Chapter 32 - Design for Quality

MANAGERS AT WORK As popularly used in industry, achieving a Six Sigma level of quality means limiting defective performance to 3.4 per million opportunities, which would be nearly defect-free. Elimination of defects has interesting implications for research and development, because RD however, their familiarity with Six Sigma varied widely-from novices to Black Belt experts. Therefore, the panelists took time to explain three important Six Sigma points, namely that Six Sigma and Design for Six Sigma: * Are philosophies-the core philosophies for defining problems in process-based performance. * Are not the same-Six Sigma (SS) involves finding and fixing problems in existing processes whereas design for Six Sigma (DFSS) involves designing errorfree processes after you realized that fixing the existing processes would not resolve your performance concerns. o Are not tools-they are core organizational competencies. Following this, the panelists explained that Six Sigma and Design for Six Sigma are implemented by applying tools and techniques within the bounds of the philosophy. The candidate tools and techniques, used in both, involve familiar things we saw in TQM, some new vocabulary, and critical process management and project selection activities. Techniques used in Six Sigma are listed on the next page. Customer-centric goals and customer-focused metrics strongly drive the core Six Sigma philosophy that will, in the long run, be part of the way surviving firms operate. Rather than providing distinct competitive advantage, they will be prerequisites for competition. Panelists emphasized that factors important to success in Six Sigma and Design for Six Sigma are no different from the important attributes of quality in other areas of business and manufacturing. From that perspective, they noted the most important factors of success in Six Sigma and Design for Six Sigma: 1. Commitment and leadership from the top of the organization. 2. Repeatable project selection and management processes involving rigorous project administration, commitment management, control of costs, schedules, changes, and production, as well as quality assurance and configuration management. 3. An understanding of the customer's value proposition, very early in the process. 4. Metrics to prove and track performance. 5. Learning and using a common language for improvement. 6. Providing and maintaining adequate funding for improvement efforts. Among the foregoing points, panelists held that topdown leadership and culture were most important, and that tracking metrics, common language and adequate funding had more impact than panelists had seen in comparable TQM efforts. Finally, the panelists pointed out that in building a Six Sigma culture within an organization, it is important not only to start at the top but to start big (in depth and scope), not small. …

For organizations, long-term success is tied directly to the development process of new product. Design for Six Sigma(DFSS) methodology can encourage organizations to adopt the best design practices for achieving competitive advantage and business excellence. In this paper, the performance of integrating DFSS in new product development process is presented and discussed, and the results show that this method is beneficial to improve product performance.

A company that wants to compete in the market must operate deploying flexibility, minimising production costs, speeding up processes and assuring a high-quality level of output. For these reasons it is necessary to apply innovative and structured methodologies able to suggest techniques and methods to design, organise and manage industrial processes. Design for Six Sigma (DFSS) represents one of the most effective methods to attain increases in business performance and customer satisfaction using a smart problem-solving method (DMADV). This paper shows an implementation of a DFSS project in a SME to improve the performance level of the extrusion process. The authors demonstrate an effective approach that is able to design and reach Six Sigma level process, by applying project management, brainstorming and statistical (ANOVA, DOE) tools. The economical analysis justifies the application of this method in order to improve the performance level of a manufacturing process.

<div class="htmlview paragraph">The concept of design for six sigma (DFSS) offers a framework to design a product and process right the first time. In general, Taguchi's robust design method has been widely adapted in design optimization, which is a critical phase in any DFSS projects. The objective of the paper is to develop an advanced strategy in selecting an optimized product design and manufacturing process that should be insensitive to various multivariate variation patterns of the multi-stage manufacturing system. A Monte Carlo variation simulation based method is presented that integrates Mohalanobis Distance (MD) method, a discriminant analysis technique, to analyze the manufacturing variation patterns detected by using the multivariate statistical tool, such as principal component analysis (PCA). The proposed method will be explained with an example of an automotive assembly.</div>

The robust design of a new product is exemplified in this paper as an exercise that every engineering student could do for a better understanding of the DFSS (”Design For Six Sigma”) process. The design of a new paper binder is presented through several steps that make the process easy to follow and to understand by the student. Both classical mathematical pursuit and modern technology are presented as viable ways of solving the problem.

Optimization method and numerical simulation technology have been applied in sheet metal forming process to improve design quality and shorten design cycle. However, deterministic optimization may lead to unreliable and non-robust design due to not considering the fluctuation of design variables, environments and operation conditions, etc. In addition to that, iterations in optimization process may cause numerous simulation time or expensive experiment cost. A CAE-based six sigma robust design procedure is developed in this paper with the purpose to eliminate effect of uncertainties in design so as to improve quality. By applying Design of Experiment, Analysis of Variance, and Dual Response Surface Model, this procedure integrates Design for Six Sigma (DFSS), reliability optimization and robust design together to perform optimization. Deep-drawing processes of square cup (NUMISHEET’93) and cylindrical cup (NUMISHEET’2002) are studied as examples, in which thickness variation is taken as the optimization objective, and wrinkle and rupture criteria are taken as constraints. The results show that this design optimization method not only improves significantly the reliability and robustness of the forming quality, but also increases design efficiency with approximate model.

Numerical simulation technology and optimization method have been applied in sheet metal forming process to improve design quality and shorten design cycle. While the existence of fluctuation in design variables or operation condition has great influence on the quality. In addition to that, iterative solution in numerical simulation and optimization usually take huge computational time or endure expensive experiment cost In order to eliminate effect of perturbations in design and improve design efficiency, a CAE‐based six sigma robust design method is developed in this paper. In the six sigma procedure for sheet metal forming, statistical technology and dual response surface approximate model as well as algorithm of “Design for Six Sigma (DFSS)” are integrated together to perform reliability optimization and robust improvement. A deep drawing process of a rectangular cup is taken as an example to illustrate the method. The optimization solutions show that the proposed optimization procedure not only improves significantly the reliability and robustness of the forming quality, but also increases optimization efficiency with approximate model.

Satisfying consumer needs is the dream of every company. Apart from being an important factor for the company's survival, meeting consumer needs can increase its superiority in competition. Consumers who are satisfied with product design and product quality tend to buy back the product and reuse the product when the same need arises at a later date. This study aims to determine the effect of product design and product quality on consumer satisfaction at Honda Scoopy on Astra Motor. This study uses a quantitative approach with a survey method, while this type of research includes associative research. The population in this study are consumers who have used Honda Scoopy, the number of samples taken in this study were 100 respondents. The sample used in this study was purposive sampling. The research instrument used a questionnaire with a Likert scale. Data analysis used multiple linear regression, correlation test, determination test, partial test analysis with t test and f test (simultaneous). To test the effect between variables using SPSS (Statistical Service Product Solutions) version 23.00. The results based on the t test (partial) show that product design affects customer satisfaction, product quality affects product satisfaction, while product design and product quality simultaneously affect product satisfaction.

Once hailed as the salvation of U.S. manufacturing competitiveness, concurrent engineering (CE) offers the potential for faster development of higher quality, more producible products. Unlike traditional, serial approaches to new product development (NPD), CE emphasizes cross‐functional integration and concurrent development of a product and its associated processes.As Morgan L. Swink, J. Christopher Sandvig, and Vincent A. Mabert explain, however, CE is not a plug‐and‐play process. Successful CE implementation approaches differ depending on such factors as product characteristics, customer needs, and technology requirements. We can better understand those differences by examining CE implementation in the five NPD programs discussed here: the Boeing 777 aircraft, the heavy duty diesel engine at Cummins Engine Co., the thermoplastic olefin automotive coating at Red Spot Paint and Varnish Co., the airborne vehicle forward‐looking infrared night vision system at Texas Instruments, and the digital satellite system at Thomson Consumer Electronics.Teams provide the primary integration mechanism in CE programs, and three types of teams appeared frequently in these projects: a program management team, a technical team, and numerous design‐build teams. Depending on the project's complexity, an integration team may be needed to consolidate the efforts of various design‐build teams. Task forces also may be formed to address specific problems, such as investigating an emerging technology.Some projects emphasized collocation and face‐to‐face communication. Others relied on phone conversations, documents, and electronic mail. Projects focusing on design quality relied on formal presentations and periodic review meetings. Projects emphasizing development speed required frequent, informal communications. Programs addressing design quality required extended product definition and performance testing, with input from design engineering, marketing, and customers. Efforts to reduce development time involved small, informal teams led by design engineers and managers. Aggressive product cost goals necessitated intensive interaction between product designers and manufacturing personnel. Highly innovative products required early supplier involvement and joint engineering problem solving. Formal design reviews and shared design data systems aided information sharing between internal and external design groups.

Automation and robots are frequently mentioned as solutions to industrywide problems of increasing costs, declining productivity, skilled‐labor short‐ages, safety, and quality control. Despite numerous attempts to develop automation and robotics for construction field operations, few practical applications can be found on construction sites today. The promises of robotics remain unfulfilled, and attempts to transfer automation technology from manufacturing have not been optimal. Identification of opportunities for automation requires analysis of construction work at the appropriate level. This paper presents a hierarchical taxonomy that divides construction field operations into several levels. The basic‐task level is the appropriate level for construction automation. The paper presents a set of basic tasks that describe construction field work. Construction is compared to highly repetitive manufacturing operations to gain insight into the relationships among product design, process design, and fabrication. In manufacturing, product and process design are closely interrelated. In construction, process design is completely separate from product design, but is intimately related to fabrication. Until construction product and process design become more highly integrated, automation must occur at the basic‐task level. Advances in construction automation will continue to be characterized by a machine performing physically intensive basic tasks, operated by a human craftsperson performing the information‐intensive basic tasks.

This study aims to determine that product design, brand image and product quality influence purchasing decisions on Oppo brand mobile phones. This study uses quantitative research with hypothesis testing. The sampling technique used in this study used a non-probability sampling technique with an accidental sampling method of 96 respondents. And the data analysis technique uses data quality test, classical assumption test, and multiple linear regression analysis. The results of this study indicate that product design and product quality have a significant effect on purchasing decisions, while brand image has no effect on purchasing decisions.

Cables exist in a large number of complex electronic devices, the quality of cable process design has a direct impact on the service quality and efficiency of the equipment. Cable process planning is a complex, time-consuming, and typically knowledge-intensive task that involves product information, process routing, parameters, and material selection, depending on the experience and knowledge of the process designers heavily. However, the unstructured and tacit nature of the knowledge makes it difficult to reuse. To implement knowledge-based intelligent cable process reasoning, and increase the design quality of the cable process plan while lowering the cost, it is critical to managing the cable process knowledge systematically and effectively. This paper proposes an ontology-based modeling method for cable product and process knowledge, in this approach, (1) A cable knowledge model containing cable product description and process plan is built; (2) Case-Based Reasoning (CBR) is employed to reuse knowledge from the previous case with the maximum similarity to realize rapid cable process planning, reducing the time and cost of process planning. In addition, a customized control cable process planning is taken as an example to verify the feasibility and effectiveness of the proposed method.

Data searching is an essential process in design, and research within the design field indicates a distinction in how expert designers and novice designers approach design problems. Expert designers, possessing a wealth of specialized knowledge, tend to search for additional information less frequently during the design process. In contrast, novice designers, due to their lack of professional knowledge, often need to search for, redefine, or organize information. (Ho, 2001; Cross et al., 1994). Consequently, novice designers tend to spend a significant amount of time repeatedly searching for information, leading not only to time wastage but also to designs that lack comprehensive consideration. Therefore, this study posits that the data collection process at the onset of design significantly affects the quality of design outcomes, necessitating further exploration of the relationship between various search strategies and design quality.The methodology involves think-aloud protocols, with the selection of coding systems based on search strategies divided into 0 to 3 dimensions as proposed by Gero and McNeill (1998) within the "problem domain" abstraction. Dimension 0 defines the system from the product's usage and user needs; 1 pertains to the product's interaction, styling, and imagery; 2 involves subsystems from product specifications, functions, behaviors, principles, and pain points; 3 considers details, integrating product principles with design elements and local details for effective optimization within the design concept. Additionally, regarding the quality of design outcomes, this study draws from the basic criteria for product design concepts proposed by Li, Feng-Qiang et al. (2016), encompassing ten aspects: functionality (F), usability (U), aesthetics and form (A), innovation (I), sustainability (ST), possibility (P), safety and regulation (SA), and marketability (M). These ten design criteria serve as a standard for assessing the quality of product design. Through the "problem domain" and "ten design criteria," this study aims to explore the differences in data search processes between novice and expert designers and the resulting impact on design quality.The findings reveal that novice designers mainly focus on the shallow, basic search dimensions of 1 and 2, lacking in-depth understanding of product details, which often leads to insufficient information for design execution. Experts iterate across dimensions 0 to 3, gaining a comprehensive understanding of the product, which better supports the subsequent design process. In terms of design criteria, novices focus on aesthetics/form (A) and innovation (I), with functionality also considered to some extent, while other aspects appear to be unconsciously neglected. Moreover, interviews and data organization revealed that novices tend to search from their personal experiences, whereas experts start from user needs, leading to more effective and accurate searches. Hence, in design education, teaching students to conduct comprehensive data collection could significantly enhance their design quality.

Design for Quality (DFQ) is a design concept for product quality. In the product design stage, it uses reasonable theory or method and accurately grasps the needs of the market. Then the customers’ needs and the quality characteristics will be combined into the product design process. Based on contrasting and analyzing the characteristics of Quality Function Deployment (QFD) and Design of Experiment (DOE) in details, the p puts forward the integration frame of these two design tools. It will not only exert each advantage and compensate each shortage, but also increase product design quality and construct competitive advantage for satisfying the customers.

Challenges in the design of formulated products: multiscale process and product design