Exploring and Evaluating theProduct Performance – Cost    Design Trade Space     Neal Mackertich & Peter Kraus  Raytheon I...
Design for Six Sigma (DFSS)Design for Six Sigma is a methodology used within IPDS to predict,manage, and improve Performan...
Why Explore & Evaluate the           Design Trade Space?• There are significant cost saving opportunities available  by ex...
Enabling Design Trade Space Exploration & Evaluation         through Critical Parameter Management       A methodology for...
Critical Parameter Management Flows   Identify Critical Parameterss   Build Design Model / Transfer Functions   Conduct St...
Identify Critical Parameters Gain a detailed understanding of thecustomer value equation, productrequirement needs and pr...
Build Design Model / Transfer Function     Mathematically defines critical     parameter as a product characteristic     ...
Conduct Statistical Assessment                                                                                            ...
Conduct Statistical Assessment                                                .027                                        ...
Perform Trade Study Analysis                                                                          Trade Studies Under...
Radar Subassembly CPM Case Studys   High Volume Subassemblys   Mechanical Dimensions Critical to Electrical    Performance...
Radar Subassembly Tree Structure
Design Interface
Dimensional Capability Analysis                                                                                           ...
X Scorecard                 System Level DFSS - Critcial Parameter Management (System Total Component Requirement & X Scor...
Manufacturing Process Capabilities             Cpk<0.34                     Cpk 1.6 1.4                   Good 1.2        ...
Response Performance                                        Capability Analysis                                           ...
Electrical Response Performance              Cpk> 1.66 Max Spec     µ      σ     Cpk   -5.84    -3.63   0.35   2.10   -5.8...
Radar Subassembly          Design Trade Study Analysis• DOE, Regression and Statistical tests of significance identified o...
Radar Subassembly CPM        Case Study Project Results• Attained Six Sigma plus electrical design performance.• Reduced u...
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Exploring and Evaluating the Product Performance - Cost Design Trade Space

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Exploring and Evaluating the Product Performance - Cost Design Trade Space

  1. 1. Exploring and Evaluating theProduct Performance – Cost Design Trade Space Neal Mackertich & Peter Kraus Raytheon Integrated Defense Systems Global Lean, Six Sigma & Business Improvement Summit 15 October 2008 Copyright © 2008 Raytheon Company Customer Success Is Our Mission is a trademark of Raytheon Company
  2. 2. Design for Six Sigma (DFSS)Design for Six Sigma is a methodology used within IPDS to predict,manage, and improve Performance, Producibility, and Affordability for thebenefit of our customers – Voice of the Customer modeling and analysis an integral part of the Performance Requirements analysis process – Up-front Architectural trade space evaluation (vs. validation) – Statistical modeling & optimization of the performance / cost design trade DFS space S Affordability Producibility – Focused application of DFMA principles and best practices – Predictable acceleration of product development cycle time using Critical Chain concepts – Stochastically modeled Integration, Verification & Validation Testing
  3. 3. Why Explore & Evaluate the Design Trade Space?• There are significant cost saving opportunities available by exploring the Performance – Cost Trade Space• Mission Assurance – Increasing our ability to deliver high-performing, affordable systems• Enables whole System planning, modeling & analysis – Raytheon as a Joint Battlespace Integrator – Design, management, and performance analysis are becoming increasingly complex and distributed tasks• Re-use of technical knowledge, analysis tools, and intellectual capital
  4. 4. Enabling Design Trade Space Exploration & Evaluation through Critical Parameter Management A methodology for exploring and evaluating the product performance – cost design trade space through the statistical identification, analysis and management of critical parameters. Y’sRisk Y’s X’sOpportunity CPM enables the identification and realization of significant product cost savings opportunities and program risk reductions
  5. 5. Critical Parameter Management Flows Identify Critical Parameterss Build Design Model / Transfer Functions Conduct Statistical Assessments Perform Trade Study Analysis
  6. 6. Identify Critical Parameters Gain a detailed understanding of thecustomer value equation, productrequirement needs and priorities Selection based on performance,cost, producibility & schedule criticality Includes key Systems TechnicalPerformance Measures (TPMs) andtheir derived requirements
  7. 7. Build Design Model / Transfer Function Mathematically defines critical parameter as a product characteristic Typically derived from physical laws, historical data, simulation models or design of experiments / regression analyses Links ownership to parameters across the product hierarchyX1 y1X2X3X4 y2 Complete and consistent hierarchy of critical parameters (including transfer functions) originating from Customer needs.
  8. 8. Conduct Statistical Assessment Design Variables 1.4 1.2 1 A 0.8 Response 0.6 0.4 0.2 0 15 18 21 24 27 30 16.5 19.5 22.5 25.5 28.5 0.18 F 0.16 0.14 0.12 B 0.1 0.08 K Y = f (A, B, C, D, E, F,...,M) 0.06 0.04 G 0.02 0 180 187 194 201 208 215 222 229 236 243 250 0.8 0.7 0.6 C Y 0.5 0.4 0.3 0.2 H M 0.1 0.25 0 17 20 23 17.6 18.2 18.8 19.4 20.6 21.2 21.8 22.4 1.4 1.2 I 0.2 L 1 0.15 D 0.8 0.6 0.1 J 0.4 0.2 0 0.05 15 19 23 15.8 16.6 17.4 18.2 19.8 20.6 21.4 22.2 1.4 0 1.2 15 18 21 24 27 30 16.5 19.5 22.5 25.5 28.5 1 E 0.8 0.6 0.4 0.2 0 Allocation/Flow Down 15 18 21 24 27 3016.5 19.5 22.5 25.5 28.5
  9. 9. Conduct Statistical Assessment .027 135 Capability analysis against specified .020 101.2 .014 67.5requirements (Scorecard is sortable by % .007 33.75out of spec. / Cp(k)) .000 0 3.75E+1 4.25E+1 4.75E+1 5.25E+1 5.75E+1 Certainty is 95.12% from 4.00E+1 to 5.30E+1 Prioritize critical parameters for their G-Sys. Losses -.45business opportunity and risk reduction A-Pavg .35 D-Ant. Eff, .35 F-Integ. Eff. .34 J-Rec. BW -.34 B-Ant. Gain .29 Identification of statistical drivers that H-Tgt RCS C-Ant. Aperture .23 .21most strongly influence performance and K-Pulse Width -.19 M-Rec. Out SNR -.15 I-Noise Figure -.12cost L-Rep. Freq. -.03 -1 -0.5 0 0.5 1 Measured by Rank Correlation
  10. 10. Perform Trade Study Analysis Trade Studies Understand the cost utility of Optimize Cost vs. Performancethe existing design margin Unaffordable Identify alternative design Performanceapproaches and specifications Optimal Objective Area Cost / Performance (y) Performance / Evaluate alternatives for Scheduletheir business return Trade Space Performance Unacceptable Threshold Implement recommendation Cost Cost Threshold Performance dy/dxand monitor results in order to Cost Objectiveensure Mission Assurance Cost (x) “The Best Design is the Simplest One that Works.” Albert Einstein
  11. 11. Radar Subassembly CPM Case Studys High Volume Subassemblys Mechanical Dimensions Critical to Electrical PerformanceProject objectives:s Attain Robust Design performance at minimum production cost.s Reduce current unit production cost by 30%.s Aggressively strive for additional cost savings.s Become a documented, successful design phase example for others to follow.
  12. 12. Radar Subassembly Tree Structure
  13. 13. Design Interface
  14. 14. Dimensional Capability Analysis Dim B 0.068 0.067 100 0.066 80 Dim B 60 Freq 0.065 USL(0.064)GW 0.064 40 LSL(0.062) 0.063 20 0.062 0 0.061 9 1 2 3 4 5 7 8 9 06 07 05 06 06 06 06 06 06 06 06 0.060 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0 100 200 300 400 500 sn Bins Data set with outliers removed Histogram of dimension Dim B relative to Specifications Use Cpk as a metric of Process µ = 0.06389 σ = 0.00108 Capability. Note: Six Sigma quality level = Cpk >1.5.  µ − LSL USL − µ  Cpk = Min  ;  = 0.03  3σ 3σ 
  15. 15. X Scorecard System Level DFSS - Critcial Parameter Management (System Total Component Requirement & X Score Card) Project Name: Radiator CPM Example Date: 8/16/2006 Design Specification Validation Test DataComponent CTF (X)Spec Owner Units Code LSL Nominal USL Mean Std. Dev. Cp Cpk Sample Size D1 R admin 0.4250 0.4270 0.4290 0.4270 0.0020 0.3333 0.3333 400 D10 R admin 0.4020 0.4030 0.4040 0.4033 0.0020 0.1667 0.1167 400 D2 R admin 0.4250 0.4270 0.4290 0.4264 0.0023 0.2899 0.2029 400 D3 R admin 0.4020 0.4030 0.4040 0.4009 0.0007 0.4762 -0.5238 400 D4 R admin 0.0575 0.0595 0.0615 0.0587 0.0012 0.5556 0.3194 400 D5 R admin NA 0.0000 0.0010 0.0012 0.0031 NA -0.0162 400 D6 R admin 0.4020 0.4030 0.4040 0.4009 0.0007 0.4762 -0.5238 400 D7 R admin 0.4260 0.4275 0.4290 0.4271 0.0015 0.3333 0.2444 400 D8 R admin 0.4260 0.4275 0.4290 0.4265 0.0008 0.6250 0.2083 400 D9 R admin 0.0620 0.0630 0.0640 0.0639 0.0010 0.3333 0.0367 400
  16. 16. Manufacturing Process Capabilities Cpk<0.34 Cpk 1.6 1.4 Good 1.2 Acceptable 1 0.8 0.6 0.4 0.2 0-0.2-0.4-0.6-0.8 Dimension
  17. 17. Response Performance Capability Analysis Electrical PerformanceNumber of Observations 80 LSL 60 40 20 0 Histogram of electrical output relative to specification Use Cpk metric to establish relative µ = − 0.531 σ = 0.109 electrical performance.  µ − LSL  Cpk =   = 2.04  3σ 
  18. 18. Electrical Response Performance Cpk> 1.66 Max Spec µ σ Cpk -5.84 -3.63 0.35 2.10 -5.84 -3.20 0.32 2.69 -5.89 -3.03 0.35 2.71 -5.95 -2.97 0.41 2.50 -6.00 -2.92 0.42 2.43 -6.11 -3.01 0.45 2.26 -6.22 -3.24 0.52 1.90 -6.27 -3.38 0.56 1.73 -6.38 -3.36 0.61 1.66 -6.44 -3.13 0.62 1.79 -6.49 -2.87 0.59 2.04 -6.49 -2.48 0.49 2.71 -6.49 -2.27 0.45 3.12 -6.49 -2.09 0.42 3.51 -6.44 -2.01 0.40 3.67 -6.38 -2.00 0.39 3.69 -6.27 -2.02 0.39 3.56 -6.17 -2.05 0.41 3.33 -6.00 -2.24 0.42 2.96 -5.84 -2.37 0.44 2.58 -5.68 -2.69 0.48 2.06
  19. 19. Radar Subassembly Design Trade Study Analysis• DOE, Regression and Statistical tests of significance identified only one design feature to statistically impact performance.• Utilized gained process capability knowledge and a statistical understanding of the impact of assigned tolerances on performance to trade low-margin mechanical design tolerance for cost realization opportunities.• Through a detailed understanding of what drives manufacturing costs, the team was then able to statistically reallocate tolerances to minimize unit production costs.
  20. 20. Radar Subassembly CPM Case Study Project Results• Attained Six Sigma plus electrical design performance.• Reduced unit production costs by 58%.• Achieved cost savings of >$5M• Achieved follow-on contract cost reductions >$30M

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