Six Sigma and the Evolution of Quality in Product Development


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Six Sigma and the Evolution of Quality in Product Development

  1. 1. DESIGN FOR SIX SIGMA Six Sigma and the Evolution of Quality in Product Development By Larry R. Smith, Ford North American Truck G eorge Box had it right when he said, “All models are wrong but some are useful.” Nam Suh, chairman of the Mechanical Engineering THE EVOLUTION OF SIX SIGMA PARALLELS THAT OF QUALITY METHODS. DESIGN FOR Department at Massachusetts Institute for Tech- SIX SIGMA CAN HELP TAKE YOUR nology (MIT), developed a very useful model of Product Development as a mapping of elements ORGANIZATION EVEN FARTHER. between various domains (customer attributes to functional requirements to design parameters to process variables). Peter Senge, also of MIT, developed a useful model concerning ways or lev- els of thinking (in terms of events, patterns or Model of Product Development structure). When these two models are combined, a new model is created that can be used to under- The process of design involves understanding what you want stand the history and evolution of quality and the to achieve and then selecting a strategy that achieves that role of Six Sigma and Design for Six Sigma (DFSS) intent. To better understand the history of quality and the in product development. role of Six Sigma in product development, consider the Quality in product development began with domain model of product development shown in Figure 1. attempts to inspect quality into products or serv- Nam Suh, chairman of the Mechanical Engineering ices either in the process domain (scrap and Department at MIT, created this model, applicable to the rework), the design domain (verification tests development of either products or services, in the late 1970s.1 and durability failures) or the customer domain Suh believes the creation of great products or services (warranty costs and complaints). The evolution involves selecting strategies associated with four primary activ- of quality involved a significant mind-set transi- ities or domains: customer domain, functional domain, phys- tion from reacting to inspection events to utiliz- ical domain and process domain. The customer domain con- ing process patterns in engineering and manu- sists of customer attributes—a characterization of needs, facturing to build quality into the product. Recent wants or delights that define a successful product or service developments in quality engineering involve the from a customer perspective. The functional domain consists use of structural tools to lay the proper founda- of functional requirements—a characterization of design tion for good design and enable the process- goals or what the product or service must achieve to meet cus- level methods to work better. Six Sigma is used tomer attributes from the viewpoint of the designer. The to react to or fix unwanted events in the cus- physical domain consists of design parameters—the collec- tomer, design or process domains. DFSS is tion of physical characteristics or activities that are selected to used to prevent problems by building quality into meet functional goals. The process domain consists of process the design process across domains at the pat- variables—the collection of process characteristics or tern level of thinking. Use of new structural tools resources that create the design parameters. such as TRIZ (a Russian acronym for “theory of The development of products or services is highly iterative inventive problem solving”) and axiomatic and involves selecting elements in each domain and mapping design provide a foundation for future enhance- these elements from one domain to another. The better the ment of Six Sigma methodologies. mapping between these domains, the better the design. 28 I N O V E M B E R 2 0 0 1 I W W W . A S Q . O R G
  2. 2. Figure 1. Nam Suh’s Domain Model of Product Development Customer Functional Physical Process domain domain domain domain Customer Functional Design Process attributes requirements parameters variables From Axiomatic Design: Advances and Applications, Nam P. Suh (New York: Oxford University Press, 2001). Used with permission of Oxford University Press and Nam P. Suh. Figure 2 illustrates the generic nature of this model, The History and Evolution of Quality which is literally applicable to any design activity. The evolution of design is correlated with the evolu- The early history of quality in product development tion of our thinking. Peter Senge, a professor at MIT’s was based upon event thinking in the various design Sloan Management School, describes three levels of domains (see Figure 4, p. 30). thinking: events, patterns, and structure.2 The event After World War II, the primary way of assuring level is all too familiar. Something happens; we find out quality to customers was inspection after the process about it after the fact and are forced to react. domain. Parts were produced, and then these parts Organizations typically react to significant short-term were checked to see if they were good enough to ship. events in measures such as sales, profits, quality, etc. If the parts were not good, then an event occurred, Pattern thinking involves understanding longer-term resulting in rework or scrap and problem solving. trends and assessing implications. For example, a graph Event thinking also occurred in the physical of U.S. automotive market shares for Japanese, Korean domain. Many engineers simply threw a design and German automobile companies over the past together and then tested it, expecting the design to decade is an interesting pat- tern that should not be Figure 2. The Domain Concept in Various Fields ignored by U.S. automakers. Structure thinking involves Customer Functional Physcical Process looking at the total system to Field domain domain domain domain understand how system ele- Manufacturing Consumer Functional Design Process ments relate to each other, needs/wants specifications parameters variables and what in the system causes Materials Desired Required Microstructure Processes the patterns to behave the performance properties way they do. Figure 3 (p. 30) Software Attributes Output Input Subroutines overlays Senge’s levels of variables/ algorithms thinking onto Suh’s domain model of product develop- Organization Customer Organization Programs, People, satisfaction functions offices resources ment. This figure provides a Systems System System Components Resources convenient framework for attributes requirements machines thinking about the evolution of quality and the role of Six From Axiomatic Design: Advances and Applications, Nam P. Suh, (New York: Oxford University Press, 2001). Used with permission of Oxford University Press and Nam P. Suh. Sigma. S I X S I X S I G M A F O R U M M A G A Z I N E I N O V E M B E R 2 0 0 1 I 29
  3. 3. Six Sigma and the Evolution of Quality in Product Development Figure 3. Senge’s Levels of Thinking Overlayed on Suh’s Domain Model of Product Development Customer Functional Physical Process domain domain domain domain Structure Patterns Events fail. The failure of a design verification test is an event of quality associated with event thinking in 1996 was that the engineers answered with a sequence of estimated to be as high as $10 billion each year in build/test/fix cycles. Build/test/fix is actually a scrap, reworking of parts, correction of transactional method used today by many designers to inspect qual- errors, inefficiencies and lost productivity.3 ity into the product or service. It is hoped the design Event thinking companies, which typically operate at a gets band-aided enough so it will function properly sigma level between 3 and 4, reap huge benefits by before the product or service gets to the customer. implementing Six Sigma. Six Sigma is an effective prob- Otherwise, the inevitable result is consumer com- lem solving methodology, and companies can utilize it to plaints and warranty in the customer domain. recoup a portion of their cost of quality. Black Belts, who Unfortunately, many companies today depend target projects based on warranty costs, test/durability upon event thinking to assure quality to customers. failures and manufacturing scrap/rework/productivity These companies learn about customers through issues, save, on average, $230,000 per project.4 analysis of warranty, try to assure design integrity via Pattern level thinking was seriously introduced to prod- batteries of expensive reliability tests and rely on uct development when W. Edwards Deming, Joseph M. checks after assembly to assure that the product is Juran and others were invited to Japan shortly after World good enough to ship. At a company like GE, the cost War II. In 1950, Ichiro Ishikawa, president of the Union Figure 4. Event Thinking in the Various Design Domains Customer Functional Physical Process domain domain domain domain Structure Patterns Warranty and Inspection customer Verification and scrap/ Events complaints tests rework 30 I N O V E M B E R 2 0 0 1 I W W W . A S Q . O R G
  4. 4. Six Sigma and the Evolution of Quality in Product Development THE PROBLEM WITH TRUE QUALITY CHARACTERISTICS IS THAT THE DESIGNER CANNOT DIRECTLY USE THEM. of Japanese Scientists and Engineers, arranged for the designer cannot directly use them. For example, a Deming to meet with the 21 top management executives customer may want the steering of an automobile to be of Japanese industry and lecture about quality. Deming comfortable. An engineer cannot write on a drawing, began by introducing some ideas he had learned from “Make the steering comfortable.” The engineer must Walter Shewhart, specifically the plan-do-study-act cycle find substitute quality characteristics, dimensions or of learning and statistical process control (SPC). characteristics of the design that are correlated with cus- SPC, a pattern level quality method in the process tomer desires but have meaning to an engineer. domain, focuses on patterns or trends in process data Therefore, Ishikawa said that the designer must cre- so that the process can be adjusted before an inspec- ate a map that moves from the world of the customer to tion event occurs. When Japanese companies began to the world of the designer. He used a tree diagram to cre- implement SPC, quality improved dramatically. For ate such a map and called these maps “quality tables.” the first time, product or design engineers knew that The Kobe Shipyard of Mitsubishi Heavy Industries cre- the parts they designed were being manufactured ated the first quality table in 1972. Once the quality according to print. table was completed, Ishikawa felt the designer had a Companies begin to use Six Sigma at the pattern customer-driven definition of a good product or service. level when they target Six Sigma for variation reduction This definition or function of quality could then be in the process domain at their own and at supplier facil- deployed into the product development activity. Thus ities. Use of Six Sigma in this way can transition a com- quality function deployment (QFD) was born. pany to sigma levels between 4 and 5. At a level of about 5 sigma, companies hit a wall and progress comes to a Popular and Powerful Methods standstill. Further improvement requires use of pattern thinking in the customer, functional and physical In subsequent years, about 120 different quality tools domains, as well as in the process domain. and methods have been created at the pattern level for Use of pattern level thinking in the other design designers to manage product development process domains began when Kaoru Ishikawa, known for trends, making inspection events a nonevent. Ishikawa diagrams and formalization of quality circles, Some of the most popular and powerful methods are noticed that even though parts were being made to shown in Figure 5 (p. 32) and, in addition to SPC and print, customers were still unhappy with the prod- QFD, include: failure mode and effects analysis ucts.5 Specifications and tolerance limits were stated (FMEA) for both the product and process domains, in the drawings. Measurements and chemical analysis Genichi Taguchi's methods of parameter design (for were being performed. Standards existed for all these the product and process domains) and tolerance things and the standards were being met, but these design (for the product domain), design for assembly standards were created without regard to what the (DFA) and design for manufacturing (DFM), which customer wanted. improve the mapping from the product to the process Ishikawa wrote, “When I ask the designer what is a domain, and system engineering, value analysis (VA) good car, what is a good refrigerator and what is a good and value engineering (VE) in the functional domain. synthetic fiber, most of them cannot answer. It is obvi- The transition from event thinking to pattern think- ous that they cannot produce good products.” You sim- ing is the transition from find and fix to prevent. In ply cannot design a good product or service if you do the words of Henry Wadsworth Longfellow, “It takes not know what “good” means to a customer. Ishikawa less time to do a thing right than it does to explain encouraged people to think at the pattern level in the why you did it wrong.” So then why not do it right the customer domain, instead of just reacting to a warranty first time? The payoff in warranty savings, customer event. He said that if you don't know what a good prod- satisfaction and productivity more than offset the rel- uct is, ask your customers. Customers will give you what atively modest investment in longer-term thinking. Ishikawa called the true quality characteristics. The transition from event thinking to pattern think- The problem with true quality characteristics is that ing is also the transition from Six Sigma to Design for S I X S I G M A F O R U M M A G A Z I N E I N O V E M B E R 2 0 0 1 I 31
  5. 5. Six Sigma and the Evolution of Quality in Product Development Figure 5. Pattern Thinking in the Various Design Domains Customer Functional Physical Process domain domain domain domain Structure VA/VE FMEA DFM Patterns QFD Parameter SPC Systems engineering and tolerance DFA design Warranty and Inspection customer Verification and scrap/ Events complaints tests rework Figure 6. Quality Evolution in the Various Design Domains Customer Functional Physical Process domain domain domain domain Axiomatic Axiomatic Structure TRIZ design design directed evolution TRIZ TRIZ VA/VE FMEA DFM Patterns QFD Systems Parameter SPC engineering and tolerance design DFA Warranty and Inspection customer Verification and scrap/ Events complaints tests rework Six Sigma (DFSS). Companies that rely on event When the foundational structure of design is proper- thinking and utilize Six Sigma realize that about 80% ly established, the methods at the pattern level are of the problems they are fixing (and the money they much more effective. When pattern level methods are saving) are determined by design. DFSS is a rigor- work well, the event outcomes become world-class. ous approach to designing products and services from In the evolution of quality, two very powerful design the very beginning to ensure that they meet customer methods have emerged at the structural level: expectations.6 DFSS is an integration of all the prevent axiomatic design and TRIZ, a Russian acronym for quality tools across the pattern level domains. Use of “theory of inventive problem solving” (see Figure 6). DFSS results in sigma levels between 5 and 6. Further Axiomatic design is the result of work by Suh. In the improvement requires implementation of structural late 1970s, he asked himself the question, “Can the thinking tools. field of design be scientific?” Suh wanted to establish Thinking at a level of fundamental structure offers a theoretical foundation for design by identifying the even higher leveraged opportunities to create prod- underlying principles or axioms that every great ucts and services that not only function as intended, design must have in common. He knew that he could but also deliver unprecedented customer satisfaction. never prove that these principles were true, but could 32 I N O V E M B E R 2 0 0 1 I W W W . A S Q . O R G
  6. 6. Six Sigma and the Evolution of Quality in Product Development Figure 7. Water Faucet Example to lift the handle to adjust flow and move the handle from side to side to adjust temperature. In this design, adjusting temperature does not affect flow and adjusting flow does not affect temperature. From an axiomatic design point of view, this design is superior because the func- tional requirements maintain inde- pendence. Imagine what happens when a Functions for water faucet: designer is working in a situation FR1 = Control flow rate FR2 = Control temperature with a dozen or more functional requirements. If the design is cou- Design 1: Design 2: DP1 = Hot water knob DP1 = Handle lifting pled, then optimization of one func- DP2 = Cold water knob DP2 = Handle moving side to side tion may adversely impact several other functions. When these func- tions are fixed, the original function no longer works well. The designer is always tuning and Band-Aiding such he find a set of principles for which no one could find a design, and the customer will never be completely a counterexample? After a decade of work, two prin- happy. However, if the design is created in such a way ciples emerged. From these two principles, theorems that each of the functional requirements is handled and corollaries could be derived that, together, form independently by the design parameters, then each a scientific basis for the structure of great design. function of the design can easily be optimized with The first principle that Suh discovered was the pattern level tools. principle of independence. Consider Suh’s domain The principle of independence can be used to eval- model shown in Figure 1 (p. 29). Mapping between uate how good a design will be when the design is still domains represents a mapping of whats to hows. The on paper. But suppose you have two design alterna- principle of independence states that in great designs, tives that both follow the independence axiom. Now the hows are chosen in such a way that the whats which one is better? The second principle states that maintain independence. For example, design param- the better design will minimize the information con- eters must be chosen in such a way that functional tent necessary for implementation.7 Designs that have requirements maintain independence. Consider the a solid axiomatic foundation simply work better than water faucet designs shown in Figure 7. designs that do not. The functional requirements for a water faucet are Suppose the designer cannot find a set of design two: control the flow rate and control the water tem- parameters that keep all the functional requirements perature. The faucet on the left of Figure 7 has two independent. In this situation, improving one func- design parameters: a hot water knob and a cold water tion typically degrades another. An example in auto- knob. What is the relationship between these design motive steering is steering road feel and parking parameters and the functional requirements? When efforts. When the steering efforts are high, the cus- the hot water knob is turned, temperature is affected tomer experiences good road feel. However, high and so is flow. Turning the cold water knob also affects efforts can make it difficult for customers to park. temperature and flow. Therefore this design is cou- Adjusting efforts to make the vehicle easy to park will pled and the functional requirements are not inde- result in degraded road feel. pendent. If a consumer has optimized flow rate, then A typical approach to resolve this situation is com- turns one of the knobs to optimize temperature, the promise—trade off customer functionality and hope flow rate is changed and is no longer optimal. Designs for the best. This is where TRIZ is most helpful. TRIZ of this type eventually satisfy customers by iterating is the result of more than 45 years of research by between the two design parameters. Genrich Altshuller and colleagues.8 Altshuller hated Consider the design on the right of Figure 7. This compromise. He called the situation where functions faucet has one handle and the design parameters are oppose each other contradictions and developed a S I X S I G M A F O R U M M A G A Z I N E I N O V E M B E R 2 0 0 1 I 33
  7. 7. Six Sigma and the Evolution of Quality in Product Development Figure 8. Focus of Six Sigma and Design for Six Sigma Customer Functional Physical Process domain domain domain domain Axiomatic Axiomatic Structure TRIZ design design directed evolution TRIZ TRIZ VA/VE FMEA DFM Patterns QFD Systems Parameter SPC engineering and tolerance design DFA Warranty and Inspection customer Verification and scrap/ Events complaints tests rework Design for Six Sigma Six Sigma methodology in which design teams could systemati- the next developments of the system will be. This is a cally innovate and find design parameters that huge competitive advantage. Companies that operate resolved contradictions, creating win-win functional at the event level obtain information about the cus- situations. The methodology began by identifying all tomer through warranty data. Companies at the pat- possible contradictions that existed in patent databas- tern level interact with customers and find out what es and identifying how these contradictions were customers believe is important today. No customer resolved. Altshuller found that only a few particular can tell the designer what will be important tomorrow. principles of resolution have ever been used in the At the structural level, TRIZ directed evolution pre- history of mankind to resolve certain pairs of func- dicts what will be important to customers tomorrow. tional contradictions. So if TRIZ works at the structural foundation of For example, suppose the functions of weight and design, why does the name imply problem solving? The reliability contradict. When the design is changed to answer is simply that higher level thinking can always improve reliability, weight increases. When weight is be used as a methodology to solve lower level problems. decreased, reliability degrades. Altshuller found there The fact that problems exist in the event realm means are only four principles that have ever been used to that the original process of design had serious flaws— resolve this contradiction.9 He created a matrix of pattern or structural work that should have been done contradictions and resolution principles and used this is either missing or incomplete. The designer can information to guide design teams so that they could always go back and complete this work at anytime. brainstorm in areas that are likely to lead to win-win This is why completing work associated with pattern solutions. Altshuller also believed in minimizing infor- or structural tools can quickly lead to problem resolu- mation content; he called this the principle of ideali- tion. A good example of this is Six Sigma. It addresses ty. Later, TRIZ was expanded to include an entire problems created by event level thinking, but the algorithm of innovation techniques, including the methodology of Six Sigma utilizes pattern level tools study of system evolution. such as FMEA. Altshuller found that systems tend to evolve along specific laws and lines of evolution. By studying system The Roles of Six Sigma and DFSS evolution for the past and present (for the supersys- tem, system and subsystem), a designer can identify The product development model in Figure 6 (p. 32) is the current stage of system evolution. By applying laws useful to illustrate the roles of Six Sigma and DFSS and and lines of evolution, design teams can predict what also provides implications for next steps. The role of 34 I N O V E M B E R 2 0 0 1 I W W W . A S Q . O R G
  8. 8. Six Sigma and the Evolution of Quality in Product Development Six Sigma is to solve problems at the event level in the Move Past the Competition customer domain (customer complaints or warranty), the product domain (design or service does not pass Quality tools and methods have evolved utilizing three tests) or the process domain (internal scrap, rework or stages of thinking (event, pattern and structure) capability issues). An effective Six Sigma program, across various domains associated with product devel- even though this program saves lots of money, is still an opment. The evolution of Six Sigma parallels the evo- attempt to inspect in quality by addressing events after lution of quality methods. Six Sigma addresses event the fact. A company that operates only at this level will level concerns that occur in product development never be competitive with a company that prevents after the fact. DFSS represents a higher evolution of problems in the first place using DFSS. the methodology, utilizing pattern level thinking and The role of DFSS is to build quality into the design- tools to build quality into the product or service. by implementing prevent thinking and tools in the The future of Six Sigma and DFSS involves incorpo- product development process. DFSS is, in fact, an inte- rating structural thinking methods such as axiomatic gration of prevent methods at the pattern level across design and TRIZ. Use of these methods will make Six all four domains. The relative roles of Six Sigma and Sigma and DFSS more effective and more productive DFSS in product development are shown in Figure 8. with less effort. Companies that wish to accelerate An effective DFSS program must utilize tools that development of their own quality programs can utilize make a difference in each design domain. For exam- the evolutionary trends explained in this paper to ple, a DFSS program that does not interface with cus- understand their current level of evolution and to tomers or does not utilize powerful methods like implement focused actions that can quickly move Taguchi’s parameter design (to hit the design with them past their competition. noise or uncontrolled variation and adjust controllable REFERENCES factors in the design to make the design robust against noise) is a program that is guaranteed to leave signifi- 1. Nam P. Suh, The Principles of Design (New York: Oxford University cant portions of the work of design incomplete. This Press, 1990). 2. Peter M. Senge, The Fifth Discipline (New York: Doubleday, 1990). will virtually guarantee that the Six Sigma program will 3. Mikel Harry and Richard Schroeder, Six Sigma: The Breakthrough have plenty of issues to work on in the future. Management Strategy Revolutionizing the World’s Top Corporations (New York: Doubleday, 2000). Both Six Sigma and DFSS can be made more effec- 4. Ibid. tive by incorporating structural tools such as axiomat- 5. Kaoru Ishikawa, “Quality Analysis,” 1977 ASQC Conference Transactions (Milwaukee: ASQ, 1977), pp. 423-429. ic design and TRIZ into the methodology. Because 6. Mikel Harry and Richard Schroeder, Six Sigma: The Breakthrough these tools address design foundation flaws, they will Management Strategy Revolutionizing the World’s Top Corporations (see refer- ence 3). enhance every aspect of Six Sigma and DFSS, making 7. Nam P. Suh, Axiomatic Design: Advances and Applications (New York: the process of problem solving and problem preven- Oxford University Press, 2001). 8. Genrich Altshuller and Lev Shulyak, 40 Principles: TRIZ Keys to tion much more insightful, productive and efficient Technical Innovation (Worchester, MA: Technical Innovation Center, 1998). than programs that do not utilize these methods. 9. Ibid. Dilbert S I X S I G M A F O R U M M A G A Z I N E I N O V E M B E R 2 0 0 1 I 35