QUALITY FUNCTION DEPLOYMENT (QFD): INTEGRATION OF LOGISTICS REQUIREMENTS INTO MAINSTREAM SYSTEM DESIGN

Transcription

1 QUALITY FUNCTION DEPLOYMENT (QFD): INTEGRATION OF LOGISTICS REQUIREMENTS INTO MAINSTREAM SYSTEM DESIGN Dinesh Verma Rajesh Chilakapati Benjamin S. Blanchard Systems Engineering Design Laboratory (SEDL) Industrial and Systems Engineering, Virginia Tech Blacksburg, Virginia ABSTRACT Numerous references make a case for the integration of logistics-related activities within the system engineering process. This is consistent with good system engineering practice and with concurrent and simultaneous engineering concepts. For the effective and efficient design and development of systems (products or processes) which are responsive to requirements and competitive in a global economy, this integration must evolve from the nascent stages of system design. Quality Function Deployment (QFD) is a design method which can facilitate this objective. This paper reviews the QFD method, the underlying process and elemental activities, and finally discusses opportunities for logistics engineers to contribute to, and integrate with, the mainstream system design activity. An illustrative QFD matrix is used to depict this integration. Introduction And Background The Quality Function Deployment (QFD) method was developed at the Kobe Shipyard of Mitsubishi Heavy Industries, Ltd., and has evolved considerably since. QFD facilitates translation of a prioritized set of subjective customer requirements into a set of systemlevel requirements during system conceptual design. A similar approach may be used to subsequently translate system-level requirements into a more detailed set of requirements at each stage of the design and development process. The sequence of activities which constitute the QFD method are shown in Figure 1. Further, a QFD matrix, shown in Figure 2, serves as an excellent framework for the coordinated accomplishment of these activities and for the representation and analysis of information involved in the implementation of the QFD method. As a general quality tool (in the TQM context), the QFD matrix is often called the House of Quality [HAU88]. In the context of system engineering, QFD facilitates a strong correlation between customer requirements and design requirements, and the inclusion of supportability requirements within the spectrum of design requirements. As such, the method goes a long way in making the customer an integral part of early design synthesis, analysis, and evaluation activities. The QFD Process Identification of a functional need is a primary input to the QFD process as shown in Figure 1. It is essential that the need be stated in functional terms to avoid premature commitment to a concept or configuration [VER94]. Methods such as customer surveys, interviews, trend analysis, and competition analysis are often used to facilitate identification of a valid need. Organizations which can identify and exploit a not-so-obvious need often gain a strategic headstart over the competition. Activities which comprise the QFD method are discussed in the following subsections. These discussions are conducted in the context of the QFD process shown in Figure 1 and the QFD matrix shown in Figure Need analysis and identification of customer requirements. As a first step, the functional need is analyzed and translated into more specific customer requirements to better understand the perceived deficiency. In essence, the purpose of this step is to capture the Voice of the Customer. Reference to the customer includes not only the end-users, but also the applicable regulations and standards, the intermediate distributors, installers, retailers, and the maintainers. As such, this is the first significant opportunity to integrate logistics requirements and issues into the mainstream design and development process.

2 NEED Identify and Classify Customer Requirements Identify Importance of Customer Requirements Identify Design Dependent Parameters Correlate Requirements and Parameters Check Correlation Grid Benchmark Customer Perceptions Benchmark Design Dependent Parameters Analyze Correlation Grid for Inconsistencies Figure 1. The Quality Function Deployment (QFD) process. Delineate Design Dependent Parameter Target Values and Relative Priorities Properly developed checklists and taxonomies can help ensure a comprehensive and complete identification of customer requirements. Further, consistent and concise translation of the need into customer requirements ensures uniformity of effort, and better understanding and communication between members of a design team. The customer s language is often qualitative and subjective which imparts vagueness and imprecision to this phase of system design. Verma has addressed this imprecision through the use of linguistic variables and a linguistic scale [VER94]. He uses concepts from fuzzy set theory to manipulate this imprecise and vague information. Often the customer requirements are generated through a brainstorming exercise by members of the design team. This approach suffers from a number of crucial drawbacks. More likely than not, this process captures the Voice of the Company or The Voice of the Team Leader rather than the all-important Voice of the Customer. Such practices can lead to poor reception of the ultimate product in the marketplace. Byrne and Barlow [BYR93] have reviewed user brainstorming procedures that can be employed to elucidate customer requirements without the interference of internal opinions. Once identified, similar customer requirements are classified into groups and sub-groups. This develops into a hierarchy of customer requirements, from the most abstract to the most specific. The number of classification levels depends upon system complexity or the extent of detail being represented. 2. Importance of customer requirements. Selected requirements often impact each other adversely. For instance, a customer may desire ease while opening and closing a car door, but at the same time want power windows. Power windows increase the weight of the door and this correlates negatively with the ease of closing or opening it. To overcome such conflicts, requirements are assigned priorities. It is essential that priorities reflect preferences of the customers. There are several approaches to prioritizing customer requirements. These approaches range from direct indication by the customer to usage of the analytical hierarchy process [ARM94] and cost and technical factors [WAS93]. Three or five level priority scales are often used [GUI93; ZUL90; SLA90; HAU88]. While most applications utilize a simple numerical scale for priorities, linguistic scales (utilizing concepts from fuzzy set theory) have also been developed and applied [VER94; WAS93a; MAS93]. 3. Identification of design dependent parameters (DDPs). Design dependent parameters or technical performance measures are engineering characteristics under a designer s control. These parameters are manipulated to directly or indirectlyinfluence customer requirements. In this context, customer requirements are often referred to as the set of WHATs, while design

3 Design Dependent Parameters (DDPs) Importance of customer requirements 3 2 Customer requirements 1 Correlation Matrix 4 Benchmarking customer perceptions Figure 2. The QFD matrix or house of quality. 1 1 This figure represents a screen shot from FuzzyQFD, a computer-based implementation of QFD incorporating fuzzy concepts and principles to better address design imprecision and vagueness. The computer model was developed within the Systems Engineering Design Laboratory at Virginia Tech.

4 set of WHATs, while design dependent parameters represent the set of HOWs. Accordingly to Sullivan [SUL86], These (engineering) characteristics are the product requirements that relate directly to the customer requirements and must be selectively deployed throughout the design, manufacturing, assembly, and service process to manifest themselves in the final product performance and customer acceptance. The DDPs should be tangible, describe the product in measurable terms, and directly affect customer perceptions [HAU88]. DDPs guide the analysis and evaluation of design concepts, configurations, and artifacts during the conceptual, preliminary, and detailed system design phases. As such, it is essential that all relevant DDPs be identified. Once again, development of focused checklists and taxonomies facilitates this objective. A complete and comprehensive set of DDPs includes not only performance related parameters, but also parameters which impact system supportability and cost. 4. Correlation of customer requirements and design dependent parameters. This step of the QFD process involves populating the correlation matrix within the house of quality. Each DDP is analyzed in terms of the extent of its influence on customer requirements. Varying levels of this correlation are represented in the correlation matrix. Depending upon the extent of resolution necessary, three or five levels of correlation are used. Further, correlation between DDPs and customer requirements may be represented through the use of symbols as shown in Table 1. Table 1. Correlation between customer requirements and parameters. Correlation Label Very Low Low Medium High Very High Corresponding Icon 5. Check correlation matrix. It is necessary at this stage to conduct an examination of the correlation grid before proceeding further. This examination involves checking for: Empty rows in the correlation matrix. Empty rows in the correlation grid signify unaddressed customer requirements. In response, the set of design dependent parameters needs to be revisited and, if necessary, additional DDPs identified. Empty columns in the correlation matrix. Empty columns in the correlation grid imply redundant or unnecessary system-level design requirements. The design team may have included design requirements which cannot be traced back to any customer requirement and could potentially be dropped from further consideration. The above two possibilities, and other inconsistencies pertaining to customer requirements, their importance and correlation with design dependent parameters, must be identified and discussed in terms of their implication on system design and development. 6. Benchmarking customer requirements. A key activity involves identification of available systems/products capable of responding to the functional need (to whatever extent). Customer perceptions are then benchmarked relative to how well these capabilities satisfy the initially specified set of requirements. The objective is to assess the state-of-the-art from a customer perspective. It is important that members of the design and development team not influence this activity. Their technical knowledge is likely to bias results [SLA90]. Benchmarking of customer perceptions is facilitated through tools such as customer surveys, customer interviews, demonstrations, media information, and feedback from the marketing, sales and service organizations. The purpose of this effort is to highlight the absolute strengths and weaknesses of the products in the marketplace and those areas of your products that require improvement [SUL86]. This activity provides invaluable insight into avenues where competitive gains can be made most effectively. 7. Technical assessment of design dependent parameters (DDPs). This activity involves assessment of the competition from a technical perspective. Designers and engineers actively participate during this step in the QFD process. Cavanagh [CAV90] has identified some methods and techniques to facilitate the effective accomplishment of this step, to include: a) product testing (baseline the system or product, competitive systems or products, non-competitive but similar systems or products), b) informal evaluations

5 (renting competitors products), and c) contract laboratories. Technical assessments are expressed in quantitative and objective terms, and often convey a need for research and technology development if the current state of the art fails to satisfy important customer requirements. 8. QFD matrix inconsistency analysis. The source, nature, and implication of various inconsistencies in the QFD matrix must be addressed prior to the definition of design requirements. For instance, if results from the technical assessment activity seem contradictory to results pertaining to customer benchmarking, it may signal faulty measures or misinterpretation of customer perception [HAU88]. Research is on-going at Virginia Tech (within the Systems Engineering Design Laboratory) to develop an expert system based QFD consistency checking mechanism. 9. Definition of design dependent parameter target values. This is a critical system design activity since the DDP target values specify the feasible design space and impact subsequent design decisions. Pertinent and strategic opportunities must be identified and exploited. Experience and familiarity with similar systems is invaluable for effectiveness during this activity. Once again, for completeness, logistics-related requirements must be integrated into this step. Comprehensive definition of design requirements facilitates subsequent supportability-related analyses such as definition of the maintenance concept, level of repair analysis, failure mode, effects, and criticality analysis, maintenance task analysis, and so on. Verma has developed two indices, IPN (Improvement Potential and Necessity) and TOF (Tolerance Of Fuzziness), to facilitate the definition of DDPs [VER94]. Development of these indices gives due consideration to the priority of customer requirements, their correlation with design dependent parameters, customers perceptions with regard to existing systems, and their technical assessments. 10. Delineation of design dependent parameter relative importance. To facilitate design analysis and evaluation activities, DDP relative priorities must be delineated. Further, in order to maintain traceability, relative priorities of design dependent parameters are computed from the importance levels assigned to customer requirements and the extent of their correlation with DDPs. Along with the activities identified and discussed thus far, a roof is often developed over the QFD matrix. This mechanism allows delineation of positive and/or negative correlations between design dependent parameters, which in turn facilitates informed trade-offs. This roof is depicted in Figure 3. Application Of The QFD Method The QFD method provides a framework to define and clarify system level objectives and requirements. Further, the QFD process helps consolidate diverse data from numerous sources. This promotes an objective perspective through a minimization of emotion-based reasoning, leading to identification of critical issues and effective decision making [GUI93]. These advantages of the QFD method have led to its application in several areas [BAR92; HAL93; CAD93; LOC93; FUN93; FLY93; and MAL93]. Application of QFD to logistics. A robust design and development effort mandates the early inclusion of supportability requirements into the broad spectrum of system design requirements. This inclusion is consistent with good system engineering practice and must be initially driven through the utilization of focused checklists, taxonomies, and questionnaires. Figure 3 illustrates this concept. The initial identification of support-related customer requirements must be sustained and evolved into a correlated set of system support requirements, and subsequently into sub-system and component-level requirements. Multiple linked houses of quality are often used to maintain this traceability and to sustain the Voice of the Customer throughout the design and development effort. Early integration of supportability issues into design ensures the deployment of a cost-effective support infrastructure responsive not only to the primary system but also the manufacturing and/or construction facility [BLA95; BLA91]. Summary The integration of logistics issues into the mainstream system design process is no longer an option. A highly competitive environment and a shrinking resource base mandate this involvement. System supportability issues must be addressed early during the requirements definition process and evolved progressively through subsequent system design phases. The QFD method offers the necessary framework for the accomplishment of this objective. In summary, potential benefits of using the QFD method include: 1. Customer focused product development. System and product design requirements (to include system supportability requirements) and objectives can be traced from customer requirements. This facilitates

6 inclusion of the Voice of the Customer into the early system design process. 2. Shorter system development cycles. According to Guinta and Praizler, application of QFD helps avoid the need for fire-fighting during detail system design [GUI93]. Emphasis is placed on a before-the-fact approach, rather than after-thefact. Slabey suggests that the overall system or product development cycle time is likely to be reduced by a factor of 33% to 50% through proper and timely implementation of QFD [SLA90]. 3. Enhanced early system design efficiency. According to Slabey, QFD facilitates a proactive, rather than a reactive approach, to system and product design and development [SLA90]. This directly impacts design efficiency through fewer, earlier, and easier-to-incorporate design changes. This is significant since the ease and cost of implementing a design change is a function of the system design phase. The ease of effecting a system design change reduces progressively as one proceeds through the life cycle. Figure 4 presents a comparison (and reduction) of the number of design changes necessary with and without proper application of QFD. Here, the number of design changes made by a Japanese company during product design using QFD are compared to the number of design changes made by a U.S. company which did not use QFD. U.S. company Japanese company Figure 4. Comparison of design changes necessary with and without QFD [HAU88]. 4. Effective early system design tradeoffs. Integrated representation of information from diverse sources within the QFD matrix promotes greater visibility and facilitates accomplishment of effective design trade-offs. Further, trade-off decisions can be tailored to exploit strategic opportunities identified as aresult of the customer benchmarking exercise. Correlation of requirements with DDPs provides insight into necessary product strengths. 5. Fewer production start-up problems. According to Slabey, the proactive approach inherent within the QFD method involves early consideration of downstream issues pertaining to manufacturing, distribution, installation, operation, and sustaining support [SLA90]. This reduces start-up problems. As shown in Figure 5, the Toyota Company in Japan initially experienced a surge of problems at production start. This was addressed and controlled through timely implementation of QFD, resulting in a reduced number of problems across the board. Figure 5. Toyota production start-up problems before and after QFD [SLA90]. 6. Lower start-up costs. Fewer start-up problems translates into reduced start-up costs. Figure 6 shows two curves depicting the result of applying QFD at Toyota Auto Body over a seven year period. The cost index of the program reduced from 100 in 1977 to 39 in This represents a 61% reduction in start-up costs over this seven year period. 7. Reduced deployment and support costs. Benefits of QFD can be derived event after production start in the form of reduced problems for customers resulting in reduced warranty costs and sustaining support costs. References [ARM94] Armacost, R. L., P. J. Componation, M. A. Mullens, and W. W. Swart, AHP Framework for prioritizing customer requirements in QFD: An industrialized housing application, IIE Transactions, Vol. 26, No. 4, July 1994.

7 [BAR92] Barnard,B., QFD-Empowering the multidisciplined team for manufacturing excellence, Figure 6. Startup and pre-production costs at Toyota before and after QFD [SLA90]. Proceedings, APICS International Conference, Montreal, Canada, [BLA91] Blanchard, B. S., System Engineering Management, John Wiley and Sons, New York, [BLA95] Blanchard, B. S., D. Verma, E. L. Peterson, Maintainability: A Key to Effective Serviceability and Maintenance Management, John Wiley and Sons, New York, [BUR94] Burgar, P., Applying QFD to course design in higher education, Proceedings, ASQC Congress, Las Vegas, Nevada, [CAD93] Cadogan, D. P., A. E. George, and E. R. Winkler, Aircrew helmet design and manufacturing enhancements through the use of advanced technologies, Proceedings, SPIE - The International Society of Optical Engineering, Munich, Germany, [CAV90] Cavanagh, J., What Do I Put On My QFD Charts?, Transactions, Second Symposium on QFD, Novi, Michigan, June [FUN93] Funk, P. N., How we used quality function deployment to shorten new product introduction cycle time, Proceedings, APICS International Conference and Exhibition, San Antonio, Texas, [GUI93] Guinta, L. R. and N. C. Praizler, The QFD Book, AMACOM Books, American Management Association, 1993 [HAL93] Hales, R. F., Quality function deployment in concurrent product/process development, Proceedings, IEEE Symposium on Computer- Based Medical Systems, Ann Arbor, Michigan, [HAU88] Hauser, J. R. and D. Clausing, The House of Quality, Harvard Business Review, May-June [LOC93] Locascio A. and D. L. Thurston, Multiattribute design optimization with quality function deployment, Proceedings, 2nd IE Research Conference, Los Angeles, California, [MAL93] Mallon J. C. And Mulligan D. E., Quality function deployment - a system for meeting customers needs, Journal of Construction Engineering and Management, Vol No. 3, Sept [MAN92] Mann, G. A. and L. L. Halbleib, Application of QFD to a national security issue (a case study), Annual Quality Congress Transactions, [MAS93] Masud A. S. M. and E. B. Dean, Using Fuzzy Sets in Quality Function Deployment, Proceedings, 2nd Industrial Engineering Research Conference, [SLA90] Slabey, W. R., QFD: A Basic Primer - Excerpts from the Implementation Manual for the Three Day QFD Workshop, Transactions, Second Symposium on QFD, Novi, Michigan, June [SUL86] Sullivan, L. P., Quality Function Deployment, Quality Progress, June [VER94] Verma, D., A Fuzzy Set Paradigm for Conceptual System Design Evaluation, Dissertation Manuscript, Virginia Tech, Blacksburg, Virginia, [WAS93] Wasserman, G. S., On how to prioritize design requirements during the QFD planning process, IIE Transactions, Vol. 25, No. 3, May [WAS93a] Wasserman, G. S. and G. P. Mohanty, Using Fuzzy Set Theory to Derive an Overall Customer Satisfaction Index, Transactions, Fifth Symposium on Quality Function Deployment, Novi, Michigan, June [ZUL90] Zultner, R. E., Software Quality Deployment - Adapting QFD to Software, Transactions, Second Symposium on Quality Function Deployment, Novi, Michigan, June Biographical Sketches Dinesh Verma received the Ph.D. and the M.S. in Industrial and Systems Engineering in 1994 and 1991,

8 respectively, from Virginia Tech, and the B.S. in Mechanical Engineering in 1986 from Punjab Engineering College in India. He is presently employed as a Senior Research Associate in Industrial and Systems Engineering at Virginia Tech. Verma has authored several technical papers, book reviews, co-edited a technical monograph, co-authored Maintainability: A Key to Effective Serviceability and Maintenance Management (Wiley), and is co-author of the forthcoming textbook Economic Decision Analysis (Prentice Hall). He serves as an invited lecturer at the University of Exeter in the United Kingdom. Verma also serves as the Editor (Book Reviews) for the Logistics Spectrum, Journal of SOLE. Rajesh Chilakapati received the B.S. in Mechanical Engineering in 1990 from K.L. College of Engineering in India. He is currently working as a Research Associate in the Systems Engineering Design Laboratory while finishing the M.S. in Industrial and Systems Engineering at Virginia Tech. Chilakapati is the principal developer of the FuzzyQFD software package. Chilakapati will be joining Price Waterhouse LLP as a Management Consultant later this year. He is active in IIE and SME. Benjamin S. Blanchard received the B.S. in Civil Engineering in 1951 from the University of Maine and the M.B.A. in 1969 from the University of Rochester. Blanchard is now a Professor of Engineering and Assistant Dean, College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. He is Chairman of the Systems Engineering Program, responsible for the College's off-campus graduate engineering programs throughout Virginia, and teaches courses in Systems Engineering, Reliability and Maintainability, and Logistic Support. Prior to joining the academic community in 1970, he was employed in industry for over 17 years as design engineer, field service engineer, and engineering manager with Boeing, Sanders Associates, Bendix, and General Dynamics. Blanchard has authored four textbooks entitled, System Engineering Management (1991), Logistics Engineering and Management (1992), Engineering Organization and Management (1976), Design and Manage to Life Cycle Cost (1976) and co-authored four others, Maintainability: A Key to Effective Serviceability and Maintenance Management (1995), Life-Cycle Cost and Economic Analysis (1991), Systems Engineering and Analysis (1990), and Maintainability Principles and Practices (1969). He has published numerous journal articles, and has lectured extensively throughout North America, Europe, Asia, and Australia.