Enterprise Analysis and Cost Optimization System (EACOS)

Transcription

1 Enterprise Analysis and Cost Optimization System (EACOS) 2006 Department of Defense Maintenance Symposium & Exhibition Modeling for Sustainment Technical Breakout Session October 23, Rob Thomson Program Manager Aging Aircraft Programs Northrop Grumman Corporation Guy Engler Program Manager Life Cycle Optimization Northrop Grumman Corporation

2 The Essence of EACOS is Life Cycle Optimization Achieving the Greatest Collective Benefits for the Fleet within Cost & Other Constraints Other Requirements & Constraints Fleet Drivers / Problems / Issues / Roadmaps Enterprise Analysis and Solutions Total Available Funding (by FY and Type) Resource Analysis Decision Support System (RADSS) Weighted Decision Criteria & Standards Availability Mission Capable Rate Readiness Total Ownership Costs Combat Capability Other Data Analysis Scenario Analysis Sensitivity Analysis Portfolio Planning Balanced Investment Portfolio 1

3 EACOS Background Objectives Identify drivers affecting fleet availability and system modernization / operating & support costs Identify and model solutions, investment options, and scenarios that provide: Optimized improvement in aircraft fleet availability Optimized combat capabilities Optimized approaches to modernization and sustainment Optimized approaches to Fleet Viability Reductions in total ownership cost Best return on investment Bottom line is to achieve the greatest collective benefits within constraints, thus realizing optimum investment strategies and an Optimized and Balanced Budget and Execution Portfolio for the fleet Northrop Grumman has completed EACOS implementations for the C-5, B-1B, A-10, F-16, & B-2 fleets, plus a Cross-Fleet Pilot Project 2

4 Complicating Factors Increasing Ownership Costs Reliability & Maintainability Problems Structural Problems Changing Mission & Performance Requirements Parts Supply Problems Decreasing Mission Capable Rates Availability 3

5 The EACOS Approach What is it? Enterprise Analysis and Cost Optimization System Logistics Information Systems WHAT IT DOES a a Baselines System Identifies necessary improvements a Allows modeling & prediction of Improvements a Optimizes Investments System Design Mission Profiles Operations Inventory / Supply Maintenance Related Cost Data Reliability Improved Readiness / Availability Reduced Total Cost of Ownership 4

6 # of Aircraft Baseline -10% NMCS -20% NMCS -30% NMCS -40% NMCS -50% NMCS The EACOS Approach A Repeatable Process! Baseline Availability Probability Identify Drivers Probability Examine Problem Areas X: Hardware / Software changes at the component or system level # Aircraft Identify Possible Solutions A = f (x,y) Prioritize Solutions with RADSS Blend with Roadmap Projects Y: RTOC AAIP SERP PIWG RIC Policy / process changes to the support infrastructure Optimized Investment Portfolio 5

7 The EACOS Approach Phase I: Enterprise Analysis Baseline Fleet and Identify Drivers & Solutions (Investment Candidates): Fleet Availability Mission / Operations profile (Operations, Training, MC Rates, Flying Hours) Reliability (Inherent Failures, Mission Aborts, Mean Time Between Failures) Maintainability (Not Mission Capable Maintenance, Maintenance Manhours) Supportability (Not Mission Capable Supply, Flow Days) Maintenance Concepts (Field, Depot, Phase, and Analytical Condition Inspections) Supply Concepts (Supply Chain Management, Defense Logistics Agency) Phase II: Cost Optimization System Define Decision Goal Identify and Weight Decision Criteria and Standards (to generate Benefit Scores ) Evaluate Investment Candidates (Phase I Analysis, Roadmaps, etc.) Analyze Data (Show Stoppers, Correlations, Constraints) Identify Global and Boolean Constraints (for Linear Program optimization) Load Data into Resource Allocation Decision Support System (RADSS) Run RADSS to generate Benefit Priorities and Benefit-to-Cost ratios Conduct Sensitivity and Scenario Analysis Select Optimum Solution Set to achieve greatest collective Benefits vs. Constraints 6

8 Fleet Availability Baseline & Required Improvements Mathematically Modeling the Effects of Investments % Yr Mission 2005 Capable PAA Future, Higher Requirement Future Yr Level 2005 of Aircraft PAA Available Probability Probability that Aircraft are Mission Capable Needs to shift Current Avg Mission Available Capable Aircraft # Aircraft Current Avg Available Possessed Aircraft Need 83 A/C Mission Capable 63 A/C Mission Capable Today 7

9 Monthly Status of an Average Aircraft 62.4% MC Rate EACOS identifies Drivers to Fleet Availability and Ownership Costs Mission Capable 15.1 Not Mission Capable 9.1 Fully Mission Capable 9.7 Partially Mission Capable 5.4 PMCB 2.7 PMCM 1.5 PMCS 1.2 NMC Maintenance 4.8 NMC Both 2.4 Flying Time 1.64 days 9.6 Sorties per Month ************ 39.4 Hours per Month ************ 4.1 Hours per Sortie NMCM Unscheduled 4.1 NMCMS.7 NMCBS 1.7 NMCBU.7 EACOS then models Solutions to all Drivers (including related Investment Costs) and Predictive Results NMC Supply 1.9NMC Supply 1.9 Depot 5.8 8

10 Sample of Hundreds of Drivers and Solutions 9

11 EACOS Phase II: Investment Optimization Resource Allocation Decision Support System A Complete, User-Friendly, Integrated Portfolio Planning and Management System RADSS GOAL: Rate NAVFAC Southwest Environmental Site Clean-Up Projects GOAL: to allow Rate Optimization NAVFAC Southwest of Total Benefits Environmental to Constrained Site Clean-Up Costs Projects to allow Optimization of Total Benefits to Constrained Costs Current Criteria (.500) (.100) (.050) (.100) Environmental Stakeholder Use of Mission Risk Values, Concerns Innovative Impact Environmental Stakeholder Use of Mission Technologies & Priorities Risk Values, Concerns Innovative Impact & Priorities Technologies Fed/State/Local Regulators (.050) Community/ Political (.050) (.050) (.050) RRSEM Degree of Financial Contaminant Hazard Factor (CHF) Certainty + Degree of Financial Migratory Pathway Factor (MPF) Certainty Early Award (.025) + Receptor Factor (RF) Early Expense (.025) + RRSEM CHF Total Customized AHP Decision Model (s) 10 (.100) Response Complete/ Close-out Response Complete/ Close-out (.050) Legal Agreements Legal Agreements Windows Database Management Data Bases (M/S Access) Reports and Graphics Safety Risk Performance Lifetime Supportability Special Factors Versatility Enhanced Analysis and Decisions CRITERIA ALTERNATIVES Fuel Cells Solar Cells Nuclear Reactor CRITERIA PERFORMANCE ALTERNATIVES X X X X.4 Solar Cells X.5.3 X X Fuel Cells X Safety Risk Performance Lifetime Supportability Special Factors Versatility Overall Powerful Optimization Program & Sensitivity Analysis

12 Broad Range of RADSS Applications Tailored to Individual Customer Requirements 11

13 RADSS TM Architecture Portfolio Planning via Decision Modeling Problem Sets Problem Sets Problem Sets Problem Sets Problem Sets 12 Problem Sets Problem Problem Problem Problem P External Data Bases Decision Models Decision Models Decision Models Decision Models Decision Models Relational Data Base Decision Models Acquisition / O&M Life Cycle Costs Multi-Dimensional Cost and Boolean Constraints Linear Programming Module Reports & Graphics Benefits Benefits / Cost Prioritization Maximization Optimization Analysis Tool Scenarios Sensitivities Audit Trail Reports Graphics

14 Criteria, Constraints, and Rules RADSS supports both Qualitative and Quantitative Decision Criteria and Standards RADSS supports multiple Constraints Global (Total or annual budgets) Category (On classifications) Minimum or maximum (Cost or resource levels) Additive (The more stringent constraint takes precedence) RADSS supports Boolean Rules Include (Alternatives are funded regardless of their cost or benefit) Exclude (Alternatives are not funded regardless of their cost or benefit) Linked (Dependent alternatives are linked to independent alternatives) One or None (Only one alternative of a group or none are funded) All or None (Either all alternatives in a group or none -- are funded) 13

15 Sample EACOS Decision Model 14

16 Sample EACOS Decision Model Generates Benefits Score for each Solution Availability 30 % Decision Goal Improve aircraft availability, combat capability, control cost, and improve fleet viability through Capability 28 % Viability 24 % Life Cycle Costs 18 % Survivability Mission Effectiveness Capability Obsolescence 49 % 30 % 21 % Return on Investment--ROI 46 % Savings Magnitude--RTOC 54 % Capability Improvement 39 % Situational Awareness 28 % Pilot Workload 20 % C2ISR 13 % Avionics Low Observables Weapons 41 % 36 % 23 % Supportability 29 % MC Rate Change 27 % Maintenance 26 % Depot Days 18 % NMC Hours Maintenance Obsolescence MICAPS 45 % 23 % 32 % Unscheduled MMH/FH 65 % Maintain with Less 35 % PDM Days 50 % Mod Line Days 50 % 15

17 Sample PBL Decision Model Application Decision Goal Improve aircraft availability, combat capability, control cost, and improve fleet viability through Availability Capability Viability Life Cycle Costs 30 % 28 % 24 % 18 % Investment Decision Models can also Survivability 49 % Return on Investment--ROI 46 % Mission Effectiveness 30 % Savings Magnitude--RTOC 54 % Capability Obsolescence 21 % include Performance Based Logistics Criteria tailored to each Fleet: Supportability 29 % NMC Hours 45 % Maintenance Obsolescence 23 % MICAPS 32 % Capability Improvement 39 % Operational Availability Mission Reliability Avionics Low Observables Weapons 41 % 36 % 23 % MC Rate Change 27 % Situational Awareness 28 % LCC System Cost per Unit of Usage Logistics Footprint Logistics Response Time Maintenance 26 % Pilot Workload Unscheduled MMH/FH 65 % Maintain with Less 35 % 20 % Depot Days 18 % C2ISR 13 % PDM Days 50 % Mod Line Days 50 % 16

18 EACOS Phase II: Investment Optimization Process 1 RADSS Decision Process Define Decision Goal Identify Decision Criteria 2 Weight the Criteria Create & Weight Standards 3 Identify Decision Alternatives Resource Analysis Decision Support System (RADSS) Problem Sets Decision Models Problem Sets Decision Models Problem Sets Decision Models Problem Sets Decision Models Problem Sets Decision Models Problem Sets Decision Models Problem Problem Problem Problem P Relational Data Base Benefits / Value Benefits / Cost Multi-Dimensional Prioritization Cost and Boolean Maximization Constraints Optimization Analysis Tool Scenarios Linear Sensitivities Programming Audit Trail Module Reports Fleet Availability 34.0% NMCMU NMCS NMCBU Other Down Time Metrics 26.0% MC Rate Fix Rate Break Rate DR MRT Cann Rate UR MMH/FH 4 Collect Criteria Data for each Alternative Assess Enhancement Candidates for execution in the xxxxxxxxx Other ORD Requirements 10.0% Analyze Data & Identify 5 Show Stoppers Positive Outliers Correlations Risk Constraints 5.0% Service Life Syst. Charact. Schedule Other Logistics Parts Obsoles. Cost Mx Planning Trans & Basing Technical Policy/Law/Political External DMSMS Data Bases Load & Execute RADSS 6 Unconstrained Multiple Constraints Multiple Scenarios Acquisition / O&M Life Cycle Costs Reports Data Export and Iterative Analysis System Performance 7.0% Performance Computer Resources C4I Cumulative Benefit $0 Total Ownership Human Systems Cost Integration 14.0% 4.0% Maintenance Training Aircrew Training Manpower & Personnel $2,678,669 $7,414,884 $18,743,490 $24,555,609 $67,422,673 $113,629,528 $171,689,019 $242,586,340 $285,463,802 $325,357,855 $512,822,480 $607,732,840 $725,800,979 $804,549,149 $1,009,263,080 $1,021,935,599 $1,036,835,537 $1,066,955,168 $1,112,113,886 $1,246,639,761 Delta MCR (%) Cumulative Cost Series1 Series2 0.5 HIGHEST TO LOWEST DELTA MCR % 112 xxxxxxxx xxxx xxxxxsystem 2 MLG xxxxx 98 NLG xxxx Windshield 12 Digital Fuel xxxxxx 13 Crew xxxxxxx 116 Fuel xxxxx MLG xxxxx 67 xxxxxxs 94 Outboard xxxxxx Wing xxxxxx xxxxxxx xxxxxxxx $0 $50,000 $100,000 $150,000 $200,000 $250,000 $300,000 $(000) Show Horses Selection Iterations Show Stoppers Examine Results & Make Final Adjustments 7 Benefit Prioritizations Benefit to Cost Ratios (ROI) Sensitivity Analyses Scenario Analyses Select Optimum Solution Set Portfolio Planning 17

19 EACOS RADSS Run Benefit to Cost 18

20 Delta Mission Capable Rate vs. Cost 19

21 Delta RTOC versus Cost 20

22 Scenario #9 - Medium Cost Boolean Rules 21

23 Medium Cost Alternatives ($25M - $200M) 22

24 Cumulative Effects of Funding Scenario #9 23

25 Predictive Nature of Funded Scenario(s) Funding of a single Investment Candidate will result in calculated changes to the metrics contained in the EACOS Fleet Decision Model, i.e., deltas to: Availability / Mission Capable Rate Scheduled / Unscheduled Maintenance Manhours MICAPs Capability Life Cycle Cost Depot Flow Time Other Metrics applicable to the customized fleet decision model Modeling and funding of a group of Investment Candidates within a specified scenario will result in a cumulative delta change to fleet MC Rate, RTOC, etc. 24

26 Benefits of the EACOS Process Complements / Enhances Current Decision Processes Rigorous Enterprise Analysis and RADSS Approach is highly Effective Allows Modeling & Better Differentiation & Prioritization Enhances Marginal Returns on Complex Decisions Increases Total Benefits within Constrained Budgets Collectively Achieves Greater ROI Optimized & Balanced Budget and Execution Portfolio 25

27 Summary The RADSS modeling tool simplifies and optimizes complex investment decisions Considers decision criteria of all Stakeholders Considers a wide-variety of constraints Considers sensitivity and scenario analysis Results in multiple, customized Investment Portfolios EACOS has broad applications across the Services All aging systems can benefit from the baselines and EACOS predictive assessments to help support modernization & end-to-end sustainment investment decisions All DoD processes (supply, maintenance, depot, operations) can benefit from EACOS principles & tools Special initiatives can benefit, e.g., Modeling for Performance Based Logistics (PBL) and Aircraft Availability Improvement Program (AAIP) 26