chase Rainwater and Mauricio Carrasco ASC PA 03-2423 9/12/03

19. Quantifying the Effect of Commercial Transportation Practices in Military Supply ChainsTask 5 - MM0205 Project description • Identify applicable successful commercial transportation practices such as direct shipments, lateral shipments, scheduled deliveries, express deliveries, etc. • To quantify the effects commercial transportation practices on military supply chains • Primarily examining the depot/base repair, inventory, and transportation processes for unscheduled maintenance actions for multi-indentured weapon systems Targeted benefits • Strategic policy recommendations concerning which commercial practices to adopt to improve aircraft availability and reduce cost • Simulation model(s) of examined processes • Project report Principal Investigator: Manuel D. Rossetti, Ph. D., P.E. Co-Principal Investigator: Scott J. Mason, Ph. D., P.E. Undergraduate Research Assistants: Ashlea Bennett, Ryan Houx, Mohsen Manesh, Josh McGee, David Boguslawski ASC PA 03-2417 9/15/03

20. Hybrid Simulation/Analytic Models for Military Supply Chain Performance AnalysisTask 6 - BSIT0201 Project Description • Hybrid models combine simulation and analytic models, improving computational efficiency • Use hybrid simulation/analytic models to efficiently predict the resulting performance of various logistical planning scenarios • Check the predicted results for accuracy and precision Goal To examine the feasibility of utilizing hybrid simulation/analytic techniques within logistical performance analysis in order to speed up the execution of logistical planning while maintaining the accuracy and precision of the performance predictions. Principal Investigator: Manuel D. Rossetti, Ph. D., P.E. Undergraduate Research Assistants: Ashlea Bennett, Ryan Houx, Mohsen Manesh, Josh McGee, David Boguslawski ASC PA 03-2418 9/15/03

21. Commercial Practices as Applied to Total Asset Visibility (TAV)TASK 7 - ATA0201 Project Description The Air Force requires the ability to track all materiel in an efficient and timely manner. Because of current limitations in the tracking of palletized items, total asset visibility, as is common in the commercial industry, is not available to the Air Force Corporations such as Wal-Mart, Tyson Foods, and Federal Express have the ability to track widely distributed assets on a real-time basis. Principal Investigator: Scott J. Mason, PhD, PE (UA) Co-Principal Investigators: Justin Chimka, PhD (UA), Major Michael Greiner, PhD, USAF (AFIT) Graduate Assistant: Thomas Yeung, BSIE (UA) ASC PA 03-2421 9/15/03

22. Year 2Delivery Order #26 UA04-AFRL Task 1-5 BSIT0301 Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics Planners SMM0301 Maintenance Decision-Making under Prognostic and Diagnostic Uncertainty MM0303 Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities MM0302 Multi-State Selective Maintenance Decisions PMD0302 Quantification of Logistics Capabilities

23. Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics PlannersTask 1 - BSIT0301 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 ASC PA 03-2409 9/12/03

24. Maintenance Decision-Making under Prognostic and Diagnostic Uncertainty Task 2 - SMM0301 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 ASC PA 03-2409 9/12/03

25. Quantifying the Impacts of Improvements to Prognostic and Diagnostic Capabilities Task 3 - MM0303 • 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 ASC PA 03-2409 9/12/03

26. Multi-State Selective Maintenance Decisions Task 4 - MM0302 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 ASC PA 03-2409 9/12/03

27. Quantification of Logistics Capabilities Task 5 - PMD0302 • 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. ASC PA 03-2409 9/12/03

28. Year 3 UA-AFRL05 Task 1-6 Task 1-AFRL 2005 Simulating Technology Improvements for Maintenance Excellence (TIME) Task 2-AFRL 2015 Comprehensive Selective Maintenance Decision-Making in an Autonomous Environment Task 3-AFRL 2025 C/KC-135 Weapon System Stockage Policy Analysis Task 4-AFRL 2045 Human-centric Mobile Information Technology in Air Force Logistics Task 5-AFRL 2065 Cognitive Modeling of Group Decision Behaviors in Multi- cultural Contexts Task 6-AFRL 2075 Maintenance Prognostics Decision Aiding

29. Simulating Technology Improvements for Maintenance Excellence (TIME) • Investigator: Dr. Rossetti • Goals & Objectives • To research mechanisms for (1) the evaluation of automatic data collection system’s benefits and costs within a logistics environment, and (2) to provide decision support technologies for the integration of the automatic data collection system with the simulation planning mechanisms. • Planned Approach • Expand upon current work to provide a prototype object-oriented simulation of the flight and maintenance operations at a typical air base • Investigate using object-oriented analysis and design techniques how to model automatic data collection systems within the system • Anticipated Outcomes • Flexible modeling tools for simulating the effect of automated sensor technology on flight, maintenance, and supply operations • New simulation techniques for control and system evaluation

30. Comprehensive Selective Maintenance Decision-Making in an Autonomous Environment • Investigators: Dr. Cassady and Dr. Pohl • Goals & Objectives • To develop a comprehensive multi-system, multi-mission selective maintenance model that incorporates new information expected to be available to aid in the decision-making process and other important concepts such as component aging, imperfect maintenance, cannibalization and workforce shaping • Planned Approach • Modify the existing selective maintenance models through a sequence of extensions • Define an approach for solving these newly-formulated selective maintenance problems • Review the AMIT and Smart Systems program in order to understand what elements of information will be available to logistic planners as they allocate their resources • Test our solution approaches via extensive numerical experimentation • Anticipated Outcomes • Suite of models and solution approaches for making effective selective maintenance decisions

31. C/KC-135 Weapon System Stockage Policy Analysis • Investigator: Dr. Pohl, Dr. Chimka, Dr. Rosetti, Dr. Mason • Goals & Objectives • Explore the demand pattern for the 66% of MICAP items with Cause Code A/B across the fleet and investigate the local stockage policies for the 38 different CONUS bases. • Develop a low intermittent variable demand forecasting model that considers the various mission design series of the aircraft, the variability in the number of aircraft assigned at each location as well as the proximity of each location. • Perform a marginal analysis that explores the expected gain in weapon system availability given the funding level to stock the additional parts recommended by the forecasting model. • Planned Approach • Review demand patterns for MICAP data across the fleet. • Review Local stockage policies for the different CONUS bases. • Search the current forecasting literature for intermittent demand forecasting techniques appropriate to this specific problem domain and explore their effectiveness for use on the C/KC-135 fleet. • Recommend changes using new forecasting algorithms. Analyze the costs associated with the recommended changes in the stockage policy against the expected gains in Weapon System Availability. • Anticipated Outcomes • Flexible and consistent forecasting model for C/KC-135 parts stockage analysis. • Economic options for fleet support and service life extension.

32. Human-centric Mobile Information Technology in Air Force Logistics • Investigator: Dr. Johnson • Goals & Objectives To establish how current and future advances in on-board and off-board information systems can be most effectively utilized by the various team members that will be making coordinated decisions using real-time, mobile information technology (IT) (manufacturer, carrier, dispatcher, truck driver, Air Force customer). • Planned Approach The project will be conducted in four phases: 1) determine the “best practices” of commercial freight transport organizations that are using mobile IT systems, 2) conduct an analysis of current and future usability requirements for on board and off-board systems, 3) conduct an analysis of Air Force logistics operations that will increasingly use mobile IT systems, and 4) determine the changes in skill requirements and training that will be necessary for Air Force personnel to fully capitalize on the capabilities of advanced mobile information technology. • Anticipated Outcomes The project will document the current and future status of the systems from a human factors perspective, where there are gaps in the knowledge base, and what areas will need to be addressed in the near future by the research and development communities.

33. Cognitive Modeling of Group Decision Behaviors in Multi-cultural Contexts • Investigator: Dr. Nam • Goals & Objectives • To develop a culture-based group decision behavior model, capturing design guidelines and user requirements to develop culture-centered group decision support tools and interactive computer systems for logistics decision-makers. • Planned Approach • Development of descriptive case studies • Design of culturally relevant pedagogy and tools • Empirical experiments • Design guidelines and user requirements • Anticipated Outcomes • Increased our understanding of group dynamics and diverse decision behavior patterns in multi-culture contexts • Culturally relevant pedagogy and tools that facilitate cross-cultural interactions • Evaluation of the effectiveness of existing group collaboration technologies and training system developed in this research

34. Maintenance Prognostics Decision Aiding • Investigators: Dr. Nachtmann, Dr. Rossetti, Dr. Chimka • Goals & Objectives • To explore the human interface of equipment prognostics and make system improvements through (1) increased understanding of technician involvement in equipment prognostics and (2) improved prognostic decisions through enhanced technician empowerment. • Planned Approach • Examine other prognostic environments such as medicine and meteorology for possible adaptation to equipment prognostics • Explore the current human interaction within equipment prognostics through observation of MX technicians in prognostic decisions of hypothetical equipment prognostic cases in a controlled manner • Using information gathered from observations, study how to improve the equipment prognostics system from the viewpoint of the technician user • Anticipated Outcomes • Understand the role of the technician in the prognostic decision-making process • Provide systems improvement analyses and recommendations to enhance technician empowerment to improve prognostic predictions

35. CELDi Year 4 • June 2004 • Notification of $1M Funding from Department of Defense for The Logistics Institute/Center for Engineering Logistics and Distribution

36. CELDi