1Analyzing a Quality Function Deployment (QFD) Matrix: An Expert SystemBased Approach to Identify Inconsistencies and Opportunities

Dinesh Verma, Ph.D.Systems/Supportability Strategist

Lockheed Martin Federal Systems, Inc.9500 Godwin Drive

Manassas, Virginia 20110(dinesh.verma@lmco.com)

Rajesh ChilakapatiPrincipal Consultant

Price Waterhouse LLP1399 Concord Point LaneReston, Virginia 20194

(rajchil@ibm.net)

Wolter J. Fabrycky, Ph.D.Lawrence Professor Emeritusand Senior Research Scientist

ISE, Virginia TechBlacksburg, Virginia 24061

(fabrycky@vt.edu)

ABSTRACTIdentification of a need or a deficiency triggers conceptual system design. The first step in

conceptual design is to analyze and translate the need or deficiency into specific qualitative andquantitative customer and design requirements. Design methods such as Quality Function Deployment(QFD), Parameter Taxonomies, and Input/Output Matrices (IOM) provide a useful framework for thistranslation. Well defined and unambiguous requirements enhance communications and can potentiallyreduce the number of detours during subsequent design and development phases. However,imprecision and vagueness characterize the conceptual design phase. To accommodate imprecision, theQFD method and the concept selection methodology, initially proposed by Pugh [91], have beenmodified and extended by applying concepts from fuzzy set theory [Verma and Fabrycky, 1995; andVerma and Knezevic, 1996]. The extended approach provides a rigorous yet graceful mechanism fordealing with imprecise requirements, priorities, and correlations as prerequisites to concept selection.

This technical paper presents an expert system based extension to the fuzzy QFD methodology.Emphasis is on the: a) identification of strategic market and product opportunities, b) identification ofapplied research focus areas, and c) isolation of inconsistencies between customer articulation offunctional requirements and the definition of system requirements and parameter target values. Anexpert system based parser has been embedded within the Fuzzy QFD tool to facilitate strategic productplanning, early design decision making, and parameter target setting.

INTRODUCTION AND BACKGROUNDA functional need or deficiency is a necessary input to conceptual design. This need is analyzed and

translated into a set of specific customer requirements. These requirements are assigned priorities (bythe customers) and then correlated with a set of design requirements, represented by Design DependentParameters (DDPs).1 Next, existing and competitive systems and products are benchmarked from acustomers perspective, and technically assessed prior to the assignment of target values to the DDPs. Afuzzy QFD matrix, as shown in Figure 1, is utilized to facilitate these activities.2 Linguistic scales areutilized for the assignment of priorities to customer requirements, the correlation between requirementsand DDPs, and the benchmarking of customer perceptions. One such scale to express correlations (withfive linguistic labels) is shown in Figure 2.3 Finally, two indices, IPN (Improvement Potential andNecessity) and TOF (Tolerance of Fuzziness), are developed to facilitate assignment of target values to

1 Design Dependent Parameters (DDPs) are inherent design characteristics under control of the design team. The DDP concept is explained in

[Fabrycky, 1994].2 The fuzzy QFD method is implemented as a computer-based proof-of-concept tool called FuzzyQFD, development of which was sponsored

by the Virginia Center for Innovative Technology and the Software Productivity Consortium.3 Linguistic scales, along with common fuzzy arithmetic operations, are built into the FuzzyQFD tool to facilitate computation of fuzzy

absolute and relative DDP priorities, and the computation of IPN, TOF, and Feasibility Indices discussed in [Verma and Fabrycky, 1995].

2DDPs. The mechanisms of the Fuzzy QFD methodology and tool are discussed in more detail in [Vermaand Fabrycky, 1995; and Verma, 1994]. The QFD method is subjective and dependent on the synthesisof the voice of the customer. Consistency and traceability must be maintained while translating customerspecified requirements into design features and design parameter target values.

Figure 1. The Fuzzy QFD matrix schematic.

Very Low Medium High Very Low High

Figure 2. Linguistic scale for assigning fuzzy priorities to customer requirements.

The IPN index is computed to give an indication of the potential of a DDP to improve customersatisfaction, along with a necessity for this improvement (based on customer satisfaction levels).Accordingly, IPN is a function of customer satisfaction and correlation between customer requirementsand DDPs. When assigning target values to DDPs, the design team may specify a preferred value alongwith a tolerance band around this value. The TOF index for every DDP, computed as a function of itsIPN index and the importance of correlating customer requirements, suggests the acceptable width of

PreferenceLevel

1.0

0.0

3these tolerance bands. A higher value of the TOF index suggests little acceptable variation in therequired value of a DDP. In the extreme case, a value of unity suggests a crisp requirement.4

Technical assessments of competing systems and products, together with associated values of theIPN and TOF indices, provide valuable insight to the design team when assigning target values to theDDPs. Target values may be specified as normal and convex fuzzy profiles depicting the most preferredvalue(s), together with a tolerance band and varying levels of preference within the band. For example,the Reliability requirement may be articulated as Greater Than 40,000 Hours MTBF. This is showngraphically in Figure 3. Target values, which may also be defined as crisp requirements with noacceptable deviation from the preferred level, specify the feasible system design space.

Figure 3. Design concept/criterion feasibility assessment.

An extension of this approach along with the concept of a fuzzy feasible system design space and amodification to Pughs concept selection matrix is discussed in [Verma, 1994; and Verma and Knezevic,1996]. The research presented and discussed in this paper pertains to the insight that a design team canglean from a QFD matrix while assigning target values to relevant design parameters [Chilakapati, 1995].This assignment represents a significant commitment on part of the design team, and an important step inthe system design process, in that it defines the feasible design and technology space. Accordingly, itmust be conducted to ensure a competitive posture in the commercial marketplace.

ANALYZING THE QUALITY FUNCTION DEPLOYMENT (QFD) MATRIXThe large set of systems engineering/integrated product development activities may be generally

classified as having a synthesis, analysis, or evaluation orientation. Within this context, QFD is anexcellent design analysis and synthesis mechanism. It provides a framework for analyzing a functionalneed or deficiency leading to the synthesis of customer-focused system requirements. This paperpresents an approach to enhance the usability of QFD through the:

1. Identification of potential inconsistencies within a QFD matrix and the implication of theseinconsistencies on system requirements

2. Identification of potential and strategic opportunities implied within a QFD matrix and theNature of these opportunities and their exploitation by a strategic product planning team

3. Representation of above knowledge and other heuristics within an embedded expert system forincreasingly mature responsiveness of the approach and its tailoring to a domain/business area.5

4 Details regarding the IPN and TOF indices are given in [Verma and Knezevic, 1996].

Requirement Profile

PredictionProfile

36,000 39,000 42,500 37,000 40,000

Identification of Inconsistencies: The fidelity and focus of system requirements is dependent uponconsistency within the QFD matrix, and the first step towards enhancing it is through the clear andprecise articulation of the functional need or deficiency. While QFD does not remove the burden ofdecision making from the design team, it facilitates the synthesis of a prioritized and feasible designspace. Subsequent to need identification, consistency during the delineation of specific qualitative andquantitative customer (or stakeholder) requirements can be maintained through proper representation ofcustomer/consumer wants. In this context, it is critical that the product planning team has identified allthe stakeholders; not just the end user or consumer. Once the product planning team has populated theQFD matrix, analysis may reveal the inconsistencies discussed below:

Ignored Customer Requirements: Anignored customer requirement is identifiedby an empty row in the QFD matrix, asshown in Figure 4. Appropriate designparameters to address the customerrequirement in question may not havebeen identified. Since customerrequirements drive subsequent design anddevelopment activities, it is important toaddress this inconsistency early in theprocess. Furthermore, while customerrequirement priorities are establishedfrom customer input, relationships between requirements and any precedences or dependencies shouldalso play a role. WHATs which drive othe

Redundant Design Parameters: Aredundant or unnecessary designparameter is indicated by an unfilledcolumn in the QFD matrix, as shown inFigure 5. Upon investigation, it may benecessary to remove this designrequirement. However, it is critical thatdesign requirements be identified by across-functional team to ensurecompleteness while realizing that designparameters are driven by customerrequirements.

Weak Correlation for Significant Custunambiguous as the first two cases. Accordexperience play a role in the delineation oalong with others, in the expert system ithreshold (or tailoring of the tolerance of a d

5 The Fuzzy QFD software program has been extended

programmer of the Fuzzy QFD software program and the

Figure 4. Example of an ignored customer requirement.r customer requirements should be ranked highest.4

omer Requirements: Few additional inconsistencies are asingly, judgement of the product planning team and historicalf inconsistencies. The representation of this inconsistency,mplementation allows for an adjustment of the minimumesign team).

with an expert system based Analyzer module. Chilakapati was the principal Analyzer extension.

Figure 5. Example of a redundant design parameter.

5As shown in Figure 6, an importantcustomer requirement, may (at best) haveweak correlation with a single designparameter. This may require areassessment of the implementationapproach or the fundamental technologysolution to satisfy the functional need ordeficiency. Design parameters whichsufficiently respond to customerrequirements and associated prioritiesmust be identified.

Percentage Fill of Matrix: While notan inconsistency, an over-populated QFDmatrix, as shown in Figure 7, may inhibitmeaningful translation of customerrequirements into focused designrequirements. An over-populated QFDmatrix may imply that customerrequirements are too broadly defined; andneed to be further refined.

Conflicting Customer and TechnicalBenchmarking: In Figure 8, consider thecustomer requirement Like face-to-face,with high correlation with parameters4 and 5. Customer benchmarkingindicates System 1 is excellent whileSystems 2 and 3 rate as satisfied by thecustomers. However, the technicalassessment for parameters 4 and 5indicates that System 1 ranks lowest for4 and all systems are at par for 5. Thiscontradiction may suggest a dichotomybetween customer articulation and theproduct teams understanding ofrequirements. Accordingly, inconsistencyin the correlation between customer anddesign requirements is implied and mustbe sufficiently addressed.

Difference in Perceived Importanceand Satisfaction: A difference inperceived customer importance andassociated satisfaction is indicated if acustomer is extremely dissatisfied with a

Figure 6. Example of an important customer requirement withweak correlation.

Figure 7. Over-populated QFD matrix.

Figure 8. Contradiction between customer and technicalbenchmarking.

Figure 9. Difference in perceived importance and satisfaction.

6requirement or product feature/functionality for which the priority wasarticulated as being very low. Anexample is shown in Figure 9.

Identification of StrategicOpportunities: During conceptualdesign, parameter target setting andconcept selection provide a significantopportunity to exploit strategic marketopportunities for greater economic gain.

Analysis of information synthesized within a QFD matrix can provide insight to identify theseopportunities. This is particularly true if the product planning team approaches customer requirementsfrom a functional point of view, rather than focusing on improving any particular implementation of aproduct feature or functionality. An example is depicted in Figure 10, where the customer has expressedsevere dissatisfaction with regard to an important requirement. Apart from implying that there are majorgains to be exploited by any of the competitors in this area, this could also indicate a lack of technologyin the present field to make the necessary improvement, and becomes a relevant research focus area.

Identification and Isolation of Critical System/Product Features: Whether architecting a newsystem design or improving an existing system, most organizations have to prioritize the design andimprovement initiatives in a resource constrained environment. In an attempt to facilitate this process,and concurrently keeping the customer an integral part of the prioritization, an IPN (Improvementpotential and need) index is computed within the Fuzzy QFD methodology and tool. The IPN index iscomputed to give an indication of the potential of a design parameter or product feature to improvecustomer satisfaction, along with the necessity for this improvement. Accordingly, IPN is a function ofcustomer satisfaction levels and correlation between customer requirements and design parameters. Thesignificance of the IPN index lies in its focus on a short set of critical design parameters and productfeatures in an environment when a multitude of customer requirements and product features are in theprocess of being prioritized. This is depicted in Figure 11 where parameters 1, 2, 11, 12, and 13 have anIPN index value of Very High. Accordingly, these parameters and product features represent highreturn and strategic improvement areas within the design domain.

AN EXPERT SYSTEM-BASED ANALYZER AND ADVISORWhile inconsistencies and opportunities can be analyzed manually, the process is iterative and can be

time consuming for matrices of modest complexity. The relevant logic and reasoning has beenrepresented in an expert system based analyzer. Furthermore, the expert system architecture allows itsextension to incorporate heuristics within any particular domain (for example, the design anddevelopment of building automation and control systems).

Expert System Structure: The expert system advisor was developed within an object orientedknowledge-based system using forward chaining as the mode of analysis. The object oriented datarepresentation feature of knowledge-based systems allows relevant Quality Function Deployment data to

Figure 10. Identification of strategic product opportunities.

Figure 11. Isolation of Critical Product Fea

be effectively represented, without the loss ofrelationship data. Customer Requirements (CRs) andDesign Dependent Parameters (DDPs) as representedas classes of objects, as shown in Figure 12. Both, therequirements and parameter classes have slot valueswhich are inherited by instances of each class. Theproperties of each customer requirement are shown inTable 1, while Table 2 shows the properties of eachdesign dependent parameter.

Table 1. Slot values of cla

Slots for class CRs AName Tex

Customer ImportanceCorrelation to DDP 1 through n Number from 1-

Benchmarking Numbers from 1

Table 2. Slot values of cla

Slots for class DDPs AName Tex

Customer Requirement List List of corrTechnical Assessments Numbers, i

Fig7

tures and Design Parameters

ss CRs.

llowed Value(s)t with descriptive nameNumber from 1-5

5, indicating correlation with DDPs-5, indicating customer satisfaction

ss DDPs.

llowed Value(s)t with descriptive nameelated Customer Requirementsndicating technical assessment

ure 12. Class definitions in the knowledge-based system.

8Along with the classes and objects, specific rules and functions have been created to allowinferences across all instances of a class. While rules are unique to expert systems and have powerfulbuilt-in pattern matching capabilities, functions are common even in traditional computer programs andare more data-processing in nature. Data within the QFD matrix is subjected to sets of analyses (onefor each inconsistency and/or strategic opportunity, or heuristic) to obtain the final report, or output. Aset can be a function, a rule, or a combination of these. For example, when isolating redundant orunnecessary design parameters, the relevant function parses all DDP slot values of each instance of theclass CRs, and if a particular instance is found to have an empty Customer Requirement List slot,then that slot is added to list of redundant DDPs.

The expert system based analyzer has a graphical user interface which allows the team to tailorsensitivity (or tolerance) of the analyses. For example, when analyzing the QFD matrix for strategicopportunities, the expert system parses and flags customer requirements for which the satisfaction levelslie below a user-specified threshold. Table 3 shows the editable thresholds and their relation to some ofthe analysis sets.

Table 3. Threshold levels.

Test Name ThresholdsSet 3 Weak Correlation Test Correlation Threshold

CR Importance ThresholdSet 4 Percentage Fill Test Overfill Threshold

Underfill ThresholdSet 5 Customer Requirement Importance-

Benchmarking Inconsistency TestBenchmarking ThresholdCR Importance Threshold

Set 6 Benchmark-Technical AssessmentInconsistency Test

Inconsistency Threshold

Set 7 Potential Strategic Opportunities Test Benchmarking Threshold

Implementation of the Expert System-Based Analyzer and Advisor: KAPPA-PC, a commercialexpert system modeling tool, was selected as the implementation environment. It has all the necessaryfeatures of knowledge-based systems and runs in a Microsoft Windows environment. This softwarepackage has the added advantage of facilitating easy creation of graphical user interfaces. Fuzzy QFD,like KAPPA-PC, runs in a Microsoft Windows environment. Accordingly, communication between thetwo packages was relatively simple using the Dynamic Data Exchange (DDE) protocol. The data beingtransferred between the Fuzzy QFD software package and the KAPPA-PC based advisor consists of thede-fuzzified (discrete) Quality Function Deployment matrix data from Fuzzy QFD. Results of theanalysis from the expert system are transferred back to Fuzzy QFD.

POSSIBLE EXTENSIONS TO THIS WORKA very practical extension to the work presented herein relates to the extension of the logic and

reasoning embedded within the expert system with technology, product domain, and product-marketspecific heuristics. This could consist of multi-layered reasoning to take the analysis to a higher level ofcompetency. It is feasible to believe that rules can be formulated that act on conclusions reached byexisting rules. This method allows the expert system advisor to make secondary deductions. Thearchitecture of this expert system is ideally suited for this purpose. To drive the voice of the customer

9throughout the process, a series of matrices are usually utilized. Transforming the hows and associatedtarget values from one level into whats at the next level down is facilitated. However, to keep the multi-tiered process manageable, the analysis is often limited to the most important parameters. Also, theremay be scope for consistency and requirements traceability checking across multiple houses, as well asinside each of them.

The Fuzzy QFD software employs fuzzy logic to represent various facets of the Quality FunctionDeployment method. During the transfer of data from Fuzzy QFD to the expert system advisor, the datais de-fuzzified into discrete numbers. This entails loss of fuzzy representation and disregard for aspectsof imprecision and subjectivity. Accordingly, this work can be extended by using an adaptive fuzzyexpert system.

REFERENCES

Fabrycky, W. J., Modeling and Indirect Experimentation in System Design Evaluation, SystemsEngineering, Journal of the International Council on Systems Engineering (INCOSE), Vol. 1, No. 1,July - September, 1994, Pages 133 - 144.

Pugh, S., Total Design: Integrated Methods for Successful Product Engineering, Addison-Wesley,Inc., New York, 1991.

Verma, D., A Fuzzy Set Paradigm for Conceptual System Design Evaluation, Doctoral Dissertation,Virginia Tech, Blacksburg, Virginia, U.S.A., December 1994.

Verma, D. and W. J. Fabrycky, Development of a Fuzzy Requirements Matrix to Support ConceptualSystem Design, Proceedings, International Conference on Engineering Design (ICED), Praha,August 22-24, 1995.

Chilakapati, R., An Expert System Based Advisor for the Quality Function Deployment Method, MastersThesis, Virginia Tech, Blacksburg, Virginia, U.S.A., March 1995.

Verma, D. and J. Knezevic, Development of a Fuzzy Weighted Mechanism for Feasibility Assessment ofSystem Reliability During Conceptual Design, International Journal of Fuzzy Sets and Systems,Vol. 83, No. 2, October 1996.

ABSTRACTINTRODUCTION AND BACKGROUNDANALYZING THE QUALITY FUNCTION DEPLOYMENT (QFD) MATRIXIdentification of Strategic Opportunities: During conceptual design, parameter target setting and concept selection provide a significant opportunity to exploit strategic market opportunities for greater economic gain.Analysis of information synthesized within a QFD matrix can provide insight to identify these opportunities. This is particularly true if the product planning team approaches customer requirements from a functional point of view, rather than focusing

AN EXPERT SYSTEM-BASED ANALYZER AND ADVISORFigure 11. Isolation of Critical Product Features and Design Parametersbe effectively represented, without the loss of relationship data. Customer Requirements (CRs) andDesign Dependent Parameters (DDPs) as represented as classes of objects, as shown in Figure 12. Both, the requirements and parameter classes have slot values which are inherited by instances of each class. The properties of each customer requirement arePOSSIBLE EXTENSIONS TO THIS WORKA very practical extension to the work presented herein relates to the extension of the logic and reasoning embedded within the expert system with technology, product domain, and product-market specific heuristics. This could consist of multi-layered re