1. Chapter 12
Design for
Six Sigma

2. DFSS Activities Four Principal Activities
Concept development, determining product functionality based upon customer requirements, technological capabilities, and economic realities
Design development, focusing on product and process performance issues necessary to fulfill the product and service requirements in manufacturing or delivery
Design optimization, seeking to minimize the impact of variation in production and use, creating a “robust” design
Design verification, ensuring that the capability of the production system meets the appropriate sigma level

3. Key Idea
Like Six Sigma itself, most tools for DFSS have been around for some time; its uniqueness lies in the manner in which they are integrated into a formal methodology, driven by the Six Sigma philosophy, with clear business objectives in mind.

4. Tools for Concept Development
Concept development – the process of applying scientific, engineering, and business knowledge to produce a basic functional design that meets both customer needs and manufacturing or service delivery requirements.
Quality function deployment (QFD)
Concept engineering

5. Key Idea Concept Development
Developing a basic functional design involves translating customer requirements into measurable technical requirements and, subsequently, into detailed design specifications.

6. Key Idea QFD
QFD benefits companies through improved communication and teamwork between all constituencies in the value chain, such as between marketing and design, between design and manufacturing, and between purchasing and suppliers.

7. House of Quality Interrelationships Customer requirement
Technical requirements
Voice of
the
customer
Relationship
matrix
Technical requirement
priorities
Customer
requirement
Competitive
evaluation
Interrelationships

9. Quality Function Deployment
technical
requirements
component
characteristics
process
operations
quality plan

10. Building the House of Quality
Identify customer requirements.
Identify technical requirements.
Relate the customer requirements to the technical requirements.
Conduct an evaluation of competing products or services.
Evaluate technical requirements and develop targets.
Determine which technical requirements to deploy in the remainder of the production/delivery process.

11. Tools for Design Development
Tolerance design
Design failure mode and effects analysis
Reliability prediction

12. Key Idea Tools for Design Development
Manufacturing specifications consist of nominal dimensions and tolerances. Nominal refers to the ideal dimension or the target value that manufacturing seeks to meet; tolerance is the permissible variation, recognizing the difficulty of meeting a target consistently.

13. Tolerance Design Determining permissible variation in a dimension
Understand tradeoffs between costs and performance

14. Key Idea Tolerance Design
Tolerances are necessary because not all parts can be produced exactly to nominal specifications because of natural variations (common causes) in production processes due to the “5 Ms”: men and women, materials, machines, methods, and measurement.

15. DFMEA
Design failure mode and effects analysis (DFMEA) – identification of all the ways in which a failure can occur, to estimate the effect and seriousness of the failure, and to recommend corrective design actions.

16. DFMEA Failure modes Effect of the failure on the customer
Severity, likelihood of occurrence, and detection rating
Potential causes of failure
Corrective actions or controls

17. Reliability Prediction
Generally defined as the ability of a product to perform as expected over time
Formally defined as the probability that a product, piece of equipment, or system performs its intended function for a stated period of time under specified operating conditions

18. Types of Failures
Functional failure – failure that occurs at the start of product life due to manufacturing or material detects
Reliability failure – failure after some period of use

19. Types of Reliability
Inherent reliability – predicted by product design
Achieved reliability – observed during use

20. Reliability Measurement
Failure rate (l) – number of failures per unit time
Alternative measures
Mean time to failure (MTTF)
Mean time between failures (MTBF)

21. Cumulative Failure Rate Curve

22. Failure Rate Curve
“Infant mortality period”

23. Average Failure Rate

24. Key Idea Reliability Prediction
Many electronic components commonly exhibit a high, but decreasing, failure rate early in their lives (as evidenced by the steep slope of the curve), followed by a period of a relatively constant failure rate, and ending with an increasing failure rate.

25. Product Life Characteristic Curve
Three distinct time period
Early failure
Useful life
Wearout period

26. Predicting System Reliability
Series system
Parallel system
Combination system

27. Series Systems 1 2 n
RS = R1 R2 ... Rn

28. Parallel Systems 1 2 n
RS = 1 - (1 - R1) (1 - R2)... (1 - Rn)

29. Series-Parallel SystemsC RA RB RD RC A B D C RC
Convert to equivalent series system RA RB RD A B C’ D
RC’ = 1 – (1-RC)(1-RC)

30. Tools for Design Optimization
Taguchi loss function
Optimizing reliability

31. Key Idea Tools for Design Optimization
Design optimization includes setting proper tolerances to ensure maximum product performance and making designs robust, that is, insensitive to variations in manufacturing or the use environment.

32. Loss Functions loss no loss nominal tolerance Traditional View
Taguchi’s
View

33. Loss function

34. Taguchi Loss Function
No strict cut-off point divides good quality from poor quality. Rather, losses can be approximated by a quadratic function so that larger deviations from target correspond to increasingly larger losses.

35. Optimizing Reliability
Standardization—use components with proven track records
Redundancy—provide backup components
Physics of failure—understand physical properties of materials

36. Tools for Design Verification
Reliability testing
Measurement systems evaluation
Process capability evaluation

37. Key Idea Tools for Design Verification
Design verification is necessary to ensure that designs will meet customer requirements and can be produced to specifications.

38. Reliability testing Life testing Accelerated life testing
Environmental testing
Vibration and shock testing
Burn-in (component stress testing)

39. Measurement System Evaluation
Whenever variation is observed in measurements, some portion is due to measurement system error. Some errors are systematic (called bias); others are random. The size of the errors relative to the measurement value can significantly affect the quality of the data and resulting decisions.

40. Metrology - Science of Measurement
Accuracy - closeness of agreement between an observed value and a standard – can lead to systematic bias.
Precision - closeness of agreement between randomly selected individual measurements – can lead to random variation.

41. Accuracy vs. Precision

42. Repeatability and Reproducibility
Repeatability (equipment variation) – variation in multiple measurements by an individual using the same instrument.
Reproducibility (operator variation) - variation in the same measuring instrument used by different individuals

43. Key Idea Calibration
One of the most important functions of metrology is calibration—the comparison of a measurement device or system having a known relationship to national standards against another device or system whose relationship to national standards is unknown.

44. Process Capability
The range over which the natural variation of a process occurs as determined by the system of common causes
Measured by the proportion of output that can be produced within design specifications

45. Process Capability Study Typical Questions Asked
Where is the process centered?
How much variability exists in the process?
Is the performance relative to specs acceptable?
What proportion of output will be expected to meet the specs?
What factors contribute to variability?

46. Types of Capability Studies
Peak performance study - how a process performs under ideal conditions
Process characterization study - how a process performs under actual operating conditions
Component variability study - relative contribution of different sources of variation (e.g., process factors, measurement system)

47. Process Capability
specification
natural variation
(a)
(b)
(c)
(d)

48. Process Capability 20 25 30 Minutes Upper specification Lower Nominal
value
Process distribution
Process is capable 18

49. Process Capability 20 25 30 Minutes Upper specification Lower Nominal
value
Process distribution
Process is not capable 18

50. Effects of Reducing Variability on Process Capability
Lower
specification
Mean
Upper
Nominal value
Six sigma
Four sigma
Two sigma 22

51. Key Idea Process Capability
The process capability index, Cp (sometimes called the process potential index), is defined as the ratio of the specification width to the natural tolerance of the process. Cp relates the natural variation of the process with the design specifications in a single, quantitative measure.

52. Process Capability Index
UTL - LTL 6s
Cp =
UTL - m 3s
Cpu =
m - LTL 3s
Cpl =
Cpk = min{
Cpl, Cpu }