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

SLIDE 1

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

SLIDE 2

2

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

• rient data to make sound business and warfighting decisions.

(DON, 2016)

SLIDE 3

3

Sust Sustainme ainment nt Lif Life Cy e Cycle

New Weapon Systems Retiring Weapon Systems R&D Production

In-Service Weapon Systems

O&S Disposal

Life Cycle Cost Weapon System

CMV-22 F-35 B/C F/A-18 E-G CVN 68 LHD 1 LCC 19 EMALS/ AAG MLP/ESD/ AFSB/ESB AV-8B E2-D MV-22 P-8A KC-130J H-46 FFG 7 H-60 B/H F/A-18 A-D LHA 6 MH-53E P-3 EA-6B H-60 R/S CH-53K MQ-25A FF OHIO REPL SSC MQ-8C C-2A MQ-8B SSN 21 CG 47 LSD 41 RQ-21A DDG 1000 CVN 78 MQ-4C LX(R) SSN 774 LCS 1&2 JHSV/EPF DDG 51 LPD 17 LCAC LCU PC 1 MCM 1 SSN 688 SSBN 726 SSGN 726 AH-1W CH-53E E-6B NGJ MALD-N AARGM-ER AAG IRST LRASM AIM-9X BLK 2 SDB II JAGM AARGM TOMAHAWK APKWS E-2C AIM-9X BLK 1, AMRAAM LASER JDAM MAVERICK HARM, JSOW JDAM HARPOON SLAM-ER

92 TMS Variants 4,106 Total Aircraft 15.56 Average Aircraft Age (as of Dec 18) 1,085,966.3 CY18 Flight Hours Executed

VH-92A H-1 Y/Z

Naval Aviation Data

SLIDE 4

Naval Aviation Data

MC FMC

Cost Equipment

Aircraft Systems Components Sensors

People

Maintainers

Supply Training

Pilot & Aircrew Maintenance

Ordnance Flight

Utilization

OOR

Depot

Aircraft Components

Engineering

4

SLIDE 5

Naval Aviation’s Big Data Problem

5

Data Storage

MDW 2009 MDW 2019 SDR

Maintenance Data Warehouse 2009 Maintenance Data Warehouse 2019

Sensor Data SDR 2-TMS

SLIDE 6

Moving From Reactive to Proactive to Predictive

• Supply Stock Out Predictions
• Predictive Maintenance
• Maintenance Operations Centers MOC
• End-to-End Supply Analysis
• Maintainer Baseball Card
• Statistical Deviations & Variance
• Dynamic Scheduling
• Schedule Maintenance Optimization
• Emergent requirements
• Aircraft on Ground AOG
• Analytical Collaboration
• Edge Analytics
• Condition Based Maintenance Plus

(CBM+)

• Supply Optimization Models
• Simulation Models
• Machine Learning (DTS)
• Flight Hour Execution Models

REACTIVE

Act on Known Issues

PROACTIVE

Manage Known Risk

PREDICTIVE

Anticipate and Preclude Risks

NAVAIR Areas of Responsibilities

• Acquisition of Systems
• Sustainment of Systems

READINESS

6

SLIDE 7

Near Term/ Tactical Improvement Long Term/ Strategic Improvement

Aircraft Down NOW…get it back up Self-Service Business Intelligence

• Near real-time insight into A/C status
• Aircraft Management Dashboard (AMDB)
• Air Boss’ Report Card
• Maintenance E2E
• RCB Degraders “one list’
• AOG HUG
• AOG Candidates/Coordination/Actions DB
• Visibility into supply chain to expedite
• Supply Analysis Tool E2E
• Wholesale/Retail Stock
• ILOB NAVSUP Due In PO/PR
• RFI/BCM/DIFM
• Failures/Removals/Backorders
• SORT Model – Optimize component deliveries
• Reliability Control Board (RCB)
• Degrader ‘one list’
• Initial Root Cause

Posture FUTURE aircraft readiness IT Analytical Systems

• Statistically Trend Performance at all levels:

Address systemic degraders

• Vector
• Aircraft Va: Mission degraders, supply, cost,

utilization metrics statistically heat mapped

• Components Vc: FRC Level III components
• Weapons Vw: Weapons Logistics Analysis
• DECKPLATE
• Aviation Data Warehouse
• DECK-ALS
• DECK ETR
• LOGCELL – NAVSUP COGNOS Environment

Exercised Within a Digital Environment

7

Readiness Digital Products

SLIDE 8

Enterprise Analytical Collaboration

8

American Airlines Integrated Operations Center

SLIDE 9

Sensor Data Repositories Sensor and Maintenance Data Collection Enterprise Infrastructure

• 54 NAVAIR Mid-Tiers Servicing

CVNs/LClass/MALS/NAS INCONUS and OCONUS Locations

• Data Transport via GOTS (JDMS) and

COTS Solutions (evaluating COTS/Open Source ESB Alternatives)

Scalable Data Platform Data Warehouse Integrate Data and Analytics Enterprise Wide Alerts Notifications/Monitoring/Feedback Enterprise Access to Data and Analytics

Proof of Concepts Validated Other Supporting Systems/Processes

Secure Access and Integration

Pilots determine best-of-breed COTS / GOTS / Open Source

• Automatic identification technology (AIT)
• Serialized item management (SIM) / Asset Visibility / ERP
• Portable Electronic Maintenance Aids (PEMAs)
• Diagnostics / Ground Stations
• Item unique identification (IUID)
• Automated information systems (AISs)
• Interactive Training and Technical Manuals

CBM+/Enterprise IDE

Smart Aircraft Data Repositories Data Marts

ESB Enabled

Global visibility of data in motion ASTATS

Enterprise RCM / CBM + Focus

RCM Overview Entry Level Apprentice I Basic knowledge of RCM; Conduct RCM Analysis in conjunction with Journeyman Journeyman II Able to Perform RCM Analyses Independently & Defend Analyses Master III Able to Approve RCM Analyses, Perform Advanced Analyses, Mentor Analysts Current State

• Designed to teach Entry

level personnel (GS 7-9)

• CLL 030 RCM
• Class Desk APMSE / APML

Orientation (20 minute introduction to RCM/ CBM+)

• DAU Training
• Supervisors develop

Entry-level Development Plan

• RCM Fundamentals class

(CLIO -671-113)

• Expected to have high-

level knowledge of the NAVAIR 00-25-403

• Tested via verbal exam
• Requirements to gain

certification

• Minimum 1 year performing

RCM analyses

• Demonstration of knowledge of

RCM

• Detailed knowledge of the

NAVAIR 00-25-403

• Demonstration and

understanding of basic Reliability Analysis Methods

• Verbal exam
• Advanced RCM Analysis

(CLIO-671-210)

• Minimum 1 year

performing RCM as Journeyman

• Knowledge of complex

RCM tools/concepts

• Verbal exam
• Additional Training
• R&M Advanced Methods
• f RCM (Hamlin course)

Future State

• RE-21 Training Video

(projected release Dec 2018)

• Performance-Based training vs. PPT and dependence on OJT that is inconsistent and incomplete
• Requirement to demonstrate abilities vs. 1 year in job
• 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

Results in 2 hrs of Training FY17 - 74. FY18 - 57 Results in 24 hrs of Training Results in x hrs of Training Results in x hrs of Training

Change the ratio of MX Policy Processes Roadmaps Training

9

SLIDE 10

Big Data, Data Science, and the U.S. Department of Defense

Dissertation by

• Dr. Roy Lancaster

10

SLIDE 11

• 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
• ccupations 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
• ccupation?

The Debate

11

SLIDE 12

• 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?

Data The Next Natural Resource

12

SLIDE 13

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

SLIDE 14

Literature Review

Important Findings

• Data scientists are professionals at gleaning actionable information from large

amounts of data. Data scientist use traditional math, science, and statistical techniques along with modern analysis software to glean actionable information from large data sets (Davenport & Patil, 2012).

• Zhu and Xiong (2015) explained there is a new discipline emerging called data

science and there are distinct differences between the established sciences, data technologies, and big data.

• The relationships between big data and data science require further exploration

and the underlying principles of data science need to emerge to fully understand the potential of data science (Provost & Fawcett, 2013).

• The commercial data science occupation continues to emerge and there is no

federal occupation for a data scientist. The perceived data science skills are encompassed in several federal occupations.

• There is a perceived shortfall of analytical talent in the United States and DOD
• rganizations.

14

SLIDE 15

Results

• Several iterations of reading and coding were required in the data reduction process

and the researcher was looking for tones, impressions, and credibility of the collected data while keeping in the forefront how the collected data related to the research questions in this study.

• With continual reading and synthesizing of the collected data recurring topics and

patterns emerged. The coding structure was refined as the transcripts of the analysts and focus group interviews were coded and analyzed and resulted in the final coding structure.

15

SLIDE 16

Resulting Themes

• Big Data: The BZC is a complex organization with many disparate data systems generating large data

sets and is struggling with gleaning actionable information from the data sets. The BZC supports Moorthy’s (2015) definition that big data is a collection of data sets that become so large and complex that it becomes difficult to process using traditional relational database tools and traditional data processing applications.

• Access to quality data: The analysts indicated infrastructure and policies are constraining access to
• data. Additionally, the data that is accessible lacks accuracy and completeness. A BZC data

governance strategy that includes how analysts get access to quality data to support mission requirements is warranted.

• Metrics: The analysis of the collected data suggests a theme of metrics and the BZC places emphasis
• n managing their business through the analysis of metrics. The analysts proclaimed they spend a

significant amount of time pulling data together and creating metrics for their leadership.

• Infrastructure: Legacy and disparate systems: The analysis of the collected data suggests the BZC

has sections of their business with modern computer infrastructure and analysis capabilities but their business is also constrained in the ability to conduct enterprise big data analysis partially due to outdated or legacy information systems, infrastructure, and many disparate systems.

• Data analysis processes: The analysis of the collected data suggests the BZC is mostly building and

analyzing reactive metrics on historical data with small pockets of predictive analytical capability.

16

SLIDE 17

Resulting Themes

• Management: Harris and Mehrotra (2014) proposed leadership is a top management challenge in the ear of big
• data. Companies may need to train incumbent managers to be more numerate and data literate as well as hire

new managers who already possess the skills to manage and lead in the era of big data and data sciences. Participants provided statements regarding how leadership consumes analysis information and difficulties with determining what metrics to use to measure the success of the BZC. The BZC is a military organization that rotates its military leaders often, and the participants suggested this creates challenges for BZC analysis.

• Data Science Skills: The participants agreed to scholarly views of the perceived data science skills and

unanimously agreed that the perceived data skills are immature within the BZC. Six analysts agreed that data science is a unique role beyond that of a traditional analyst and two analysts suggested the role of the data scientist does not have to be unique and three analysts were unsure. Additionally, the participants acknowledged that there is no data science occupation within the Federal OPM job structure and they expressed that there are very few analysts within BZC with the complete range of the perceived data science skills.

• Access to software: The analysis of the collected data suggests there are some sections of the BZC leveraging

advanced analytical software. However, the collected data suggest the BZC has limited advanced analytical software available to most analysts. Information technology policies appeared as a significant constraint preventing access to modern analytical software.

• Access to training: The analysis of the collected data suggest the data science skills of civilian analysts are

immature at the BZC. The participants expressed there are very few analysts training opportunities and even less training opportunities related to the perceived data science skills. Some of the participants explained that they are fully qualified and meeting their OPM job series requirement but acknowledged their OPM occupational

requirements do not require data science skills training

17

SLIDE 18

Resulting Themes

• Competing for Talent: The results of the exploration suggest the BZC has experienced some success

in attracting analysts in some locations but is also experiencing difficulties in attracting this talent. The participants expressed concern about their people being sought after by competing industries and the process to bring new hires into their organization is too slow.

• Domains: A common theme in data science research suggests that for data scientists to generate

business value, they will need to work closely with domain experts in the organization (Granville, 2014). The importance distinction between data science and business domain understanding was a theme that was present in this research. The participants offered their perceptions regarding the data science role within DOD organizations and the importance of data science and business domain connections. Some participants proposed that data scientists should be proficient in the business domain while other participants suggested data scientists could serve the business best by conducting the advanced analysis and then provide the results to a business domain analyst.

• Organizational structure and culture: When discussing challenges associated with conducting big

data analysis within the BZC a theme of organizational structure and culture was apparent, and determining how to best employ data scientists and how to create a culture that shares data and information is warranted at the BZC.

18

SLIDE 19

Recommendations to DOD Organizations

• Create data science teams by combining data science related federal occupations
• Assess analytical talent and data domain understanding across the workforce
• Develop an action plan to train the gaps
• Evaluate policies that are prohibiting the use of commercially evolving advanced

analytics training (ie… Linked-In Learning, Coursera, Analytics Professional CAPS Certification

• Influence the skills requirements sections of position descriptions/job

announcements to bring in data science skills

• Influence digital and analytics skills in all levels of management
• Evaluate policies that are prohibiting the use of modern data science analytical

software (many are open source, Python)

• Develop an action plan to integrate and share quality data
• Evaluate current information systems architecture. Does it currently support

advanced analytics?

• Explore cloud based, rapidly scalable architectures that can bring critical data

together and take advantage of modern analytical tools sets in the same environment.

19

SLIDE 20

Data Domain & Data Science

T/M/S Core Analysis

Domain Expertise

Advanced Analytics & Innovation

Data Science & Software Innovation

20

SLIDE 21

Recommendations to DOD Organizations

• Because there is no formalized data science occupation within the DOD workforce and

because the DOD is competing scarce data science talent. Creating data analysis teams that comprise the breadth of data science and domain understanding is a reasonable

• approach. DOD organizations should evaluate the abilities of their existing analysts in

domain understanding and data science skills to support an action plan to mature analytics within their organizations.

21

SLIDE 22

NAVAIR Analytical Talent Management

22

Data Science Skills

SLIDE 23

Data Science in the Federal Government

March 2019

23

SLIDE 24

Conclusion & Opportunities

24

• F-35, V-22, C-130 are bringing us together (F-35 is an analytical challenge like no other)
• Leverage common data science & analytical forums
• Leverage service post-graduate academies (AFIT, AAG, NPS…)
• NAVAIR data science community of interest (brown bags)
• Leverage cross-service “challenges” (hackathons, data challenges…)
• Joint Artificial intelligence Center (JAIC)
• OSD Strategic Capabilities Office (SCO)
• Rotation of employees (especially developmental employees)

Thank you!

SLIDE 25

Readiness

25

SLIDE 26

Agenda

• Introduction 1-3

– Designated navy analyst in 1990 (things have changed), Education & Experience – Naval aviation readiness problem & strike fighter warning order

• Nirvana story 1-3

– A perfect set of analytical systems – The reality, quotes regarding sense of urgency for data analytics – Commercial competition is re-shaping data analytics

• Naval Aviation’s Data & Analytics Journey 15-18

– Disparate transactional and analytical systems – Massive amounts of data arrived (problem & opportunity) – Reactive, Predictive, Prescriptive – Lessons learned (balance between outsourced and government tools) – Enterprise analytical collaboration

• Big Data, Data Science & the U.S. Department of Defense 15-18

– What is a data scientist? – Research (methodology, case study, findings and conclusions)

• Conclusion

– Opportunities to work together (F35, V22, C130) – DOD consortiums regarding data analytics/science (JAIC, OSD SCO…) 26