AFRL Update Presentation

1. CELDi Center for Engineering Logistics & Distribution AFRL Update Presentation Dr. Manuel Rossetti

2. Quantifying the Effect of Commercial Transportation Practices in Military Supply Chains Sponsor: Air Force Research Laboratory PI: Manuel D. Rossetti, Ph.D., P.E. Co-PI: Scott J. Mason, Ph.D., P.E. Graduate Researcher: Josh McGee Objectives: • Develop a simulation model based on the Multi-Indenture Multi-Echelon (MIME) repairable inventory system used by the United States Air Force (USAF). • Assess the effect of applying commercial practices to military supply chains, and then evaluate the results by using metrics currently used by the Air Force. Deliverables: • Strategic policy recommendations concerning which commercial practices to adopt to improve weapon system availability and reduce cost. • Simulation model of examined processes. • Project report

3. Project Motivation • Military budgets have been declining. • Supply chain must be more flexible and responsive with less money. • The many layers of the supply chain often result in unnecessary cost and delay times, as well as low network reliability. • Better integration between the multiple levels of the supply chain may be achieved through the effective utilization of transportation modes and criterion.

4. Initial Steps • Conceptualized military supply chain as a multi-indenture, multi-echelon (MIME) spare parts inventory system • Identified and defined commercial transportation practices that are applicable to military supply chains • Identified successful transportation practices that have had success in other branches of the military • Practices include: • Lateral Shipping • Truckload Shipments • Less-Than-Truckload (LTL) Shipments • Direct Shipments

5. Multi-Indenture Multi-Echelon (MIME) System • The MIME system discussed in this research is similar to the systems analyzed by: • Sherbrooke (1968 and 1986) • Muckstadt (1973) • Nahmias and Rivera (1979) • Graves (1985).

6. Weapon2 Weapon1 Weapon3 Base2 Weapon1 Weapon1 Base3 Weapon2 Base1 Weapon2 Depot Weapon3 Weapon3 MIME System An example MIME System with one central depot serving three bases which in turn operate several weapon systems. Repaired part Inventory Warehouse This diagram represents the failure/repair cycle. Repair Facility Failed part Spare part Airplanes

7. Air Force Multi-Echelon Structure • In our simulation model the MIME structure includes: • 6 independent bases • 1 support depot • 24 Aircraft assigned to each squadron • 3 squadrons assigned to each wing • 1 wing assigned to each base

8. Air Force Multi-Indenture Structure • Each weapon system has two levels of indenture • The first level of indenture entails aircraft which are made up of multiple Line Replaceable Units (LRU) • These LRUs are in turn comprised of multiple Shop Replaceable Units (SRU)

9. Air Force Multi-Indenture Structure The diagram below illustrates the Multi-Indenture structure used in our simulation model.

10. Aircraft States For the purposes of this model, aircraft are always categorized as being in one of three states: • Mission Capable (MC). An aircraft is designated MC when it is capable of flying a sortie. This status can correspond to an aircraft that is currently flying a sortie or is waiting to be assigned to a sortie. • Non-Mission Capable (NMC). An aircraft is designated NMC when one or more of its critical SRUs fails. This status corresponds to an aircraft that is down either awaiting a spare part or currently in the process of spare part installation. NMC aircraft cannot fly sorties. • Phase Inspection (PI). An aircraft is designated PI when it enters the phase inspection module. While in phase inspection the aircraft is not available to fly sorties; therefore, an aircraft listed as PI is also considered NMC.

11. Failures • A Time-to-Failure (TTF) value is generated for each SRU comprising an aircraft from one of three Mean-Time-to-Failure distributions: • High - exponential (500) hours • Medium - exponential (400) hours • Low - exponential (300) hours

12. Failures • As the aircraft accrues flight hours, each of the corresponding SRU TTF values are decremented accordingly. • A failure occurs when one or more of the aircraft’s SRUs TTF values reaches or goes below zero. • Failures are detected during one of three inspections: • Pre-Flight • In-Flight • Post Flight • Once a failure is detected the Aircraft is designated NMC and sent to unscheduled maintenance

13. Replacement Process • When a failed part is detected, it is removed from the aircraft and sent to be repaired. • An inventory search is initiated for the part beginning at the base level. • If there is not a spare available at the base level an order is placed to the depot and given a backorder status • Inventory inside the system follows a 1-for-1 inventory policy

14. Replacement Process • NMC aircraft wait at the base in queue for needed spare parts to arrive. • Once a spare part arrives in the base inventory, the installation process begins. • Following the replacement of all failed parts, an aircraft is once again listed a MC and released to fly sorties.

15. Sorties • Sorties are generated at the beginning of every day and assigned to specific bases. • The number of sorties per base is generated from the uniform distribution over the range 56-66. • The sorties are then separated into 2 runes/goes to simulate a 10 turn 12 scenario. • Sortie duration is generated from the triangular distribution with parameters (.5,1.35,2) hours.

16. Sorties • At the specified run time each sortie attaches itself to an available aircraft and begins the pre-flight operations. • If there are no available aircraft the sortie waits for a period of time and searches again. • If there are no available aircraft in the allotted time the sortie is aborted. • An aircraft will continue to fly sorties until one of its component SRUs fails.

17. Repair Process • When an SRU fails it is sent to the repair process with a 1% probability it can be repaired at the base and a 90% probability it must be sent to the depot. • It the part must be sent to the depot it is delayed for a specified shipping time and then enters the repair process. • The repair processes at the base and the depot are modeled as simple delays. • The repair stations at all bases and the depot give priority to backorders for repair jobs.

18. Shipping • Shipping Options • Less-Than-Truckload (LTL) • Truckload (TL) • Direct Shipments (MICAP) • Lateral Transshipments

19. Shipping • LTL/TL • The LTL options simulates and environment where trucks ship parts independent of the load size. • The TL option simulates and environment where there is a minimum load size that has to be met before parts are shipped. • MICAP • This simulates express shipping, and is used to ship all backordered parts in our model. • Lateral Transshipments • Shipments between bases in the same geographical region. • When this feature is turned on spares inventory is first checked at the base level then the regional level then the order is sent to the depot.

20. Experimental Design • Factorial Experimental Design • 11 Factors • 2 Levels Each • Would require 2048 runs. • Fractional Factorial Design • 1/16 fractional design • 128 required runs • Resolution V design

21. Factors • For this presentation we will focus on three

22. Factors • Each of the factors has two levels

23. Simulation Parameters • Warm up period of six months • Collect data for one year • Simulation is set to run 128 instances each of which represents a different combination of factors or a single design point within the experiment • The simulation is warmed up at the beginning of each instance • The system is cleared after each of the runs • Therefore, the simulation model collects data for 128 independent design points. • Each of these 128 design points is replicated five times using a different stream of random numbers, yielding a total of 640 independent observations.

24. Responses • In the experiment data was collected for eight different response values. • For this presentation we will focus on two.

25. Summary Statistics

26. Diminishing Returns

27. Regression Analysis • Regression analysis was performed on each of the responses. • Reduced number of factors included in the regression equations

28. Conclusions • MICAP was one of the most influential factors in the experiment. • Expedited shipping • Large cost component • LTL/TL also had an influence. • Lowest cost experienced with TL along with the repair resources shifted to the local level • Transshipment was not very influential due to the fact is was overshadowed by the other shipping components.

29. Continuing Our Work • Resulting Papers • WSC paper to be published for the upcoming conference • Second paper in preparation • Continuing Research • Sortie Generation • Schedule Risk • Resource Management

30. Modeling Sortie Generation for Unit Level Logistics Planners Sponsor: Air Force Research Laboratory PI: Manuel D. Rossetti Co-PI: Raymond R. Hill, WSU and Dr. Narayanan Objective • 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. Activities • Extend current simulation model to detail the sortie generation process • Model verification and validation • Experimental design • Analysis of simulation results • Delivery of models and report to AFRL/HESR

31. Problem Statement • The sortie generation process is driven by the sortie schedule. The process of scheduling aircraft is an iterative process which includes annual, quarterly, monthly, and weekly scheduling meetings. • Annual and Quarterly schedules involve rough requirements planning • At the monthly planning session that a specific schedule takes shape • Weekly planning involves refining the monthly schedule based on constraints which are met through the month

32. Problem Statement • Problem: • There is not a good tool available for schedulers to evaluate the risk involved in a schedule or in making needed schedule changes. • How we plan to address the problem: • Develop a simulation model which can evaluate a schedule and possibly evaluate the risk involved in certain schedule changes.

33. Literature Overview of Sortie Generation Sortie Generation Howard, H. and Zaloom, V. (1980) Hilliard et al. (1992) Solomon, M. and Desrosiers, J. (1988) Solanki, R. and Southworth, F. (1991) Sklar, M. et al. (1990) Faas (2003) One of the measures by which a Fighter Wing is evaluated is its ability in launching aircraft on time and in the correct configuration to meet the requirements of the day’s missions. Our model will focus on the interactions between sortie and resource scheduling in hopes of quantifying the risk inherent in different logistical plans.

34. Modeling Approach and Experimentation Modeling • We have created a model of the basic Multi-Indenture Multi-Echelon scenario. Our goal for this project is to extend the current model, detailing the sortie generation process Once sufficient detail has been added to the model we will validate it and begin experimentation. Experimentation • We plan to test the effectiveness of different planning strategies in two areas: • Sortie Scheduling • Resource Management

35. Weekly Schedule • “Weekly scheduling is the final refinement of the monthly plan and results in the weekly flying and maintenance schedule.” The weekly schedule is distributed no later than 1200 Friday morning before the effective week and will include: • Aircraft takeoff and landing times including aircraft tail numbers • Sortie sequence numbers • Configuration requirements • Munitions requirements • Fuel loads • Special or particular mission support requirements • Alert requirements • Exercise vulnerability • Deployments • Off base sorties • Equipment training requirements

36. Weekly Schedule There are three types of changed which can be made to the weekly schedule after its distribution: Pen-and-Ink are intended to allow for minor changes to the weekly schedule which arise due to fluctuation in aircraft availability. Allowable changes include tail numbers, takeoff/landing times, etc… Interchanges or swapping tail numbers are intended to prevent unnecessary reconfigurations and expenditure of work hours. Configuration changes in the required configuration of units can be made to reduce man hours as long as the requirements of the sortie can be met.

37. Performance Measures • Flying Schedule Effectiveness: • We plan to develop other performance measures that will effectively capture the risk involved in a specific schedule. These other performance measures should be: • Meaningful to the user • Easy to interpret We also plan to investigate other performance measures used currently used by the Air Force.

38. Progress We are currently developing: • User input screens • Detailed sortie generation model • Database to handle user input and model output Modeling Challenges • Creating a user input environment that is user friendly and useful to the model • Capturing the initial state of the squadron which is being simulated