Center for Engineering Logistics and Distribution (CELDi)

1. Center for Engineering Logistics and Distribution (CELDi) National Science Foundation sponsored Industry/University Cooperative Research Center (I/UCRC) Welcome to the CELDi Research Conference April 2005 hosted by Oklahoma State University

2. Welcome Dr. Stephen McKeever Vice President, Research & Technology Transfer

3. Agenda – April 12 • Overview of CELDi • Technical Review and Poster Sessions • Logistics Systems Analysis and Design • Intelligent Systems • Material Flow Design & Improvement • Luncheon • Technical Review and Poster Session • Supply Chain Modeling • Industry Speaker: Randy Gibson, President and CEO of Automation Associates • Reception

4. Agenda – April 13 • 7:30 am Continental Breakfast • 8:00 Research Initiatives • Clemson University • Texas Tech University • University of Houston • University of Nebraska • 9:45 Strategic Research Update • 10:00 National Science Foundation • 10:15 Industrial Advisory Board Meeting • 11:00 Center Evaluator • Luncheon • 1:00 pm Research Collaboration Roundtable

5. I/UCRC Purpose • Develop partnership among academe, industry and other organizations • Promote research of mutual interest • Contribute to the Nation’s research infrastructure • Enhance the intellectual capacity of the engineering workforce through research and education • CELDi Mission • The vision for the center is to provide integrated solutions to logistics problems, through modeling, analysis and intelligent-systems technologies.

6. Organization Center Headquarters Center for Engineering Logistics & Distribution University of Arkansas Dept. of Industrial Engineering 4207 Bell Engineering Center Fayetteville, AR 72701 Tel (479) 575-2124 • Fax (479) 575-8431 http://celdi.ineg.uark.edu • Academic Partners • Lehigh University • Oklahoma State University • University of Arkansas • University of Florida • University of Louisville • University of Oklahoma • Potential Partners: • Clemson University • Texas Tech University • University of Houston • University of Nebraska

7. Research Sponsors • Agere Systems • - Air Force Research Laboratories • - Boeing • - Cobb-Vantress • - ConAgra Foods • - Crane Div., Naval Surface Warfare Center • - Defense Logistics Agency • - E & J Gallo Winery • - Federal Aviation Admin. Logistics Center • - General Motors • - Halliburton • - Hewlett Packard • - Innovative Scheduling • - National Science Foundation • - Naval Supply Systems Command • - Navy FISC, Jacksonville • - Oklahoma City Air Logistics Center • - Oklahoma Department of Commerce • - Oklahoma Department of Transportation • - Pine Bluff Arsenal • - Science Applications International Corp. • UPS – Supply Chain Solutions • - U. S. Army Defense Ammunition Center • - Wal-Mart Stores Inc.

8. Technical Sessions 9:00 am Logistics Systems Analysis and Design POSTERS and LIFE forms 10:30 am Intelligent Systems Material Flow Design & Improvement POSTERS and LIFE forms 1:00 pm Supply Chain Modeling POSTERS and LIFE forms

9. Level of Interest and Feedback Evaluation (LIFE) forms • Dr. Tom Jones, Center Evaluator • Critical input toward research agenda • Valuable experience for student researchers

10. Technical Session 9:00 am Logistics Systems Analysis and Design POSTERS and LIFE forms

11. Improving Planning under Uncertainty at U.S. Navy Fleet and Industrial Supply Center, JacksonvillePrincipal Investigator: J. Geunes Research Team: C. Rainwater and A. Sylvester We have developed a basic MRP simulator that allows testing the impacts of various mechanisms for planning under uncertainty. Logistics Systems Analysis and Design University of Florida Project # ____FL05-FISC ____

12. Algorithms For Optimizing Yard OperationsPrincipal Investigator: R. AhujaResearch Team: Guvenc Sahin, Siddhartha Mohapatra, Sriram Parthasarathy A computerized simulation tool is already in place that is being enhanced. Heuristic algorithms are already in place (but require testing with real data). Logistics Systems Analysis and Design University of Florida Project # ____FL05-INOV____

13. Freight Movement Model Development for Oklahoma, Phase IVPrincipal Investigators: Ricki G. Ingalls, Ph.D., Manjunath Kamath, Ph.D. Samir Ahmed, Ph.D. (OSU), P. Simin Pulat, Ph.D., Guoqiang Shen, Ph.D. (OU) Deliver the regional model prototype and develop the math model for freight movement within the state The regional prototype is complete Literature review of state freight movement models. Targeted development of freight flow model for the State of Oklahoma. The methodology to model freight movement within the state of Oklahoma should be applicable to other states. Oklahoma State University University of Oklahoma Logistics Systems Analysis and Design Project # OSU/OU04-05-ODOT

14. Fixture Design Criteria: Phase III Principal Investigator: Shivakumar Raman, Ph.D. Research Team: Aashish Wadke Logistics Systems Analysis and Design Project # OU04-ALC University of Oklahoma

15. Lean 6-Sigma Approach to Field Service LogisticsPrincipal Investigator: Tom Landers Provide ongoing research, consultation, design, and assessment support to the FAA in identifying & implementing best practices for “lean 6-sigma” initiatives to retain continuous process improvement. Performed preliminary assessment of projects, including status, plans, impacts, and dependencies (first deliverable submitted). • Strategic sourcing • Issues effectiveness • Price variation • Bench stock • Confirmed defects • Capacity planning Distributor 1 Job Site Job Site MRO Center • Targeted Benefits • Systematic approach • Best practices Distributor 2 Job Site University of Oklahoma Project # __OU04-FAALC____ Logistics Systems Analysis and Design

16. Design and Structural Analysis of the Modular Pallet System Developed for Container Roll In/Out Platform (CROP)Principal Investigator: M. Cengiz AltanResearch Team: Brad Williams & John Byers Logistics Systems Analysis and Design University of Oklahoma Project # ___OU05-DAC____

17. Modeling Sortie Generation, Maintenance, and Inventory Interactions for Unit Level Logistics PlannersPrincipal Investigator: Manuel D. RossettiResearch Team: Raymond R. Hill, Dr. Narayanan, Josh B. McGee, and Todd Hausman The goal of this project was to develop simulation modeling methodologies that will assist logistics managers in analyzing the effects of different resource allocation policies and identify potential risks in logistics plans. • Simulation proved to be a viable tool in evaluating weekly schedules. This tool will allow unit level logistics planners to quickly and easily evaluate schedule tradeoffs and perform what-if analysis. • Potential exists to try different schedules: • To reduce the number of change opportunities • To improve phase flow line • Extend current Multi-Indenture Multi-Echelon simulation model to detail the sortie generation process • Develop a simulation model which can evaluate the effectiveness of a schedule along with the risk involved in individual schedules • Design User Interface • Design test scenario • Analyze simulation results The goal of this project was to illustrate that simulation can be used as an effective tool to support sortie scheduling decisions. Logistics Systems Analysis and Design University of Arkansas Project # UA-AFRL1

18. Virtual Information CIM Applications Information State Commands Materials Equipment Virtual Personnel Data Work In Progress Product Resources Simulator Data Simulation Model Simulation Technology Improvements for Maintenance Excellence (TIME) Principal Investigator: Manuel D. Rossetti, Ph.D., P.E.Research Team: Brad Hobbs, Stephen Farris, Josh McGee University of Arkansas Project # __UA-AFRL 2005___ Logistics Systems Analysis and Design

19. Comprehensive Selective Maintenance Decisions in an Autonomous Environment Principal Investigator: C. Richard Cassady, Ph.D., P.E.Research Team: Edward A. Pohl, Ph.D., Suzan Alaswad, Kellie Schneider, Yisha Xiang, Rebekah Johnson, Nick Rew Logistics Systems Analysis and Design University of Arkansas Project # UA-AFRL 2015

20. Research Experiences for Teachers (RET)Principal Investigators: John S. Usher Research Team: Russell Dart, Melissa Boeglin • Case study to validate tool • Undersea Systems Development and Support Branch • Surface Navy Total Ship Training System In-Service Engineering Section • Evaluate additional management assessment reports • Business unit summary scores • Delta charts for single competency across org • Briefed BU managers on project objectives and desired outcomes • Populated assessment tool with Undersea System Development Branch logistics data • Currently populating assessment tool for Surface Training Section • Populate tool for each business unit • Undersea System Development - Boeglin • Surface Training – Dart • Evaluate results and brief BU mgrs • Define proposed management report enhancements • Develop new reports and brief BU managers • Evaluate/modify/standardize improved reports • Document for programming into tool Logistics Systems Analysis and Design University of Louisville Project # UL-04-RET

21. Automated Forecasting of Replacement Part DemandPrincipal Investigators:Gail W. DePuy, John S. UsherResearch Team: Cindy Edlin • Minimizing overstocked parts to reduce holding costs • Minimize understocked parts to improve fleet readiness. • Develop a forecasting tool that combines time-series and causal factors to replace simple exponential smoothing. Have found significant correlation between specific product demand and “steaming hours” with a lag of 6-9 quarters which corresponds to typical operational cycles. Logistics Systems Analysis and Design University of Louisville Project # UL05-01 Crane

22. Project Scheduling and Capacity AnalysisPrincipal Investigators: Gail W. DePuy, John S. Usher Research Team: Joshua Roberts • Improve NSWC Crane ability to schedule resources for remanufacturing operations. • Develop project planning and resource scheduling tool based on a tool previously developed Have nearly completed full testing of the system with an example antenna refurbish application at NSWC-Crane (28 per year). Can now determine schedule from deadline and resource weekly schedules. Logistics Systems Analysis and Design University of Louisville Project # UL05-02 Crane

23. Technical Session 10:30 am Intelligent Systems Material Flow Design & Improvement POSTERS and LIFE forms

24. Unique ID of Tooling & Equipment at Pine Bluff Arsenal PIs: Justin R. Chimka, Earnest W. Fant, Nebil BuyurganResearch Assistant: David Scott Investigate unique identification technologies, define application environment and assist Pine Bluff Arsenal by identifying opportunities for improved manufacturing and maintenance • Equipment maintenance • Externally/Internally calibrated tooling • Production/Quality tool cribs • Unique identification technologies • Application environments • Manufacturing and maintenance Automate data entry to assist tracking of portable tooling, scheduling of preventative maintenance and calibration, and general data management of production area tools and machinery Intelligent Systems University of Arkansas UA05-PBA

25. Human-centric Mobile Information Technology in Air Force LogisticsPrincipal Investigator: Steven L. Johnson, Ph.D., PEResearch Team:Karthik Racheru Intelligent Systems University of Arkansas Project # __UA-AFRL 2045___

26. Culture Effective LogisticsTeam TeamCognition Technology Support Cognitive Modeling of Group Decision Behaviors in Multi-Cultural Contexts • To empirically investigate the effects of cultural differences and task environments on logistics-related decision makings in multi-cultural contexts • To develop cognitive model of logistics decision making behaviors in multi-cultural contexts, based on team performance and team cognition Collaborator PI Chang S. Nam, Ph.D. HCI Lab (UA) Amy S. Turner AFRL Collaborator Tonya L. Smith-Jackson, Ph.D. ACE/HCI Lab (VT) Research Assistant HCI Lab • Immad Qazi • Ravi S. Gunda • Robert A. Furrey HF students • Broader Impact: • Team dynamics in multi-cultural contexts and multi-agency team coordination • Culturally relevant intervention strategies • Effectiveness of group support technology • Culture-centered team decision behavior model Project #: UA-AFRL 2065 Thrust Area: Intelligent Systems

27. Research Goals Study 1:Team Decision Making in Multi-Cultural Contexts • To identify key issues/barriers regarding team decision makings in multi-cultural contexts • To empirically investigate cultural and environmental determinants that affect team decision making behaviors in multi-cultural contexts Independent Variables Dependent Variables Homogeneous Culture group Team performance Heterogeneous • Net income • Customer wait time • Task time CMC Communication mode FTF Task characteristic Dynamic Team cognition Routine • Team process • Communication • Mental models First period Time span Middle period Latter period Project #: UA-AFRL 2065 Thrust Area: Intelligent Systems

28. Research Goals Study 2:Experimental Evidence on Multi-Agency Team Coordination • To empirically investigate how multi-agency logistics teams respond to emergency that needs major medical coordination • To identify environmental determinants that affect multi-agency logistics team coordination and collaboration Independent Variables Dependent Variables Awareness Team performance Lowlevel Highlevel • Task time • Waiting time • Inventory level Cross training Shared learning Notraining Team cognition ATV Communication mode • Communication • Mental models • Coordination AT Project #: UA-AFRL 2065 Thrust Area: Intelligent Systems

29. Porphyrin Mediated Destruction of NitroenergeticsPrincipal Investigator: H. James Harmon (OSU) The goal of this work is to devise and understand a continuous-feed on-line solar-powered process for the destruction of energetic and hazardous materials. The final end-product is mobile flatbed or flat-car based solar unit to process energetic materials on-site without that logistics processes that transport the chemicals to a facility. This will provide an efficient inexpensive effective way of destroying organic chemicals such as organic waste streams from chemical processing, environmental pollutants, and even pesticides and chemical agents. This would be applicable for portable field use as well as permanent installations at chemical processing sites as well as, perhaps, water treatment facilities Task 1. Determination of Intermediates. Task 2. Photocatalytic Parameters. Task 3. Photocatalytic Reaction Pathways and Intermediates. Task 4. Quantum Mechanics and Optimization. Task 5. Prototype design and testing. Task 6. Stability and Immobilization of Porphyrin. Task 7. Sensors. Task 8. Set Up Chemical Analytical Facility to Support S. Korean Energetics Destruction. Task 9. Treat Waste Stream of TNT Production Facility at Radford, VA. Material Flow Design & Improvement Oklahoma State University Project # OSU05-DAC

30. Engineering Development of Automated Temperature System Principal Investigator:Earnest W. Fant, Ph.D., P.E.Research Team:Nebil Buyurgan, Ph.D., Chaitanya Sannathi This research study requires the development to implement the previous research which had the objective of determining the economic feasibility and technical practically of automating the assessment and controlling of a critical process variable for cooked meat portions. Material Flow Design & Improvement UNIVERSITY OF ARKANSAS Project # UA05-CNAG

31. Radio Frequency Identification and Productivity Improvements in Military LogisticsPrincipal Investigator: Dr. Manuel D Rossetti Research Team: Mr. Srinivasan Parthasarathy • Based on the extensive literature review, no streamlined schema has been found for justifying and implementing RFID technology in logistics, addressing the key questions, • Is investment on RFID technology going to make a substantial worthwhile improvement to the SC? • How to quantify the benefits of implementing RFID in the supply chains through an accepted and standardized methodology? • How to improve the information flow and tracking processes using RFID? • How to best exploit the benefits of RFID technology in the SC and the possible re-engineering involved? • Addressing the above questions is multi-attribute in nature and companies need to look at more/proper performance metrics than just cost (ROI). • To provide an analysis of RFID re-engineering opportunities in terms of productivity and benchmarking • Examine how organizations make decisions regarding the implementation of RFID • Develop multi-attribute techniques for evaluating RFID implementations • Test techniques in DDC warehousing situations • Series of Mini-Interviews with companies that are already in the implementation stages of RFID in their logistics to aid our methodology • Survey of Industry best practices which will serve as a benchmarking procedure for collection of recent industry data applicable to DDC • Testing of methodology, process modeling thro flow-chart diagrams and simulation software tools at DDCs. • Through the application of the simulation and cost models, we will analyze and document the behavior of the modeled RFID system and the justification of DDC re-engineering and RFID implementation. University of Arkansas Project # : UA05-DLA Material Flow Design

32. Technical Session 1:00 pm Supply Chain Modeling POSTERS and LIFE forms

33. Capacity Planning Using Leading Indicators at Agere SystemsPrincipal Investigator: S. David Wu, Ph.D., Lehigh UniversityCo-Principal Investigator: Rosemary T. Berger, Ph.D., Lehigh UniversityGraduate Assistant: Berrin Aytac, Lehigh UniversityAgere Liaisons: Chris Armbruster, Herb Betz • Products can be grouped into clusters based on similarity measure (technology, manufacturing resources, etc.) • One or more leading indicators can be identified whose demand pattern predicts overall demand of cluster • Correlation of demand pattern in relation to its group • Time lag by which demand pattern leads the group To explore models for demand volatility and the use of “leading indicators” to produce reliable demand forecasts to be used for capacity planning. • Develop leading indicator theory and implement using spreadsheets. • Interview capacity planners to understand processes • Develop and test prototype tools for demand forecasting and capacity planning • Improve tools based on feedback from controlled tests Leading Indicators provide time-lagged model that predicts demand pattern of broader group, critical for capacity planning in industry characterized by extremely volatile demand. Project #LH04-AGRE Supply Chain Modeling Lehigh University

34. Linking Internal Performance Metrics with Customer Value Indices Principal Investigator: Michael G. Kolchin Research Team: Matthew Lisk, Research Assistant • To determine linkages between internal performance metrics and customer value indices in the provision of services. • To ensure that appropriate activities are monitored to ensure delivery of high quality service. Too early to report results. Results from the study have the aim of providing providers of services the identification of internal performance metrics that will ensure high levels service delivery, as well as the information systems necessary to track these metrics. Supply Chain Modeling Lehigh University Project #LH05-BG

35. Technology Demand Management Demand Planning IDI:A Case Study in Supply Chain IntegrationPrincipal Investigator: Teresa M. McCarthy, Ph.D., Lehigh UniversityResearch Team: Donna Davis, PhD, Texas Tech Univ.; Howard Forman, PhD, Drexel University; Robert Trent, PhD, Lehigh University To explore the Interfirm Demand Integration process model from a true supply chain context, capturing the perspectives of several trading partner in multiple supply chains. Multiple supply chain case study research design. Depth interviews are conducted in companies comprising 3-tier supply chains. Four supply chains in four different industries will be included, each supply chain consisting of a retailer, manufacturer, and supplier to the manufacturer. IDIInfrastructure HumanResource IDI Process EffectiveRelations PerformanceMeasurement Supply ChainPerformance Results will help firms understand how, when, and with whom to integrate supply and demand activities in order to deliver superior customer value and achieve differential advantage. CollaborativeForecasting Project # LH05-HP Supply Chain Modeling Lehigh University

36. Fleet Optimization for Oilfield Production Enhancement ServicesPrincipal Investigators: Ricki G. Ingalls, Ph.D. This project that will create a model that will determine an optimal configuration of production enhancement equipment at camps for a given set of potential jobs. Project Complete 1) Literature REview 2) Develop the mathematical model for this optimization. 3) Implement the mathematical model in commercial optimization software. 4) Deliver the final report. A model that can be adapted to fleet optimization problems in other industries. Supply Chain Modeling Oklahoma State University Project # OSU04-HLBT

37. Labor Planning Model for Uncertain DemandPrincipal Investigator: Ricki G. Ingalls, Ph.D. This project will create a labor planning model for Crane NWSC that takes into account volatile demand profiles. Early results show that this model can be adapted to the Crane process. 1) Leverage the learning from the CELDi project, Manufacturing and Distribution Strategies for Volatile and Cyclical Customer Demand, which was done for Smith Tool and was completed in 2003. 2) Develop the mathematical model for this optimization. 3) Implement the mathematical model in commercial optimization software. The implementation will be in Xpress, which is a product of Dash Optimization. This project extends earlier CELDi work and shows that the volitile labor planning model has broad application. Supply Chain Modeling Oklahoma State University Project # OSU04-05-ODOT

38. Equipment Scheduling and OptimizationPrincipal Investigators: Carlos Oliveira, Ph.D., Ricki G. Ingalls, Ph.D. Create an optimization model that will determine the optimal configuration and assignment of pumping equipment at the multiple Halliburton camps. New project - no results to date 1) Literature Search 2) Develop a database for the optimization. 3) Develop the mathematical model for this optimization. 4) Implement the mathematical model in commercial optimization software. 5) Alpha test the optimization package on a problem of Halliburton’s choosing. 6) Deliver the final report. This is a very complex scheduling problem that can be applied to different industries. Supply Chain Modeling Oklahoma State University Project # OSU05-HLBT

39. TIE Project: Healthcare Supply Chain Modeling using Simulation Principal Investigator: Dr. Manuel Rossetti Research Team: Dr. Rossetti, Amit Bhonsle Healthcare Cost Reduction Supply Chain Modeling University of Arkansas Project # UA/CHMR

40. NAVSUP Weapon System Inventory Demand ForecastingPrincipal Investigator: Dr. Manuel D. RossettiResearch Team: Vijith Varghese Compare relevant intermittent demand forecasting techniques; the measures of performance being error and system wide cost. Study the effect of aggregation of demand history. Develop and propose an improved variant of the discussed forecasting methods. The Phase 1 results shows Croston with an alpha value of 0.2 found to be the winner overall as well as in scenarios where there is high intermittence and lumpiness. The factors (shown in the figure below) has significant effect on the error measures. There is interaction between all factors except history size in most of the scenarios. The research is implemented in two phases. The first phase is an empirical comparison of different techniques based on forecast errors. The second phase investigate on the performance of each technique in a simulation model with the performance measure being system wide cost. A cost based comparison of intermittent demand forecasting technique. Find the most appropriate technique for NavSup. Project # UA04-NAVSUP Add University of Arkansas Supply Chain Modeling

41. Evaluation of Segmentation Techniques for Spare Parts Inventory ManagementPrincipal Investigator: Dr. Manuel D. RossettiResearch Team: Ashish Achlerkar, Mohammad H. Al-Rifai, Vikram L. Desai • Developed a data generation methodology • Developed a Multi Echelon Inventory optimization model • Developed an Heuristic to set (r, Q) policies at all the locations • Develop a data generation methodology • Develop a Multi Echelon Inventory optimization model • Develop an Heuristic to set (r, Q) policies at all the locations • Develop a segmentation methodology • Experimentation and Analysis • Recommend a generic solution Include color picture or graphic. • Segmentation methodology would significantly reduce the computation time for setting control policies for millions of SKUs in a multi echelon inventory system • The solution will empower the managers of large multi echelon inventory systems with more control over the SKUs for strategic analysis Supply Chain Modeling University of Arkansas Project # UA04-NAVSUP, Ph2

42. C/KC 135 Weapon System Stockage Policy Analysis Principal Investigator: Edward A. Pohl, Ph.D. Research Team: Manuel D. Rossetti, Ph.D., P.E., Justin Chimka, Ph.D., Jason Honeycutt, Roger Snelgrove Thrust Area University of Arkansas Project # UA-AFRL 2025

43. “Simulation Solutions for the Integrated Supply Chain” Randy Gibson President and CEO Automation Associates

44. Reception • Reception at 6:30pm • OSU Student Union, Sequoyah Room 280