• AppliedEA
    Operational Quality for a Mobile World
    Increase availability rates
    Reduce operational costs
    Monitor, analyze, and benchmark
  • What we do
    • Enhance system availability
    • Reduce failure rates
    • Minimize operational costs
    • Increase maintenance effectiveness
    • Facilitate efficient parts replacement
    • Maximize performance
    • Meet quality control and regulatory standards

Innovate maintenance efforts beyond pen & paper, visualization tools or data science analysis

Maintenance has an important corporate impact


Mission Completion A planning issue

Affordability An acquisition issue

Availability An operations and logistics issue

Liability Exposure A regulatory and financial issue

Customer Retention A business issue

Mobile Machines are changing the world

The global Mobile Machines sector is large and growing exponentially. Relevant systems include manned and autonomous aircraft, ground vehicles, robots, space craft, heavy equipment, industrial & mining apparatus, trains & light rail and maritime vessels


Maintenance is the largest and most important budget item associated with Mobile Machine ownership and operation

Despite substantial capital investments, failure rates of Mobile Machines are currently 10-60%
Maintenance Challenge

AppliedEA monitors and analyzes critical performance benchmarks of Mobile Machines in order to

  • Enhance system availability
  • Reduce failure rates
  • Minimize operational costs
  • Increase maintenance effectiveness
  • Facilitate efficient parts replacement
  • Maximize performance
  • Meet quality control and regulatory standards

Current maintenance solutions fail


They are expensive and ineffective in meeting operational benchmarks

Scheduled and Preventive

Programmed on a time or usage trigger


Defines a causality-driven model and deduces the subsystem and system MTBF impacts

Vibration Analysis

Model-based, examines a single factor/component and identifies when it breaches operational parameters


Activated by operator reports or other diagnostics

Case Study - Unmanned Aerial Systems

Since 2008, US Customs and Border Patrol acquired 11 UASs as a critical component of its border strategy


Total Cost of Ownership

• $150 million purchase cost

• Annual operating costs equal 40% of the acquisition cost

• 5-year operating costs exceeded $460 million


2 UASs crashed and needed to be replaced

• Significant downtime

• Unrecognized operating costs, planning/supply chain failures, no performance benchmarking

• Operational scope was reduced by 70%


Unrecognized maintenance cost and catastrophic failures led to program shut down

A new approach – Proactive Analytics vs. Reactive Maintenance

A Mobile Machine executing the same maneuver under identical conditions should record exactly a similar performance profile each operation

AppliedEA analytically profiles a Mobile Machine’s normal performance and then compares its previous and current operations to identify sudden or progressive change

By contextualizing performance with external, operational and repair data, impending failures are accurately identified and alerted

As more data is logged, AppliedEA’s diagnostic, predictive and prescriptive results improve in accuracy


Our Process

Efficiancy Gains

Enterprise Functions

Program Management
Supply Chain
Fleet & Asset Management
Fleet Operations
Risk Management
Cyberhardening & Intrusion

Our Differentiators

What sets us apart

Rapid and Easy Deployment

Can work on any type of Mobile Machine

Suitable for both new and legacy systems

No hardware, no need to modify the Mobile Machine, no certifications required

Cloud or on-premise implementation

No need to reskill or hire labor

Supplements existing processes

Secure environment

Definitive Outcomes

Ongoing and immediate feedback as to the reliability status of the Mobile Machine

Integrated descriptive, predictive and prescriptive analysis

Based on advanced Artificial Intelligence and Machine Learning algorithms

Find actionable information cost-effectively regardless of the quantity of data

Utilize existing empiric data

Fully-automated – no need for manual analysis or human intervention

User-friendly dashboard interface and drilldown tools

Key Beneficiary Stakeholders

OEMs & Manufacturers
End Customers
Service Operators
Primes & Integrators
Insurance & Risk Management
OS/Application Development
Product & Mission Assurance
Warranty Providers
Cloud/Analytics Platform Vendors


Customer Impact


Increased Availability
Higher ROI
Heightened User Satisfaction
Improved Reliability
Enhanced Safety
Lower Operational Risk
Reduced Training Burdens
Streamlined Material Flows
Lower Cost Per Operational Hour
Accurate Risk Management
Better Use of Available Data
Cyber Hardening
More Detailed Troubleshooting Techniques
Maximize Available Operational Hours Per Same Budget


Reduced Liability Costs
Decreased Maintenance Costs
Lower Overall Program Costs
Competitive Differentiator
Highlights Improved Past Performance
New Opportunities in Manned and Unmanned Systems Markets

Use Case - Aviation


Report health state to pilot


Improve flight safety by identifying system and subsystem fault prior to failure


Identify and correctly isolate faults


Reduce Could Not Duplicate/Return Tested


Reduce workload


Eliminate maintenance testing time/flights


Plan/optimize maintenance

Integrity Manager

Track actual life/usage


Compare against design spectrum


Enable relifing


Confirm asset airworthiness

Fleet Manager

Asset status awareness


Assess fleet mission capability


Increase availability


Support mission planning


Enable Force Life Management


Enable MFOQA


Increase Sortie Generation Rate

O&S Manager

Identify R&M trends


Identify degraders


Support spares planning


Support PBL


Support training


Support knowledge discovery & continuous improvement


Support incident/accident investigation


Support R&D


Josh Segal
CEO & Founder

First employee and VP at Varonis Systems (Nasdaq: VRNS). Venture capital experience at Exigen Capital, Applied Materials Ventures, WR Hambrecht, and Global Catalyst Partners. Investor in P-Cube (acquired), M-Stream (acquired), Grandis (acquired), and Infinera (IPO). Combat service at Israel Defense Forces.

Gafar Lawal

Managing Director, CTO & Chief Architect at Morgan Stanley. Partner Architect at Microsoft. Chief Technology Architect at Merrill Lynch. Awarded two patents.

Patrick Long
Dir. of Innovation

SVP Aviation Programs at 4M Research. Lean Six Sigma Project Officer at US Army. Maintenance Information Technology Officer at US Army. Special Operations maintenance test pilot at US Army. Grey Eagle Unmanned Aerial System Officer at US Army.

Gen. Mike Hayden

Director of the CIA. First Principal Deputy Director of National Intelligence. Director of the NSA. Four star general at USAF. Commander of the Air Intelligence Agency. Director of the Joint Command and Control Warfare Center. Director of Motorola Solutions. Distinguished Visiting Professor at Oxford University.

Dr. Paul Kaminski

U.S. Under Secretary of Defense. Chairman of RAND Corporation, the Defense Science Board, and Seagate Government Solutions. Director of General Dynamics, The Mitre Corporation, Bay Microsystems, CoVant Technologies, and Johns Hopkins Applied Physics Lab. Advisor to the MIT Lincoln Laboratory.

Dr. Bill Schneider

U.S. Under Secretary of State. Chairman of the Defense Science Board and the Defense Business Board. Director of General Atomics, BAE Systems USA, EADS North America, ABB Susa, MBDA USA, and Selex ES USA. Advisor to the U.S. Departments of Defense, Energy, and State. Advisor to Kurion-Veolia and DSI.

Dr. Tony Tether

Director of DARPA. Director of the National Intelligence Office. Vice President of Science Applications International Corporation’s (SAIC) Advanced Technology Group. Director of Aurora Flight Sciences (acquired by Boeing) and Strobe (acquired by GM). Member of the Army, Navy and Defense Science Boards.

Dr. Rick Lawrence

Head of Machine Learning and Decision Analytics at IBM Watson. Distinguished Research Staff Member at IBM. Head of the Neutronics Methods Group at the Argonne National Laboratory. Recipient of the 2014 INFORMS Innovative Applications in Analytics Award.

Dr. Benjamin Mann

Inventor of Topological Data Analysis. VP at Ayasdi. Program Manager, Senior Scientist and Acting Deputy Office Director at DARPA. Program Officer at National Science Foundation. Faculty member at Harvard University, Clarkson University, and the University of New Mexico.

Matthew Freedman

Advisor to U.S. Pacific Fleet, Defense Intelligence Agency, U.S. Special Operations Command, Department of State, Department of Defense, Department of the Navy, National Security Council, and Office of Management and Budget. White House Transition Director reporting to Secretary of State Colin Powell.