Exploring and Evaluating the Product Performance – Cost

Design Trade Space

Neal Mackertich & Peter KrausRaytheon Integrated Defense Systems

Global Lean, Six Sigma & Business Improvement Summit

15 October 2008

Copyright © 2008 Raytheon CompanyCustomer Success Is Our Mission is a trademark of Raytheon Company

DFSS

Affordability Producibility

Performance

– Voice of the Customer modeling and analysis an integral part of the Requirements analysis process

– Up-front Architectural trade space evaluation (vs. validation)

– Statistical modeling & optimization of the performance / cost design trade space

– 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

Design for Six Sigma is a methodology used within IPDS to predict, manage, and improve Performance, Producibility, and Affordability for the benefit of our customers

Design for Six Sigma (DFSS)

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

Enabling Design Trade Space Exploration & Evaluation through Critical Parameter Management

CPM enables the identification and realization of significant product cost savings opportunities and program risk reductions

X’s

Y’s

Y’sRisk

Opportunity

A methodology for exploring and evaluating the product performance – cost design trade space through the statistical identification, analysis and management of critical parameters.

Critical Parameter Management Flow

■ Identify Critical Parameters

■ Build Design Model / Transfer Function

■ Conduct Statistical Assessment

■ Perform Trade Study Analysis

Identify Critical Parameters

Gain a detailed understanding of the customer value equation, product requirement needs and priorities

Selection based on performance, cost, producibility & schedule criticality

Includes key Systems Technical Performance Measures (TPMs) and their derived requirements

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 hierarchy

Complete and consistent hierarchy of critical parameters (including transfer functions) originating from Customer needs.

X4

y1

y2

X2X3

X1

Conduct Statistical Assessment

A

B

C

D

E

Y

Y = f (A, B, C, D, E, F,...,M)0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

180

187

194

201

208

215

222

229

236

243

250

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

17

17.6

18.2

18.8

19.4 20

20.6

21.2

21.8

22.4 23

0

0.2

0.4

0.6

0.8

1

1.2

1.4

15

15.8

16.6

17.4

18.2 19

19.8

20.6

21.4

22.2 23

0

0.2

0.4

0.6

0.8

1

1.2

1.4

15

16.5 18

19.5 21

22.5 24

25.5 27

28.5 30

0

0.2

0.4

0.6

0.8

1

1.2

1.4

15

16.5 18

19.5 21

22.5 24

25.5 27

28.5 30

Response

F

G

H

I

J

K

L

M

0

0.05

0.1

0.15

0.2

0.25

15

16.5 18

19.5 21

22.5 24

25.5 27

28.5 30

Design Variables

Allocation/Flow Down

Conduct Statistical Assessment

Certainty is 95.12% from 4.00E+1 to 5.30E+1

.000

.007

.014

.020

.027

0

33.75

67.5

101.2

135

3.75E+1 4.25E+1 4.75E+1 5.25E+1 5.75E+1

G-Sys. Losses -.45

A-Pavg .35

D-Ant. Eff, .35

F-Integ. Eff. .34

J-Rec. BW -.34

B-Ant. Gain .29

H-Tgt RCS .23

C-Ant. Aperture .21

K-Pulse Width -.19

M-Rec. Out SNR -.15

I-Noise Figure -.12

L-Rep. Freq. -.03

-1 -0.5 0 0.5 1

Measured by Rank Correlation

Capability analysis against specified requirements (Scorecard is sortable by % out of spec. / Cp(k))

Prioritize critical parameters for their business opportunity and risk reduction

Identification of statistical drivers that most strongly influence performance and cost

Perform Trade Study Analysis

Trade Studies

Optimize Cost vs. Performance

Perfo

rman

ce (y

)

Cost

Unacceptable Performance

Unaffordable

Optimal Area

Cost (x)

dy/dx

PerformanceThresholdCost

Objective

CostThreshold

PerformanceObjective

Cost / Performance /

ScheduleTrade Space

Understand the cost utility of the existing design margin

Identify alternative design approaches and specifications

Evaluate alternatives for their business return

Implement recommendation and monitor results in order to ensure Mission Assurance

“The Best Design is the Simplest One that Works.” Albert Einstein

Radar Subassembly CPM Case Study

■ High Volume Subassembly■ Mechanical Dimensions Critical to Electrical

Performance

Project objectives:■ Attain Robust Design performance at minimum

production cost.■ Reduce current unit production cost by 30%.■ Aggressively strive for additional cost savings.■ Become a documented, successful design phase

example for others to follow.

Radar Subassembly Tree Structure

Design Interface

03.0

3;

3

00108.006389.0

=−

−=

==

σµ

σµ

σµ

USLLSLMin C

pk

5004003002001000

0.068

0.067

0.066

0.065

0.064

0.063

0.062

0.061

0.060

sn

GW

Dim B

020406080

100

0.059 0.0

60.0

610.0

620.0

630.0

640.0

650.0

670.0

680.0

69 0.07

BinsFr

eq

Dim BUSL(0.064)LSL(0.062)

Data set with outliers removed Histogram of dimension Dim B relative to Specifications

Use Cpk as a metric of ProcessCapability. Note: Six Sigma qualitylevel = Cpk >1.5.

Dimensional Capability Analysis

X Scorecard

Project Name: Date:

Component CTF (X)Spec Owner UnitsCode 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 400D10 R admin 0.4020 0.4030 0.4040 0.4033 0.0020 0.1667 0.1167 400D2 R admin 0.4250 0.4270 0.4290 0.4264 0.0023 0.2899 0.2029 400D3 R admin 0.4020 0.4030 0.4040 0.4009 0.0007 0.4762 -0.5238 400D4 R admin 0.0575 0.0595 0.0615 0.0587 0.0012 0.5556 0.3194 400D5 R admin NA 0.0000 0.0010 0.0012 0.0031 NA -0.0162 400D6 R admin 0.4020 0.4030 0.4040 0.4009 0.0007 0.4762 -0.5238 400D7 R admin 0.4260 0.4275 0.4290 0.4271 0.0015 0.3333 0.2444 400D8 R admin 0.4260 0.4275 0.4290 0.4265 0.0008 0.6250 0.2083 400D9 R admin 0.0620 0.0630 0.0640 0.0639 0.0010 0.3333 0.0367 400

System Level DFSS - Critcial Parameter Management (System Total Component Requirement & X Score Card)

Radiator CPM Example 8/16/2006

Design Specification Validation Test Data

Manufacturing Process Capabilities Cpk<0.34

Cpk

-0.8-0.6-0.4-0.2

00.20.40.60.8

11.21.41.6

Dimension

Acceptable

Good

04.2

3

109.0531.0

=

−=

=−=

C

pkσ

µ

σµ

LSLUse Cpk metric to establish relativeelectrical performance.

Histogram of electrical output relative to specification

Response Performance Capability Analysis

Electrical Performance

0

20

40

60

80

Num

ber o

f Obs

erva

tions

LSL

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

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.

• 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

Radar Subassembly CPM Case Study Project Results