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Statistical Analysis of New Product Development (NPD) Cycle-time Data
The document presents an analysis of the new product development (NPD) cycle-time at Alpha Technologies, focusing on performance benchmarks and methods for efficiency improvement. It details the phase-gate NPD process, data collected, and tools used, including statistical analysis to identify factors impacting schedule and effort. The findings emphasize the need for better estimation practices and resource management to enhance NPD cycle-time and overall project performance.
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Statistical Analysis of New Product Development (NPD) Cycle-time Data
- 1. Quality and BusinessExcellence Celebration ASQ Vancouver 25th Anniversary Statistical Analysis of New Product Development (NPD) Cycle-time Data Including Applications of Results Steve Pratt, MEng., PE, CSSBB Director of Engineering, Alpha Technologies
- 2. Alpha Technologies Company Background Alphais a Full Service Power Systems Provider to Multiple Markets Pratt – Slide 1
- 3. Power Modules Indoor Power Systems Outdoor Power Systems Alpha’sPower Solutions Products Pratt – Slide 2
- 4. Phase-Gate New Product Development(NPD) Process Deliverables: • Product Concept • Business Case • Preliminary Plan Deliverables: •High-Level Design •Requirements •Complete Project Plan Deliverables: • Design Outputs • DFx Reviews • Preliminary Test Reports Deliverables: • Pilot Doc Pack • Pilot Build • Sales Forecasts Deliverables: • Compliance Certifications • Training • MarCom docs Deliverables: • Sustaining Eng • Repair/Support • Cost Reduction Phase 2 Planning Phase 1 Concept Phase 3 Development Phase 4 Qualification and Pre- Production Phase 5 LA and Production Ramp-up Phase 6 GA and MOL Pratt – Slide 3
- 5. Concurrent Engineering viaCross- Functional Core Teams Service Supply Chain Product Management Program Manager Quality Engineering Manufacturing Pratt – Slide 4
- 6. Motivation for Study ContinuousImprovement: • general perception that NPD takes too long • lack of proper management of resources • desire to establish performance benchmarks Pratt – Slide 5
- 7. NPD Data Collectedand Analyzed Schedule Data • Recorded Dates: Start, Gate 1, Gate 2, Gate 3, Gate 4 (LA) & Gate 5 (GA) • Program Plans: estimated time to LA, estimated time to GA • Calculated: total time, time per phase, actual vs. plan Cost Data • Timecard System: actual total effort (man-hours) • Program Plans: estimated total effort • Calculated: effort by function, effort by phase, remaining effort, actual vs. plan Pratt – Slide 6
- 8. Tools Used inthe Study JMP – Statistical Discovery Software Analyze-Distribution Platform: • Calculations: Quantiles, Moments • Plots: Histogram, Quantile Box, Normal Quantile • Fit Distribution: Normal, Beta, Goodness of Fit Tests Fit Y by X Platform: Bivariate Analysis • Fit Line, Fit Polynomial, Summary of Fit Fit Y by X Platform: One-way Analysis • Calculations: Means and Standard Deviations • Plots: Mean Diamonds • Analysis of Variance (ANOVA) Fit Model Platform • Calculations: Standard Least Squares, Summary of Fit, Effect Tests • Plots: Actual by Predicted, Effect Leverage, Residual by Predicted • Two-way ANOVA with interactions Microsoft Excel and PowerPoint Pratt – Slide 7
- 9. External Benchmarking Data– PDMA Best Practices Research Provides industry-average NPD Cycle-Time based on complexity of programs: • new-to-the world • new-to-the firm • next-gen improvements • incremental improvements Pratt – Slide 8
- 10. Source: Griffin A.,“Product development cycle time for business-to-business products,” Industrial Marketing Management 2002;31: 291-304 Average NPD Cycle-Time Benchmarking Pratt – Slide 9
- 11. Planned vs. ActualNPD Cycle-Time Pratt – Slide 10
- 12. ANOVA to IdentifySignificant Factors for Schedule Data Pratt – Slide 11
- 13. ANOVA to IdentifySignificant Factors for Effort Data Pratt – Slide 12
- 14. Categorizing NPD Programsinto 9 Buckets Product Types: Modules (power modules, shelves, controllers) Indoor Systems (rack-based systems, racks, distribution) OSP (outside plant power/battery systems) Program Complexities: A– major program requiring advanced development B – completely new, but no advanced development C– new with incremental development Pratt – Slide 13
- 15. NPD Summary Data– Schedule (Gate 2 to LA) Pratt – Slide 14
- 16. NPD Summary Data– Schedule (Gate 2 to GA) Pratt – Slide 15
- 17. NPD Summary Data– Effort (Total Development Hours) Pratt – Slide 16
- 18. NPD Schedule &Effort Continuous Probability Distributions Pratt – Slide 17
- 19. Additional Analyses • planvs. actual – schedule and effort • schedule and effort variance by phase • time spent per phase • total effort by function and phase • default team size and composition Pratt – Slide 18
- 20.
- 21. More Accurate Gate2 Point Estimates of Schedule and Effort Previous estimates were: - subject to negotiation - overly optimistic and aggressive - “rose-colored” recollections of past Pratt – Slide 20
- 22. Alpha’s Historical Schedule EstimationAccuracy Median MRE = 44% Pratt – Slide 21
- 23. NPD Schedule Point-Estimationvia Historical Mean Values Median MRE = 19% Pratt – Slide 22
- 24. Basic Schedule EquationUsing Historical Mean Effort Values Median MRE = 21% Pratt – Slide 23
- 25. Informal Comparisons EquationUsing NPD Mean Values Median MRE = 20% Pratt – Slide 24
- 26. Mean Magnitude ofRelative Error – Schedule Estimates Pratt – Slide 25
- 27. Alpha’s Historical Effort EstimationAccuracy Median MRE = 36% Pratt – Slide 26
- 28. NPD Effort Point-Estimationvia Historical Mean Values Median MRE = 18% Pratt – Slide 27
- 29. Mean Magnitude ofRelative Error – Effort Estimates Pratt – Slide 28
- 30. Determine the “Coneof Uncertainty” Pratt – Slide 29
- 31. Cone of Uncertaintyfor NPD Project Estimates Pratt – Slide 30
- 32. Cone of Uncertaintyfor NPD Project Schedule Data Pratt – Slide 31
- 33. Cone of UncertaintyApplied to the 9 NPD Project Buckets Pratt – Slide 32
- 34. Schedule Cone ofUncertainty Quantified by Program Phase Pratt – Slide 33
- 35. Effort Cone ofUncertainty Quantified by Program Phase Pratt – Slide 34
- 36. Effort Estimate Rangesby Program Phase Pratt – Slide 35
- 37.
- 38. Creation of Top-DownProgram Schedules Pratt – Slide 37
- 39. NPD – TimeSpent per Phase Pratt – Slide 38
- 40. How Much TimeShould Be Spent on Planning? Pratt – Slide 39
- 41.
- 42. SMART Performance Improvement Goals •fact-based and statistically valid • objective and quantifiable • deterministic at the start of a program • consistent across all types of programs • an enabler of continuous improvement • unambiguous and easy to calculate An ideal performance improvement measure is: Pratt – Slide 41
- 43. RSD: a NormalizedMeasure of Dispersion Pratt – Slide 42
- 44. Program Scoring: RSDas Performance Unit-of-Measure Pratt – Slide 43
- 45. 2013 NPD Cycle-TimePerformance Pratt – Slide 44
- 46. Earned-Value Milestones toScore WIP NPD Programs Pratt – Slide 45
- 47.
- 48. Deterministic Business CaseAnalysis “Figures of Merit” Pratt – Slide 47
- 49. Probabilistic Business CaseAnalysis Beta Distribution Pratt – Slide 48
- 50. Monte Carlo Simulationof Cash Flows ($000) Trial #1 Trial #2 Trial #3 Trial #4 Trial #5000 NPV $556 $461 $681 $447 $621 NRE 562 560597530664 GM$year1 348274 241220 274 GM$year2 604 653 407579 635 GM$year3 451651644447558 Pratt – Slide 49
- 51. Probability-Based Figures ofMerit Pratt – Slide 50
- 52.
- 53. Default Team Sizeand Composition Effort divided by Schedule represents number of individuals Pratt – Slide 52
- 54. Roadmap and BudgetPlanning Pratt – Slide 53
- 55. Program Resource Management EngineeringPokémon board • visual resource management tool • 9 program type/complexity buckets • default team size/composition Pratt – Slide 54
- 56. NPD Programs areRepresented by a Series of Magnetic Trays Pratt – Slide 55
- 57. Program Trays IncorporateDefault Team Size/Composition Pratt – Slide 56
- 58. NPD Team Membersare Represented by a Series of Cards Pratt – Slide 57
- 59. Engineering Pokémon –Additional Pieces Program Status Indicators Bonus Targets Add-on Resource Trays “Ghost” Cards Important Date Indicators Clear Plastic Overlays Pratt – Slide 58
- 60. The Board inAction Pratt – Slide 59
- 61. Resulting Improvements Pratt –Slide 60
- 62. Improved New ProductDevelopment Better planning and management of resources… • more stability and focus / less chaos • increased efficiency • enhanced organizational understanding • faster NPD cycle-time Pratt – Slide 61
- 63. In Conclusion: It isnever too late to start recording data! - even simple data such as key dates and time spent on activities can facilitate powerful analyses - capture data in real-time; don’t rely on memory or post mortem activities Pratt – Slide 62
- 64.
- 65. Thank You! STEVEN PRATT,MEng., PE Director of Engineering www.alpha.ca Pratt – Slide 64