Patel Work Skills
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Monesh Patel provides a value proposition for himself as an engineering leader with over 20 years of experience. He highlights key strengths such as balancing big picture thinking with attention to detail. He also emphasizes business orientation, people skills, communication skills, and a bias toward continuous improvement. Examples are given of past work leading development of technically challenging imaging systems and serving as chief system architect. As a design for six sigma change agent, he influenced culture change through training others and demonstrating cost savings from various projects.
Patel Work Skills
- 1. Value Proposition as an Engineering Leader Monesh Patel January, 2011
- 2. Value Proposition - Skills Set (why would you want to hire me?) • 20+ years of solid experience in leading advanced technology programs from both Engineering and Business perspectives Key Strengths Optimal balance of “big picture” and “attention to detail” - always seeking balanced timely, cost effective and technically adequate solutions Expertise in product Business oriented perspective development processes Diverse technical Strong people knowledge/experience motivational/mentoring skills Bias toward continuous Self motivated, ambitious, process improvement very quick learner Innovative, Strong problem Strong communication skills solving skills Monesh Patel, 2011
- 3. Work History Demonstrates Growth Functional Breadth, Technical Depth and Flexibility Monesh Patel, 2011
- 4. Performance Scorecard Shows Goal Oriented Achievements Monesh Patel, 2011
- 5. Relevant Experience/Training • 20+ years experience in Engineering Leadership/Program Management • 20 years on DoD programs • 15 years in Infrared/EO systems • MS & BS in Mechanical Engineering • Masters in Business Administration (MBA) • Certified Raytheon Six Sigma Black Belt • Design for Six Sigma, Business Development, Integrated Supply Chain, Lean • Leadership/PM Training • Principles of Finance Leadership • Principles of Program Leadership • Principles of IPT Leadership • EVMS for Project Managers • Strategic Leadership • Systems Engineering Training • Systems Enggineering Certification, California Institute of Technology • Systems Engineer/Architect Certification, Raytheon • UML/SysML tools/methodologies - ARTISAN • Master of ConOps certification, Raytheon Monesh Patel, 2011
- 6. Some Examples of Past Work
- 7. As Chief Engineer, Led Development of Technologically Challenging Helmet Mounted & Handheld Multi-Spectral Networked Imaging Systems HELMET HANDHELD Locate Tank-sized Targets Day and Night 5km Range Day / 1.25km Range Night Detect man-sized target to 150m (goal: 300m) Operate over wide range conditions Provide Target Location Components Laser Range Finder; Laser Markers Provide Form-Factor for Soldier Mobility Vis/NI .4 - 1 µMultispectr al COMMON FEATURES R SW 1.2 – 1.7µ input IR • Provide Multispectral LWIR, SWIR and VNIR Imaging LWIR 8 - 12 µ • Adaptive Low-Power Image Fusion • EZOOM / Super Resolution/ Image Stabilization • Digital Video Recording - Store and Playback Still Images or Video • Provide Warfighter Navigation Component - GPS / Compass / IMU Fused output • Exploit network connectivity between Warfighters - Share real time video imagery across wireless network Monesh Patel, 2011
- 8. As Chief System Architect, Led 25 person Systems Architecture Development Team on the Kinetic Energy Interceptor Program Payload Kill Vehicle : IMU KV : Link : Communication BatteryComm Antenna Comm : Antenna data «precondition» { data IR has been ejected from the Canister GNC : Processor Power Tm Antenna PCU : : Telemetry : Antenna Conditioning Tm IR has reached safe ignition condition } Boost command data Valve : Drivers data «postcondition» command command -Nav Data State Not Achieved { TBD data LDACS : Seeker : KFC/C 1 «uses» } command Nav (TBD) -Commands 1 Boost Fly-out Normal Flow «uses» «postcondition» Stage 3 * «subtype» S2/S3 Separation Not Acheived { TBD } -H&S «subtype» ACS : GPS : Receiver EED : Driver Driver : PCU * command S1/S2 Separation data : Battery FTS S3 : Command Destruct Sequence S2/S3 Separation : KFC/C * Sequence anti-Jam Antenna -Cmd «postcondition» GPS Terminate { Satellite * Mission Terminates } Antenna «postcondition» { The separation of Stage 3 from Stage 2 The IR has achieved the required position/velocity/time state that allows the payload to execute the intended mission. } Context Diagrams Use Cases KEI ConOps + AUR ConUse + Nav (TBD) Nav (TBD) KFC/C KFC/C Target Data Navigation Data Payload Booster Normal Flow Interceptor Round Booster Generate S1 Ignition Cmd S1 Ignition Cmd Sense Vehicle Rate Changes Maintain Guidance Solution Generate Steering Cmds Steering Cmds Ignite S1 Maintain S1 Vehicle Control Canister ytilibaesaeleR ORPTNAC ytiroirP egasseM )DI( ytitnedI egnahcxE noitamrofnI )S ,M ,H ,D ,M ,Y( pmatsemiT noitanitseD noitamrofnI ecruoS noitamrofnI )noitalumiS ro laeR( rotacidnI noitarepO epyT noitamrofnI noitacifissalC ytiruceS noitamrofnI nommoC lanretxE )zA ,yA ,xA( noitareleccA )zV ,yV ,xV( yticoleV )Z ,Y ,X( noitisoP emiT ytidilaV etatS tcejbO taerhT 1 emiT hcnuaL taerhT )gnoL ,taL( tnioP hcnuaL tnioP hcnuaL taerhT 1 1 emiT tcapmI detciderP tnioP tcapmI detciderP tnioP tcapmI detciderP + Functional Flows, Decomposition R Reliability Pssk Probability of Single-Shot Kill Piperf Probability of Interceptor Performace Psup Probability of Engagement Support + Implement Steering Cmds 1 1 1 Change Vehicle Attitude 1 1 )deilppuS fI( epyT elissiM Pflyout Pdiscrim Pacq Probability of Probability of Phit Probability of Pkill )deilppuS fI( yrogetaC elissiM rebmuN kcarT Collect Payload H&S data Collect booster H&S tropeR kcarT taerhT IEK - REI Interceptor Flyout discrimination Acquisition Booster H&S Data Vehicle data downlink Generate Downlink data Element Use Class 1st stage performance 2nd stage performance 3rd stage performance Pfor Probability of Field of Regard Containment Pskr Probability of Seeker Performance Cases Sequence Diagrams Function/Requirement Diagrams Allocation Trees A-Spec, March 15 ID'd in Func Affected Analysis 06, Func Reqmt H/W or Perf Specific Verificati needed Referenc UC Step Use Case Text Func Veiw No. Requirement Text Subsystem S/W Inter ation(s) on or link e Other Artifacts + 3.3.3.2-01.02.05.01.01 The Interceptor Round Payload The Interceptor Round PIDS,IM initiates Command S1 Ignition Payload shall initiate CIDS, Command S1 Ignition Payload Payload SW Func CIDS Yes 3.2.1.3 DA 3.3.3.2-01.02.05.01.02 The Interceptor Round Payload The Interceptor Round sends the S1 Ignition Command Payload shall send the S1 PIDS,IM Message to the Interceptor Ignition Command Message CIDS, Round Booster to the Interceptor Round Payload 3.3.3.2-01.02.05.01.03 The Interceptor Round Booster ignites Stage 1 Booster The Interceptor Round Booster shall ignite Stage 1 Payload HW I/F CIDS PIDS,IM CIDS, Payload Yes 3.2.1.3 DA Trades List Booster HW Func CIDS Yes 3.2.1.3 DA 3.3.3.2-01.02.05.01.04 The Interceptor Round Payload The Interceptor Round 3.2.1.2 senses Vehicle rate changes Payload shall sense Vehicle PES; rate changes Payload SW PIDS,IM CIDS, Payload Func CIDS Yes 3.7.1.2.3 Execute KEI Engageme nt Traceability of Functions to Requirements Functional, Interface, Performance, Product Requirements Combined Object Oriented AND “Traditional” Methods Used for Requirements Development Monesh Patel, 2011
- 9. As Design for Six Sigma Change Agent, Planned Product Development Culture Change - SAS DFSS “Diffusion” Plan June 2005 Mar 2006 Dec 2006 • Too many product • DFSS enables SAS drive to failures/over run • Fewer product failures/improving Mission Assurance programs/customer Cost, Schedule/Satisfied Customer Mission organization dissatisfaction • Increased Awareness of DFSS in LT, PM, • DFSS Processes • Limited understanding of Engg. – benefits highlighted through real embedded in our culture DFSS & associated benefits examples and successes; DFSS Webpage created People •100 % of Eng/PM population • Eng./PM Population untrained • Key members of Eng/PM trained and practicing population trained • Programs don’t need •Although Gate 5 requires DFSS •Program Goal Focused DFSS planning starts before Gate 5; mandatory elements – separate DFSS plans – DFSS plan – Tool Focused DFSS VOC, Cost-Technical Optimization, Risk elements embedded in EMS deployment Management, Performance Predictability Process • DFSS Tools not easily •DFSS tools rolled out and available to all • DFSS metrics used for data available (through webpage?) driven decisions • DFSSMetrics rolled out to • Need for special SME network • Inadequate predictive metrics programs diminished – SMEs liberally in place distributed • SME network developed and flowed out • No SME network • DFSS Team Established – Center POCs • Organizational ownership • Part Time DFSS Champion engaged of DFSS Structure • SAS LT flows down DFSS Expectations KRA Monesh Patel, 2011
- 10. As Design for Six Sigma Change Agent, Influenced Product Development Culture Change by Showing the Gains 1. Cost Reduction Opportunities from CAIV/DTC: $4.5M 2. OLI DFSS Blitz: $3.85M Achieve Affordability • Cost & Schedule Targets • Predictable Performance • Problem Prevention • Sustaining Gains • Customer Satisfaction DFS Robust S Producibilit Performanc y e 1) Predictable Performance 1) Cost Reduction Opportunities from DFMA: 2) Cost Avoidance from SDM for SNR: $215K $250K 3) Risk Reduction 2) Risk Reduction Net Financial Benefit of DFSS projects: $9M Total for ALL Expert projects: $16M Continue to Drive DFSS into SBRS Engineering Culture: 3 more DFSS Expert Projects Planned – spawning many more Specialist projects
- 11. As Design for Six Sigma Change Agent, Influenced Product Development Culture Change by demonstrating how to design product (to adequately meets customer needs over lifetime) QFD (Functional Analysis): Prioritization Matrix: KPPs Translated to “Pugh Like” Selection understand customer Functional Requirements Process Picks Sensor priorities (VOC) to architecture selection Architecture Prioritization of Voice of the Customer QFD Translates VOC to Ranked Design Parameters ) only ons Weighted ed noti Score conceiv Alternative Rating Score Reqs Matrix Cost Scanner +/- 5 0.5 er (pre e l Valu se ity ance 10% Pushbroom + 7 0.7 Custom ign Reu Decima Suitabil Perform l Tota le /Des Step Starer - 2 0.2 e of tive Mission Schedu Cost Voic Risk Past Row Rela TRL Sensor Design Reqs (HOWS) CRITERIA Rank Cost 5.0 0.2 1.0 0.2 1.0 1.0 8.4 0.10 5 Mission Suitability 0.2 0.2 0.2 0.2 0.2 0.2 1.2 0.01 7 OLI Customer Criteria Prioritization Results Schedule 5.0 5.0 5.0 1.0 5.0 1.0 22.0 0.26 1 Voice of Customer (preconceived notions only) Risk 1.0 5.0 5.0 5.0 0.2 1.0 5.0 0.2 0.2 1.0 1.0 5.0 7.6 22.0 0.09 0.26 6 1 Mission Suitability Scanner +/- 5 0.05 TRL/Design Reuse 1.0 5.0 0.2 5.0 1.0 1.0 13.2 0.16 3 Past Performance 1.0 5.0 1.0 1.0 0.2 1.0 Grand Total 9.2 83.6 0.11 1.0 4 Past Performance 1% Pushbroom +/- 5 0.05 11% Cost Relative Ranking Scale: 10.0 = Much more important Must Haves MS 10% Step Starer +/- 5 0.05 Component Matrix 5.0 = More important Satisfiers Cost, VOC, PP 1.0 = Equally important Delighters Sched, Risk, TRL/Reuse 0.2 = Less important Mission Suitability 0.1 = Much less important 1% Schedule Scanner +/- 5 1.3 26% Pushbroom + 7 1.82 TRL/Design Reuse Step Starer - 2 0.52 16% Functional Architecture VOC Scanner +/- 5 0.5 Risk, Schedule are Schedule 27% 10% Pushbroom + 7 0.7 Step Starer - 2 0.2 Delighters Customer Risk 26% Scanner Pushbroom +/- + 5 7 1.3 1.82 Needs (WHATS) Step Starer - 2 0.52 Voice of Customer (preconceived notions only) Risk 26% 9% TRL/Design Reuse Scanner +/- 5 0.8 16% Pushbroom + 7 1.12 Step Starer - 2 0.32 Past Performance Scanner +/- 5 0.55 Sensor Design 11% Pushbroom + 7 0.77 Step Starer - 2 0.22 Requirements Weighted Customer Total Score 100% Scanner Alternative Pushbroom Step Starer Rating Score 35 47 17 Score 3.7 5.16 1.51 Gets WO W and says DTC/CAIV, DFMA: Balance Cost, Performance Risk, Producibility Statistical Design Methods and Schedule against Customer account for variability and Care Abouts predict performance SNR Cost-Worth Ratio of Componenents =235 Probability that SNR is Larger T han X 4.0 SMR Number in Each Bin 1 SNR Histogram D 3.5 800 FMEA: Refine Req. Cumulative Probability 3.0 Cost/Worth Ratio 600 SNR=352 Risks/Anticipate 2.5 (50% margin) 400 0.5 Prob= 62% 2.0 Increasing Opportunity Problems 1.5 * 1.0 200 0.5 00 200 400 600 0 0.0 VNIR Detector PM Tooling and EM Tooling and PM Internal Test Equipment Test Equipment Mainframe Assy Motor Controller SWIR Detector Cryo Radiator Processor Processor Aux Controller Assy less Motor Chassis Optics Assy Power Supply Calibrator Assy Scanner 200 300 400 500 Dewar Assy Signal Digital Cables Scan Mirror SNR On Board Assy SNR Assy Component
- 12. As IPT Lead, Led Demonstration of Challenging Sensor Pointing and Control Requirements for Geosynchronous Orbit Application Line of Sight Knowledge < 14 micro radians End to End Simulation of Image Navigation and Registration Monesh Patel, 2011
- 13. As Lead Systems Engineer for SBIRS Low FPAs, Allocated Requirements and Predicted Product Yields to Ensure Program Success Business Case: Focal Plane Array yield is a big cost and schedule driver: poor lot yield can add millions $ and result in up to 2 years of schedule delay – This can kill the program. Using SDM to account for process variations and predict performance/yield ensure program success. Key Performance Allocate Predict FPA Yield Characteristics for FPA Requirements to Yield Identified using Statistical Components for Refine Prediction Methods (for Pixel Operability for: Adequate Pixel Model with combined pixel Operability using Actual Test Data NEDQ operability) Response Uniformity Statistical Methods Offset Uniformity (account for Linearity variability) Shorts/Opens Monesh Patel, 2011