Headquarters U.S. Air Force

Transcription

1 Headquarters U.S. Air Force Fly Fight Win Data Management as the Key to Prognostic Capability Mr. Russ Alford Warner Robins Air Logistics Center System Sustainment Division 13 Nov

2 elog21 Campaign Initiatives Logistics Enterprise Architecture (LogEA) ARCHITECTURE & GOVERNANCE Portfolio Management Air Force Data Strategy Performance Management Product Support and Engineering Supply Chain Management Expeditionary Operations and C 2 Maintenance, Repair and Overhaul Total Life Cycle System Management Prod Supt Camp AAIP Condition Based Maintenance+ Asset Marking and Tracking Demand Management Product Life Cycle Mgmt Centralized Asset Management Integrated Planning System (IPS/APS) AFFVESA, JPPC-AF, ACP-AF, NWC Consolidation Purchasing Supply Chain Management (PSCM) Next Gen Log Read Sdqn Strategic Distribution Weapon System Supply Chain Management ENABLING PROCESSES AND TECHNOLOGY Agile Combat Support (ACS)/Assured Connectivity AF Smart Ops (AFSO21) Change Management (CM) Capability-Based Programming (CBP) IT Strategy/Expeditionary Combat Support System (ECSS) WFHQ/ Agile Combat Support C2 Global Log Supt Center AF Common Operating Picture A4/7 COP Decision Support Tools Maintenance Strategic Plan Repair Enterprise 21 Single Off-Equip Net Re-engineering Depot Maintenance (DMT) AF Lean Maintenance Enterprise Integration Workforce 2

3 Future Process Model Integrated Processes Integrated Technology Optimized Resources Enterprise View Seamless end-to-end supply chain operations delivering the right support, at the right price, in the Fly right Fight place, Win at the right time -- every time 3

4 ECSS Program Overview Improve warfighter capability by transforming AF Logistics' business processes and leveraging ongoing initiatives & capabilities that IT can deliver today Objectives Establish near real-time modernized information system Apply best commercial practices Utilize COTS based solution Plan and execute obsolete legacy retirement (~250 systems) Increase equipment availability by 20% Reduce annual O & S cost by 10% 4

5 COTs Lessons Learned Past COTs attempts have generally failed Weak governance and functional oversight Didn t really understand the business processes Little or no change management and training Immature products & undercapitalized vendors No incentives to implement Data cleanup effort under appreciated & under valued Limited testing and test environment Same lessons observed in Industry MUST ADDRESS TO SUCEED 5

6 Maintenance Data Collection Data is collected to serve a process not just to collect data Engineering and Product Data Production and Quality Supply Chain Maintenance data quality is poor but so is all of our data Fat fingering, discipline, lack of edits, interfaces Maintenance data is underutilized mostly reactive Missing key elements e.g. IUID and depot inputs Processes do not drive to use of data e.g. engineering Near and Long term efforts underway to address maintenance data This problem and solutions are not limited to maintenance data 6

7 Data Improvement Initiatives Understanding the Problem: AFMLA and AFMC/A4 conducting a studies on mx & supply data to help guide the clean up of legacy data Addressing Fat fingers: IT driven improvements are/will help improve near term collection e.g. CAMS GUI, POMX, REMIS Edit rehosting Addressing Discipline IUID Implementation will address serialization needs Product Life Cycle Management (PLM) initiative addresses product data clean up and conversion ECSS dramatically changes production & quality data ERPs demand a significantly more disciplined data approach than anything we have ever done Addressing Utilization D&SWS Life Cycle System Engineering Sustainment is driving a relook at what we collect and how we use it Predictive use RCM, Condition Based Maintenance, et al 7

8 AFLMA Near Term Efforts AFMLA Data integrity study of IMDS & REMIS Bounding and prioritizing the quality issues Developing repeatable process to support ECSS Capturing the functional impact of data quality issues Benchmarking the level or effort to identify, recommend and act on data quality issues Defining remediation criteria (Fix-Now, Fix prior to or during ECSS Migration, Fix after migration to ECSS) Focus on Metrics Data Identify near-term process or technology improvements in reporting accurate status and utilization information Address lead-time ECSS data migration issues Collaborating with ECSS on a common method and toolbox 8

9 The AIT Enabled Data Collection AIT eliminates the need to collect the what (item/part, IUID, location, time, date, description, quantity, etc.) The human in the loop captures the how (receipt, shipment, removal, install, disposal, etc.) The technology feeds the airman information vs. the airman feeding the technology data Manufacturers/ Suppliers Distribution Centers/Depots POEs/PODs Transportation/ Supply/ Theater Depots/TDCs Customers PASSIVE ACTIVE PASSIVE Bar Code Bar Code Minimizes human inputs: Data is captured not entered 9

10 OEM Collaboration Workflow &Process Management MRO Product Life Cycle Management Secure Access Control Product Data Vault Configuration Management Design Collaboration Product Structure Supply Chain Collaboration (PLM) Centralized Data Management for: TOs BOMs Drawings Illustrated Part break-down Repair Manuals & Procedures Key Performance Parameters Requirements & Specifications Interchanges and Substitutions Standardizes Data Management Processes across commodities Stores data for root-cause and product improvement analysis and planning Demands AF & OEM Integration and digital technical data vs. paper Fly Fight Win 10

11 ECSS Legacy Deconstruction 11

12 ECCS Impact on Data Collection Deconstruction and Transformation ECSS must be initialized with clean data from the legacy environment Not all data for ECSS is equal Not all Legacy Data will be Migrated should be the long-term quality goal Six-Sigma is achieved via incremental Good-enough's Start with the most unforgiving data sets Understand and Manage the impact of quality compromises Fix the process and the data Manage Functionals and IT by Metrics Clean data is is an an Air Force Responsibility 12

13 Current Life Cycle Systems Engineering Systems Engineering LCSE (Pre-Milestone C) System Mgt, Requirements Mgt, Verification & Validation, Technical Control & Mgt, Configuration Mgt, SOR & SOS Certification, Planning, Design, Risk Mgt, Inspection & Maintenance Transition Planning Other HSI, AIP & MFOUQA, MQ&A, MOSA, CSI, System Safety, etc LCSE (Post Milestone C) System Mgt, Requirements Mgt, Verification & Validation, Technical Control & Mgt, Configuration Mgt, SOR & SOS Certification, Planning, Design, Risk Mgt, Inspection & Maintenance Weapon System Integrity AVSIP MECSIP FSIP PSIP ASIP Individual Aircraft Management Technical Control & Management: Technical & Design Reviews, Architecture, Tech Maturation/DEMO, Tech Measurements (KPP, TPM, Metrics), Decision Analysis, Certifications, Ops Monitoring, Deficiency Reporting, Training. Inspection & Maintenance: RCM Updates, FEMCA Updates, Reviews, TO Updates (-6 & -06), Workspec & MRRB Updates, CBM+, Prognostics, RAM, System Monitoring, System Performance Analysis. 13

14 Life Cycle Systems Engineering with LCSE-S LCSE (Pre-MS C) System Mgt, Requirements Mgt, Verification & Validation, Technical Control & Mgt, OSS&E, Configuration Mgt, SOR & SOS Certification, Planning, Design, Risk Mgt, Inspection & Maintenance Initiatives with strong interfaces/links with LCSE-S: Systems Engineering PLM Transition Planning Other HSI, AIP & MFOUQA, MQ&A, MOSA, CSI, System Safety, etc Notional POMX, IETM, ECSS MRO (IFS), Data Workbench (EPAE), Prognostics Toolkit, AIT/AMT, CAM, MFOQA LCSE (Post-MS C) System Mgt, Requirements Mgt, Verification & Validation, Technical Control & Mgt, Configuration Mgt, SOR & SOS Certification, Planning, Design, Risk Mgt, Inspection & Maintenance LCSE-S CBM+, RAM, WSIP, Prognostics (RUL/SLA), Diagnostics Monitoring, EAVI, R&M System Performance Metrics, ELMP & FSMP, RCMA/FEMCA, and Corrective Actions or New Requirements Weapon System Integrity Programs (WSIP): ASIP, FSIP, ENSIP, MECHSIP, AVSIP, and Individual Aircraft Tracking (IAT) Technical Control & Management: Technical & Design Reviews, Architecture, Tech Maturation/DEMO, Tech Measurements (KPP, TPM, Metrics), Decision Analysis, Certifications, Deficiency Reporting, and Training. Inspection & Maintenance: Reviews, TO Updates (-6 & -06), PDM Work Specifications, and MRRB Updates 14

15 Life Cycle Systems Engineering - Sustainment Represents the integration of elog21 and AFSO21 Process Improvements and Objectives for: Product Lifecycle Management Condition Based Maintenance Plus/ Prognostics Weapon System Performance Analysis Weapon System Performance Improvement Defines data needs and metrics Key Events Event Thresholds Sensors and Controllers MRO Data Drives Engineering Analysis 15

16 Systems Engineering - Current State 16

17 Monitor - Current State Core Process Map (How is the data monitored?) Allocates Requirements Understand System Perform ace Requirements Review FMECA for Potential Failure Points Determine How Sub-System Must Perform to Meet Requirements Critical Aspects? YES Determine Which Characteristics are Indicators of Performance Determine/Set UCL/LCL on Characteristics Can We Measure? YES Determine How to Measure Characteristics NO NO Auto or Manual Collection? M Transfer to Auto Form A ID Transmission Means ID Transmission Means ID Data Destination ID Data Destination Transmit Data Store Data Retrieve Data Analyze Data Transmit Data Store Data Validate Compare Actual Performance to Predicted/Reqd ID Patterns/Trends Predict Future Events (Failures/Demands) List Analysis Tools Is Performan ce w/in Limits? YES Provide Reports to Users/Mngrs NO Input into SE Process 17

18 Monitor - Future State Core Process Map (How is the data monitored?) Use Common Automated tool to determine criticality (FMECA) which characteristics are indicators of performance, upper and lower bounds, and if it is measurable ID transmission means, data destination, transmit data, store data in automated system Use Tool to retrieve data and ask appropriate/ meaningful questions Compare actual performance to predicted requirements from system performance requirements Use automated method for identifying patterns/trends and to predict future events (failures/demands) Obtain System Performance Reqs Decompose System Requirements ID Critical Components (FMECA) ID Critical Characteristics (Measurable) Set Bounds (U/L) Automate d or Manual Data? A Transmit Data Analyze Data M Digitize Change to digital format utilized by automated systems Validate (Automated) Perf/ Trends w/in Limits? NO Provide Reports to Users/ Managers YES Provide Reports to Users/ Managers Input into SE Process 18

19 Summary Improved Maintenance Data Collection is being addressed by: Transformation Life-cycle Systems Engineering Sustainment Condition Based Maintenance IUID Technology Enablers AIT (Near-Term) PLM ( Near-Term & Long-Term) ECSS (Long-Term) Analysis AFLMA (Near-Term) ECSS Legacy Deconstruction and Migration (Near-Term) 19

20 Questions Briefings/PMR Dunn 9Mar04 20