Chapter 12 Design for Six Sigma MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

DFSS 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 MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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 MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea Developing a basic functional design involves translating customer requirements into measurable technical requirements and, subsequently, into detailed design specifications. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Quality Function Deployment technical requirements component characteristics process operations quality plan MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

House of Quality Interrelationships Customer requirement Technical requirements Voice of the customer Relationship matrix Technical requirement priorities Customer requirement Competitive evaluation Interrelationships MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Concept Engineering Understanding the customer’s environment. Converting understanding into requirements. Operationalizing what has been learned. Concept generation. Concept selection. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Tools for Design Development Tolerance design Design failure mode and effects analysis Reliability prediction MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Tolerance Design Determining permissible variation in a dimension Understand tradeoffs between costs and performance MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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 MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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 MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Types of Reliability Inherent reliability – predicted by product design Achieved reliability – observed during use MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Reliability Measurement Failure rate (l) – number of failures per unit time Alternative measures Mean time to failure Mean time between failures MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Cumulative Failure Rate Curve MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Failure Rate Curve “Infant mortality period” MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Average Failure Rate MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Reliability Function Probability density function of failures f(t) = le-lt for t > 0 Probability of failure from (0, T) F(t) = 1 – e-lT Reliability function R(T) = 1 – F(T) = e-lT MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Series Systems RS = R1 R2 ... Rn 1 2 n MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Parallel Systems RS = 1 - (1 - R1) (1 - R2)... (1 - Rn) 1 2 n MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Series-Parallel Systems C 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) MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Tools for Design Optimization Taguchi loss function Optimizing reliability MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Loss Functions loss no loss nominal tolerance Traditional View Taguchi’s View MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Taguchi Loss Function Calculations Loss function: L(x) = k(x - T)2 Example: Specification = .500  .020. Failure outside of the tolerance range costs $50 to repair. Thus, 50 = k(.020)2. Solving for k yields k = 125,000. The loss function is: L(x) = 125,000(x - .500)2 Expected loss = k(2 + D2) where D is the deviation from the target. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Optimizing Reliability Standardization Redundancy Physics of failure MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Tools for Design Verification Reliability testing Measurement systems evaluation Process capability evaluation MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea Design verification is necessary to ensure that designs will meet customer requirements and can be produced to specifications. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Reliability testing Life testing Accelerated life testing Environmental testing Vibration and shock testing Burn-in (component stress testing) MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Metrology - Science of Measurement Accuracy - closeness of agreement between an observed value and a standard Precision - closeness of agreement between randomly selected individual measurements MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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 MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Repeatability & Reproducibility Studies Quantify and evaluate the capability of a measurement system Select m operators and n parts Calibrate the measuring instrument Randomly measure each part by each operator for r trials Compute key statistics to quantify repeatability and reproducibility MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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R&R Evaluation Under 10% error - OK 10-30% error - may be OK over 30% error - unacceptable MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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 MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

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) MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Process Capability Study Choose a representative machine or process Define the process conditions Select a representative operator Provide the right materials Specify the gauging or measurement method Record the measurements Construct a histogram and compute descriptive statistics: mean and standard deviation Compare results with specified tolerances MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Process Capability specification natural variation (a) (b) (c) (d) MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Key Idea 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. MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Process Capability Index UTL - LTL 6s Cp = UTL - m 3s Cpu = m - LTL 3s Cpl = Cpk = min{ Cpl, Cpu } MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing

Spreadsheet Template MANAGING FOR QUALITY AND PERFORMANCE EXCELLENCE, 7e, © 2008 Thomson Higher Education Publishing