The document discusses using Design for Six Sigma (DFSS) methodology to achieve lean software development at Raytheon Missile Systems. It describes applying DFSS principles across the software development lifecycle, including capturing customer needs, managing costs, designing for producibility and performance, using statistical modeling and simulation, and conducting failure mode and effects analysis. DFSS is presented as a way to optimize software designs for customers' needs while balancing affordability, performance, and testability.

1. Using Design for Six Sigma to achieve Lean Software Development at Raytheon Missile Systems Tony Strickland Certified Raytheon Six Sigma Expert (520) 794-7855 [email_address] ®

4. Process Integration IPDS provides an integrated set of best practices for the entire product development life cycle using a just-in-time tailoring process. R6s is a business strategy for process improvement. CMMI provides guidance for creating, measuring,managing, and improving specific processes. Each plays an integral role in the success of programs, projects and organizations

5. DFSS Design for Six Sigma is a methodology used to predict, manage and improve Producibility, Affordability , and Robust Performance for the benefit of the customers. DESIGNS THAT CONSISTENTLY ENSURE MISSION SUCCESS, MEET COST & SCHEDULE, AND CAN BE READILY PRODUCED IN THE FACTORY Design for Six Sigma Definition Affordability Producibility Robust Performance

7. Traditional DFSS Investment: Non-Recurring Costs Engineering Hours 0 Months Desired Typical Development I&T Production WHY USE DFSS? Reactive Proactive Challenge DFSS Challenge: Non-Recurring Costs Potential Revenue Generation: Recurring Costs

9. Effectively Applying DFSS in Software Intensive Systems Program Execution Gates 5-11 Business Decision Gates 1-4 5 - System Integration, Verification and Validation 4 - Product Design and Development 1 - Capture/Proposal Development 7 8 10 9 = GATE Internal Preliminary Design Review Start-Up Review Internal System Functional Review Internal Critical Design Review Internal Test/Ship Readiness Review Internal Production Readiness Review Key Characteristics Management Cost Management 2 - Project Planning, Management and Control 1 2 3 4 Bid / Proposal Review 11 Test Optimization Architecture Evaluation QFD Visual Requirements Critical Chain Defect Prediction & Prevention Performance Modeling Planning 6 - Production and Deployment Planning 7 - Operations and Support 3 - Requirements and Architecture Development 5 6

16. Multi-Tasking is an Insidious Generator of When (if) we look at “Resource Allocation” we typically discover high degrees of multi-tasking … waste Why software teams struggle to meet their schedule commitments Resource 1 Resource 3 Time Resource 2

18. CCPM Typical Results- Notional A B C A B C No Multi-Tasking Multi-tasking t = 0 Projects start later but finish sooner t = n

23. 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 Simulation or Multi-Variate Transfer Function Design Variables Input Variation Contributes To Response Variation 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

24. 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 Design Variables System/Sub-System Model f (A, B, C, D, E, F,...,M) A Visual Representation of DFSS Statistical Requirements Analysis 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 Flow Down/Allocation Based on Output Variability

33. Key Product Characteristic Tree Simple Example of a KCC Affecting Several KPCs Top-level KPCs 2nd level KPCs KCCs VOC

35. Defect Containment Chart

37. Defect Model Previous Program/Release Standard Defect Containment Chart Model Raw Defect Counts Prorate the raw defect counts for the new program based upon SLOC counts Projected Raw Defect Counts For New Program New Program Defect Containment Current Raw Defect Counts New Program - Projected Defect Containment Sum of Current Raw Defect Counts Plus Raw Defects Not Yet Detected Defect Density Predictive Model Approach

39. Defect Density Code Inspection Analysis When number of defects is between the control limits, fix findings and pass to next stage . When number of defects is below the lower limit, unit(s) may need more inspection. When number of defects is above the upper limit, unit(s) may need rework and a repeated inspection.

45. Satellite Ground Systems Applied Case

51. Questions?