Aircraft Fleet Readiness Presented to: AFCEA/LOA Logistics IT - PowerPoint PPT Presentation

Aircraft Fleet Readiness Presented to: AFCEA/LOA Logistics IT Summit By: Dr. Roy Lancaster NAVAIR Readiness Analysis Division Director 04 June 2019 Distribution A: Cleared for public release

BLUF • If the DON does not take proactive action to adapt and further expand data-driven innovations: • The DON risks falling behind our adversaries in the speed that we can obtain relevant and accurate data, as well as in the ability to orient data to make sound business and warfighting decisions. (DON, 2016) 2

Naval Aviation Data Sustainme Sust ainment nt Lif Life Cy e Cycle 92 TMS Variants 4,106 Total Aircraft 15.56 Average Aircraft Age (as of Dec 18) 1,085,966.3 CY18 Flight Hours Executed Life Cycle Retiring Weapon New Weapon Systems Cost In-Service Weapon Systems Systems Production O&S R&D Disposal IRST AARGM MV-22 CH-53K H-1 Y/Z MH-53E P-3 KC-130J MQ-8B A H-1W AV-8B C-2A H-60 B/H VH-92A F/A-18 A-D AAG LRASM MQ-8C TOMAHAWK LX(R) Weapon System APKWS DDG 51 E-2C MQ-4C LHA 6 H-60 R/S EA-6B E2-D F/A-18 E-G H-46 PC 1 F-35 B/C E-6B C H-53E MQ-25A NGJ LPD 17 LCAC SSN 21 FFG 7 CVN 68 HARM, JSOW MCM 1 FF MALD-N EMALS/ P-8A AIM-9X BLK 2 RQ-21A LCU CG 47 SSN 688 SSN 774 LHD 1 JDAM AIM-9X BLK 1, A MRAAM OHIO REPL AARGM-ER AAG CMV-22 SDB II DDG 1000 LSD 41 SSBN 726 LCS 1&2 LCC 19 LASER JDAM HARPOON MLP/ESD/ SSC JAGM CVN 78 SSGN 726 JHSV/EPF SLAM-ER MAVERICK AFSB/ESB 3

Naval Aviation Data People Equipment Maintainers Aircraft Systems OOR Components Sensors Supply Depot Aircraft Components MC FMC Training Pilot & Aircrew Flight Maintenance Utilization Ordnance Cost Engineering 4

Naval Aviation’s Big Data Problem Data Storage Sensor Data SDR 2-TMS Maintenance Data Warehouse 2019 Maintenance Data Warehouse 2009 MDW 2009 MDW 2019 SDR 5

Moving From Reactive to Proactive to Predictive PREDICTIVE Anticipate and Preclude Risks PROACTIVE Manage Known Risk REACTIVE Act on Known Issues • Condition Based Maintenance Plus (CBM+) • Supply Stock Out Predictions • Supply Optimization Models • Predictive Maintenance • Simulation Models • Maintenance Operations Centers MOC • Machine Learning (DTS) • End-to-End Supply Analysis • Flight Hour Execution Models • Maintainer Baseball Card • Emergent requirements • Statistical Deviations & Variance • Aircraft on Ground AOG • Dynamic Scheduling • Analytical Collaboration • Schedule Maintenance Optimization • Edge Analytics • Acquisition of Systems NAVAIR Areas of Responsibilities READINESS • Sustainment of Systems 6

Readiness Digital Products Near Term/ Tactical Improvement Long Term/ Strategic Improvement Aircraft Down NOW …get it back up Posture FUTURE aircraft readiness Self-Service Business Intelligence IT Analytical Systems • Near real-time insight into A/C status • Statistically Trend Performance at all levels: • Aircraft Management Dashboard ( AMDB ) Address systemic degraders • Air Boss’ Report Card • Vector • Maintenance E2E • • Aircraft Va: Mission degraders, supply, cost, RCB Degraders “one list’ • AOG HUG utilization metrics statistically heat mapped • • AOG Candidates/Coordination/Actions DB Components Vc: FRC Level III components • Weapons Vw: Weapons Logistics Analysis • Visibility into supply chain to expedite • DECKPLATE • Supply Analysis Tool E2E • • Aviation Data Warehouse Wholesale/Retail Stock • • ILOB NAVSUP Due In PO/PR DECK-ALS • • RFI/BCM/DIFM DECK ETR • Failures/Removals/Backorders • SORT Model – Optimize component deliveries • LOGCELL – NAVSUP COGNOS Environment • Reliability Control Board (RCB) • Degrader ‘one list’ • Initial Root Cause Exercised Within a Digital Environment 7

Enterprise Analytical Collaboration American Airlines Integrated Operations Center 8

CBM+/Enterprise IDE Sensor and Maintenance Data Collection Enterprise RCM / CBM + Focus Enterprise Infrastructure Roadmaps Processes Policy • 54 NAVAIR Mid-Tiers Servicing CVNs/LClass/MALS/NAS INCONUS and OCONUS Locations • Data Transport via GOTS (JDMS) and Entry Level Apprentice I Journeyman II Master III RCM Basic knowledge of RCM; Able to Perform RCM Analyses Able to Approve RCM C hange the ratio of MX Conduct RCM Analysis in Training Independently & Defend Analyses, Perform Advanced Overview conjunction with Journeyman Analyses Analyses, Mentor Analysts COTS Solutions (evaluating COTS/Open Current • Designed to teach Entry • RCM Fundamentals class • Requirements to gain • Advanced RCM Analysis level personnel (GS 7-9) (CLIO -671-113) certification (CLIO-671-210) State • CLL 030 RCM • Expected to have high- • Minimum 1 year performing • Minimum 1 year • RCM analyses Class Desk APMSE / APML level knowledge of the performing RCM as • Demonstration of knowledge of Orientation (20 minute NAVAIR 00-25-403 Journeyman • RCM • introduction to RCM/ Tested via verbal exam Knowledge of complex Source ESB Alternatives) • Detailed knowledge of the CBM+) RCM tools/concepts • NAVAIR 00-25-403 • DAU Training • Verbal exam Demonstration and • Supervisors develop understanding of basic Results in x hrs of Training Entry-level Development Reliability Analysis Methods • Additional Training Plan • Verbal exam • R&M Advanced Methods ASTATS of RCM (Hamlin course) Results in 2 hrs of Training Results in x hrs of Training FY17 - 74. FY18 - 57 Results in 24 hrs of Training Future State • RE-21 Training Video • Performance-Based training vs. PPT and dependence on OJT that is inconsistent and incomplete (projected release Dec • Requirement to demonstrate abilities vs. 1 year in job 2018) • Make/buy courses for FMECA development/review, data gathering/cleansing/analysis, statistical analyses, etc. • AIR 6.7 / AIR 4.0 co-develop the training continuum and release FY19 Enterprise Wide Alerts Notifications/Monitoring/Feedback Global visibility of data in motion Integrate Data and Analytics Smart Aircraft Data Repositories Sensor Data Repositories Data Warehouse Proof of Concepts Validated Scalable Data Platform Enterprise Access to Data and Analytics Data Marts Other Supporting Systems/Processes ESB Enabled • Automatic identification technology (AIT) Secure Access and Integration • Serialized item management (SIM) / Asset Visibility / ERP • Portable Electronic Maintenance Aids (PEMAs) Pilots determine best-of-breed COTS / GOTS / Open Source • Diagnostics / Ground Stations • Item unique identification (IUID) • Automated information systems (AISs) • Interactive Training and Technical Manuals 9

Big Data, Data Science, and the U.S. Department of Defense Dissertation by Dr. Roy Lancaster 10

The Debate • Data science is a newly forming occupation and is crucial to all sectors of the U.S. economy. Vs. • Data science is nothing new. It is comprised foundational occupations that have evolved because of the amount of data that is now available and the advances in technology (hardware & software). • Should the U.S. Government and the DOD establish a data science occupation? 11

Data The Next Natural Resource • Clear competitive advantages to companies with high analytical capability & companies are rapidly realizing the need for data scientists. • Academia & vendors are rapidly developing data science programs. • Evidence of a new “sexy” career field with significant shortage and competition for advanced analytical talent. White House named first “Chief Data Scientist” • There is confusion about a data scientist occupation and at the same time it has been labeled the most in demand and potentially rewarding job three straight years (over physicians, lawyers). • Confusing – data science is intertwined with other occupations & concepts. • Does the DOD need to advance their analytical capability? • Should the DOD pay attention to the data science occupation? • Should a federal occupational job series for a data scientist be established? 12

Research Purpose The purpose of this qualitative case study was to explore how DOD employees conduct data analysis with the influx of big data. An unidentified U.S. Air Force command was selected by the researcher as the case study organization to support this study; The Bravo Zulu Center (BZC). • This research explored the emerging commercial data scientist occupation and the skills required of data scientists to help determine if data science is applicable to the DOD. • This research sought to further define the skills required of data scientists to help enable their effectiveness in modern organizations with specific emphasis aimed at the DOD. Primary Research Question 1: How does the Bravo Zulu Center glean actionable information from big data sets? Primary Research Question 2: How mature are the data science analytical skills, processes, and software tools utilized by Bravo Zulu Center analysts? 13