• 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

$$500bn
15%
50%
200%
 

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

Predictive

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

Unscheduled

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

Engineering
  ✔  
Program Management
  ✔  
Aftermarket
  ✔  
Supply Chain
  ✔  
Maintenance
  ✔  
Inventory
  ✔  
Security
  ✔  
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
Regulators
Insurance & Risk Management
OS/Application Development
Product & Mission Assurance
Lessors
Warranty Providers
Cloud/Analytics Platform Vendors
Maintainers


 

Customer Impact

Operational

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
 

Financial

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

Pilot/Operator

Report health state to pilot

 

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

Maintainer

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

Other

Support incident/accident investigation

 

Support R&D

Leadership

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
COO

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
Advisor

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
Advisor

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
Advisor

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
Advisor

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
Advisor

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
Advisor

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

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.