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BRYAN DODSON, Executive Engineer, SKF, USA
PATRICK C. HAMMETT, Lead Faculty Six Sigma Program, Integrative Systems & Design, College of Engineering, University of Michigan, Ann Arbor, USA
RENÉ KLERX, Principal Statistician, SKF, The Netherlands
Preface ix
Acknowledgments xi
1 New product development process 1
1.1 Introduction 1
1.2 Phases of new product development 2
1.2.1 Phase I-concept planning 3
1.2.2 Phase II-product planning 4
1.2.3 Phase III-product engineering design and verification 6
1.2.4 Phase IV-process engineering 9
1.2.5 Phase V-manufacturing validation and ramp-up 10
1.3 Patterns of new product development 11
1.4 New product development and Design for Six Sigma 13
1.4.1 DfSS core objectives 13
1.4.2 DfSS methodology 15
1.4.3 Embedded DfSS 16
1.5 Summary 17
Exercises 17
2 Statistical background for engineering design 19
2.1 Expectation 19
2.2 Statistical distributions 24
2.2.1 Normal distribution 24
2.2.2 Lognormal distribution 27
2.2.3 Weibull distribution 30
2.2.4 Exponential distribution 32
2.3 Probability plotting 34
2.3.1 Probability plotting-lognormal distribution 35
2.3.2 Probability plotting-normal distribution 36
2.3.3 Probability plotting-Weibull distribution 37
2.3.4 Probability plotting-exponential distribution 39
2.3.5 Probability plotting with confidence limits 40
2.4 Summary 43
Exercises 44
3 Introduction to variation in engineering design 46
3.1 Variation in engineering design 46
3.2 Propagation of error 47
3.3 Protecting designs against variation 48
3.4 Estimates of means and variances of functions of several variables 51
3.5 Statistical bias 59
3.6 Robustness 59
3.7 Summary 60
Exercises 61
4 Monte Carlo simulation 63
4.1 Determining variation of the inputs 63
4.2 Random number generators 64
4.3 Validation 66
4.4 Stratified sampling 70
4.5 Summary 74
Exercises 75
5 Modeling variation of complex systems 76
5.1 Approximating the mean, bias, and variance 77
5.2 Estimating the parameters of non-normal distributions 81
5.3 Limitations of first-order Taylor series approximation for variance 84
5.4 Effect of non-normal input distributions 91
5.5 Nonconstant input standard deviation 93
5.6 Summary 93
Exercises 95
6 Desirability 98
6.1 Introduction 98
6.2 Requirements and scorecards 99
6.2.1 Types of requirements 100
6.2.2 Design scorecard 101
6.3 Desirability-single requirement 103
6.3.1 Desirability-one-sided limit 104
6.3.2 Desirability-two-sided limit 106
6.3.3 Desirability-nonlinear function 107
6.4 Desirability-multiple requirements 109
6.4.1 Maxi-min total desirability index 114
6.5 Desirability-accounting for variation 115
6.5.1 Determining desirability-using expected yields 115
6.5.2 Determining desirability-using non-mean responses 116
6.6 Summary 118
Exercises 118
7 Optimization and sensitivity 123
7.1 Optimization procedure 123
7.2 Statistical outliers 128
7.3 Process capability 129
7.4 Sensitivity and cost reduction 133
7.4.1 Reservoir flow example 134
7.4.2 Reservoir flow initial solution 135
7.4.3 Reservoir flow initial solution verification 136
7.4.4 Reservoir flow optimized with normal horsepower distribution 138
7.4.5 Reservoir flow optimized with normal horsepower distribution verification 140
7.4.6 Reservoir flow horsepower variation sensitivity 141
7.4.7 Reservoir flow horsepower lognormal probability plot 143
7.4.8 Reservoir flow horsepower Cpk optimization using a lognormal distribution 144
7.5 Summary 149
Exercises 150
8 Modeling system cost and multiple outputs 153
8.1 Optimizing for total system cost 153
8.2 Multiple outputs 158
8.2.1 Optimization 159
8.2.2 Computing nonconformance 159
8.3 Large-scale systems 164
8.4 Summary 166
Exercises 167
9 Tolerance analysis 170
9.1 Introduction 170
9.2 Tolerance analysis methods 174
9.2.1 Historical tolerancing 174
9.2.2 Worst-case tolerancing 175
9.2.3 Statistical tolerancing 175
9.3 Tolerance allocation 178
9.4 Drift, shift, and sorting 179
9.5 Non-normal inputs 182
9.6 Summary 182
Exercises 182
10 Empirical model development 185
10.1 Screening 185
10.2 Response surface 193
10.2.1 Central composite designs 194
10.3 Taguchi 200
10.4 Summary 200
Exercises 201
11 Binary logistic regression 202
11.1 Introduction 202
11.2 Binary logistic regression 205
11.2.1 Types of logistic regression 205
11.2.2 Binary versus ordinary least squares regression 206
11.2.3 Binary logistic regression and the logit model 208
11.2.4 Binary logistic regression with multiple predictors 211
11.2.5 Binary logistic regression and sample size planning 211
11.2.6 Binary logistic regression fuel door example 212
11.2.7 Binary logistic regression-significant binary input 213
11.2.8 Binary logistic regression-nonsignificant binary input 214
11.2.9 Binary logistic regression-continuous input 214
11.2.10 Binary logistic regression-multiple inputs 215
11.3 Logistic regression and customer loss functions 217
11.4 Loss function with maximum (or minimum) response 220
11.5 Summary 223
Exercises 223
12 Verification and validation 225
12.1 Introduction 225
12.2 Engineering model V&V 228
12.3 Design verification methods and tools 230
12.3.1 Design verification reviews 230
12.3.2 Virtual prototypes and simulation 231
12.3.3 Physical prototypes and early production builds 232
12.3.4 Confirmation testing comparing alternatives 232
12.3.5 Confirmation tests comparing the design to acceptance criteria 233
12.4 Process validation procedure 233
12.5 Summary 238
References 239
Bibliography 242
Answers to selected exercises 246
Index 251
The development of new products is a major competitive issue as consumers continuously demand new and improved products. One outcome of this competitive landscape is the need for shorter product life cycles while still achieving ever increasing expectations for product quality and performance measures. This has required companies to significantly enhance their capabilities to better identify true customer wants, translate them into quantifiable product functional requirements, quickly develop, evaluate, and integrate new design concepts to meet them, and then effectively bring these concepts to market through new product offerings.
Several companies (e.g., Apple, General Electric (GE), Samsung, Toyota, General Motors (GM), Ford) have made great strides improving the effectiveness of new product development. For example, many companies have created processes to quickly gather voice of the customer information via surveys, customer clinics, or other sources. Samsung, for instance, has a well-designed system of scorecards and tool application checklists to manage risk and cycle time from the voice of the customer through the launch of products that meet customer and business process demands (Creveling et al., 2003). In addition, advances in computer simulation and modeling techniques permit manufacturers to evaluate many design concept alternatives, thereby resolving many potential problems at minimal costs. This also allows one to minimize assumptions and simplifications that reduce the accuracy of the answer (Tennant, 2002). Finally, even when there is a need to construct physical prototypes, the cost has been lowered through rapid prototyping processes.
An interesting outcome of reducing the costs of data collection and analysis (for voice of the customer, simulation modeling, or physical testing) has been an increase in these activities. This has subsequently resulted in a deeper and broader understanding of customers and their interactions with products. This expanding knowledge base further allows a greater proliferation of product choices to satisfy increasingly diverse and sophisticated consumers.
Still, product development undoubtedly entails tremendous challenges. Many companies struggle with products that are slower to market than planned, fail to meet cost objectives, or are saddled with late design changes. Although no single recipe exists for product development success, one common thread is the ability to effectively integrate engineering resources within product and process design along with sales, marketing, manufacturing, and most importantly the end user.
Design for Six Sigma (DfSS) is a methodology that emphasizes the consideration of variability in the design process, resulting in products and processes that are insensitive to variation from manufacturing, the environment, and the consumer. The role of DfSS within new product development is to become an enabler of better integration of these resources to provide a deeper knowledge of product performance drivers and capabilities. An excellent example may be observed through GE, which has aligned the tools and best practices of DfSS within their product development process (Creveling et al., 2003). This chapter discusses the major phases of new product development with an emphasis on the roles engineers and DfSS resources play in effectively launching new products.
The time to develop a new product often depends on product complexity, which typically is a function of the technology readiness level (Assistant Secretary of Defense for Research and Engineering, 2011), the number of components, and the difficulties associated with manufacturing. In the case of an automobile, product development typically requires at least 2 years depending on the extent of the redesign. For example, if a manufacturer uses an existing powertrain and interior body frame, the development time may be reduced to less than 2 years. Product development times in aerospace industries typically range from 3 to 4 years, while the electronics industry is much faster with lead times of 6–12 months depending on the complexity of the product.
Although the total time for new product development will vary by design complexity and technological availability, the basic steps involved are common. Clark & Fujimoto (1991) and others (Tennant, 2002; Clausing, 1994) have provided basic descriptions of the product development process. The general phases (or steps) of new product development include concept development, product planning, product engineering design and verification, process engineering, and manufacturing validation as shown in Figure 1.1. The ideal situation for employing DfSS is to integrate it within these steps. To do so, one must acquire true customer needs and then apply the discipline of DfSS within the phases to efficiently transform customer needs into desirable products and services. DfSS and product development are complementary to each other and they can be implemented in parallel (Yang & El-Haik, 2003).
Figure 1.1 The phases of product development.
The following sections describe these phases in greater detail and discuss the roles of engineers and the integration of DfSS methods to improve their effectiveness.
During concept planning, manufacturers gather information on future market needs (voice of the customer), technological possibilities, and economic feasibility of various product designs. Many companies begin concept planning by expressing the character or image of their product in verbal, abstract terms using basic questions such as:
In defining a product concept, manufacturers often conduct three key assessments. These include assessing the voice of the customer, capabilities of the competition, and technological capabilities within the company.
The primary step in the development of a new product is the determination of the customer's wants and needs. Obtaining the voice of the customer traditionally has been the responsibility of Sales and Marketing who may conduct market studies, customer surveys, interviews, or use past sales data to identify market needs and trends. Although marketing is primarily responsible for customer research, under a DfSS framework, companies include more technical specialists such as product engineers in voice of the customer studies. The inclusion of technical specialists often accomplishes two objectives. First, product designers gain a better perspective of customer desires by mitigating the marketing filter. Second, technology specialists often are better suited to interpret emerging desires because of their deeper understanding of new technologies in development or existing ones in other industries that could be applied to their products.
To gain insight into consumer purchasing influences, Kano's method of analysis is a useful tool (Berger et al., 1993). Successful applications of Kano's methods require skill and experience. Translating customer wants and needs into product decisions remains a mix of art, science, and sometimes just good fortune.
To further assess the market, many companies conduct benchmarking studies of their competitors. Benchmarking is the continuous process of comparing one's own products, services, and processes against those of leading competitors. Although manufacturers typically benchmark direct competitors, they occasionally examine leaders in other industries. For example, car and bicycle manufacturers may benchmark airplane designs for ideas on how to make their products more aerodynamic, or for methods to improve internal processes.
To analyze complex products, today's manufacturers may even purchase their competitors' products and disassemble them down to evaluate the design. Here, companies are concerned with the inner workings of a product and how it is manufactured rather than its external appearance. Many companies set up “war rooms” where they make displays of competitor product components allowing internal engineers to review other designs and activate the creative process. In many cases, these war rooms provide a tremendous catalyst for making improvements. While one has to be careful to prevent benchmarking from leading to “look-alike” products, it can be a valuable tool to generate new ideas, which undoubtedly is necessary for continuous improvement of a product design.
The culmination of the concept and initial planning phase is often referred to as concept approval. This is an important date, because it typically is when financial resources are committed to bringing the product to market. While a company may reject a new product later in development, concept approval is generally “when the clock starts ticking.”
Once a concept is approved, a manufacturer must translate it into more concrete assumptions and detailed product specifications. In the language of DfSS, this involves the translation of customer requirements into product functional requirements, product attributes, and product features. This invariably consists of trade-offs between cost, functionality, and usability. Consider the design of an...
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