quality function deployment (qfd) - CiteSeerX

Analyzing a Quality Function Deployment (QFD) Matrix: An Expert System Based Approach to Identify Inconsistencies and Opportunities Dinesh Verma, Ph.D.

Rajesh Chilakapati

Wolter J. Fabrycky, Ph.D.

Systems/Supportability Strategist Lockheed Martin Federal Systems, Inc. 9500 Godwin Drive Manassas, Virginia 20110 ([email protected])

Principal Consultant Price Waterhouse LLP 1399 Concord Point Lane Reston, Virginia 20194 ([email protected])

Lawrence Professor Emeritus and Senior Research Scientist ISE, Virginia Tech Blacksburg, Virginia 24061 ([email protected])

ABSTRACT Identification 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 and quantitative customer and design requirements. Design methods such as Quality Function Deployment (QFD), Parameter Taxonomies, and Input/Output Matrices (IOM) provide a useful framework for this translation. Well defined and unambiguous requirements enhance communications and can potentially reduce the number of “detours” during subsequent design and development phases. However, imprecision and vagueness characterize the conceptual design phase. To accommodate imprecision, the QFD method and the concept selection methodology, initially proposed by Pugh [91], have been modified and extended by applying concepts from fuzzy set theory [Verma and Fabrycky, 1995; and Verma and Knezevic, 1996]. The extended approach provides a rigorous yet graceful mechanism for dealing 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 of applied research focus areas, and c) isolation of inconsistencies between customer articulation of functional requirements and the definition of system requirements and parameter target values. An expert system based parser has been embedded within the Fuzzy QFD tool to facilitate strategic product planning, early design decision making, and parameter target setting. INTRODUCTION AND BACKGROUND A 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 (by the customers) and then correlated with a set of design requirements, represented by Design Dependent Parameters (DDPs).1 Next, existing and competitive systems and products are benchmarked from a customer’s perspective, and technically assessed prior to the assignment of target values to the DDPs. A fuzzy QFD matrix, as shown in Figure 1, is utilized to facilitate these activities.2 Linguistic scales are utilized for the assignment of priorities to customer requirements, the correlation between requirements and DDPs, and the benchmarking of customer perceptions. One such scale to express correlations (with five linguistic labels) is shown in Figure 2.3 Finally, two indices, IPN (Improvement Potential and Necessity) and TOF (Tolerance of Fuzziness), are developed to facilitate assignment of target values to 1

2

3

Design Dependent Parameters (DDPs) are inherent design characteristics under control of the design team. The DDP concept is explained in [Fabrycky, 1994]. 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. 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].

1

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

Figure 1. The Fuzzy QFD matrix schematic. 1.0 Very Preference Low Level

Low

Medium

High

Very High

0.0

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

2

these tolerance bands. A higher value of the TOF index suggests little acceptable variation in the required 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 the IPN and TOF indices, provide valuable insight to the design team when assigning target values to the DDPs. Target values may be specified as normal and convex fuzzy profiles depicting the most preferred value(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 shown graphically in Figure 3. Target values, which may also be defined as “crisp” requirements with no acceptable deviation from the preferred level, specify the feasible system design space.

Prediction Profile Requirement Profile

36,000

39,000 37,000

42,500 40,000

Figure 3. Design concept/criterion feasibility assessment.

An extension of this approach along with the concept of a fuzzy feasible system design space and a modification to Pugh’s 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 can glean 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 in the system design process, in that it defines the feasible design and technology space. Accordingly, it must be conducted to ensure a competitive posture in the commercial marketplace. ANALYZING THE QUALITY FUNCTION DEPLOYMENT (QFD) MATRIX The 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 an excellent design analysis and synthesis mechanism. It provides a framework for analyzing a functional need or deficiency leading to the synthesis of customer-focused system requirements. This paper presents an approach to enhance the usability of QFD through the: 1. Identification of potential inconsistencies within a QFD matrix and the implication of these inconsistencies on system requirements 2. Identification of potential and strategic opportunities implied within a QFD matrix and the Nature 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 for increasingly 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].

3

Identification of Inconsistencies: The fidelity and focus of system requirements is dependent upon consistency within the QFD matrix, and the first step towards enhancing it is through the clear and precise articulation of the functional need or deficiency. While QFD does not remove the burden of decision making from the design team, it facilitates the synthesis of a prioritized and feasible design space. Subsequent to need identification, consistency during the delineation of specific qualitative and quantitative customer (or stakeholder) requirements can be maintained through proper representation of customer/consumer wants. In this context, it is critical that the product planning team has identified all the stakeholders; not just the end user or consumer. Once the product planning team has populated the QFD matrix, analysis may reveal the inconsistencies discussed below: Ignored Customer Requirements: An ignored customer requirement is identified by an empty row in the QFD matrix, as shown in Figure 4. Appropriate design parameters to address the customer requirement in question may not have been identified. Since customer requirements drive subsequent design and development activities, it is important to address this inconsistency early in the process. Furthermore, while customer Figure 4. Example of an ignored customer requirement. requirement priorities are established from customer input, relationships between requirements and any precedences or dependencies should also play a role. ‘WHAT’s which drive other customer requirements should be ranked highest. Redundant Design Parameters: A redundant or unnecessary design parameter is indicated by an unfilled column in the QFD matrix, as shown in Figure 5. Upon investigation, it may be necessary to remove this design requirement. However, it is critical that design requirements be identified by a cross-functional team to ensure completeness while realizing that design parameters are driven by customer requirements.

Figure 5. Example of a redundant design parameter.

Weak Correlation for Significant Customer Requirements: Few additional inconsistencies are as unambiguous as the first two cases. Accordingly, judgement of the product planning team and historical experience play a role in the delineation of inconsistencies. The representation of this inconsistency, along with others, in the expert system implementation allows for an adjustment of the minimum threshold (or tailoring of the tolerance of a design team). 5

The Fuzzy QFD software program has been extended with an expert system based Analyzer module. Chilakapati was the principal programmer of the Fuzzy QFD software program and the Analyzer extension.

4

As shown in Figure 6, an important customer requirement, may (at best) have weak correlation with a single design parameter. This may require a reassessment of the implementation approach or the fundamental technology solution to satisfy the functional need or deficiency. Design parameters which sufficiently respond to customer requirements and associated priorities Figure 6. Example of an important customer requirement with weak correlation. must be identified. Percentage Fill of Matrix: While not an inconsistency, an over-populated QFD matrix, as shown in Figure 7, may inhibit meaningful translation of customer requirements into focused design requirements. An over-populated QFD matrix may imply that customer requirements are too broadly defined; and need to be further refined. Conflicting Customer and Technical Benchmarking: In Figure 8, consider the customer requirement ‘Like face-to-face’, with ‘high’ correlation with parameters ‘4’ and ‘5’. Customer benchmarking indicates System 1 is ‘excellent’ while Systems 2 and 3 rate as ‘satisfied’ by the customers. However, the technical assessment for parameters ‘4’ and ‘5’ indicates that System 1 ranks lowest for ‘4’ and all systems are at par for ‘5’. This contradiction may suggest a dichotomy between customer articulation and the product team’s understanding of requirements. Accordingly, inconsistency in the correlation between customer and design requirements is implied and must be sufficiently addressed. Difference in Perceived Importance and Satisfaction: A difference in perceived customer importance and associated satisfaction is indicated if a customer is extremely dissatisfied with a

Figure 7. Over-populated QFD matrix.

Figure 8. Contradiction between customer and technical benchmarking.

Figure 9. Difference in perceived importance and satisfaction.

5

requirement or product feature/ functionality for which the priority was articulated as being very low. An example is shown in Figure 9. Identification of Strategic Opportunities: During conceptual design, parameter target setting and concept selection provide a significant opportunity to exploit strategic market Figure 10. Identification of strategic product opportunities. 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 on improving any particular implementation of a product feature or functionality. An example is depicted in Figure 10, where the customer has expressed severe dissatisfaction with regard to an important requirement. Apart from implying that there are major gains to be exploited by any of the competitors in this area, this could also indicate a lack of technology in 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 new system design or improving an existing system, most organizations have to prioritize the design and improvement 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 (Improvement potential and need) index is computed within the Fuzzy QFD methodology and tool. The IPN index is computed to give an indication of the potential of a design parameter or product feature to improve customer satisfaction, along with the necessity for this improvement. Accordingly, IPN is a function of customer satisfaction levels and correlation between customer requirements and design parameters. The significance of the IPN index lies in its focus on a short set of critical design parameters and product features in an environment when a multitude of customer requirements and product features are in the process of being prioritized. This is depicted in Figure 11 where parameters 1, 2, 11, 12, and 13 have an IPN index value of Very High. Accordingly, these parameters and product features represent “high return” and strategic improvement areas within the design domain. AN EXPERT SYSTEM-BASED ANALYZER AND ADVISOR While 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 been represented in an expert system based analyzer. Furthermore, the expert system architecture allows its extension to incorporate heuristics within any particular domain (for example, the design and development of building automation and control systems). Expert System Structure: The expert system advisor was developed within an object oriented knowledge-based system using forward chaining as the mode of analysis. The object oriented data representation feature of knowledge-based systems allows relevant Quality Function Deployment data to

6

Figure 11. Isolation of Critical Product Features and Design Parameters be effectively represented, without the loss of relationship data. Customer Requirements (CRs) and Design 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 are shown in Table 1, while Table 2 shows the properties of each design dependent parameter.

Figure 12. Class definitions in the knowledgebased system.

Table 1. Slot values of class “CRs”. Slots for class “CRs”

Allowed Value(s)

Name Customer Importance Correlation to DDP 1 through n Benchmarking

Text with descriptive name Number from 1-5 Number from 1-5, indicating correlation with DDPs Numbers from 1-5, indicating customer satisfaction

Table 2. Slot values of class “DDPs”. Slots for class “DDPs”

Allowed Value(s)

Name Customer Requirement List Technical Assessments

Text with descriptive name List of correlated Customer Requirements Numbers, indicating technical assessment

7

Along with the classes and objects, specific “rules” and “functions” have been created to allow inferences across all instances of a class. While “rules” are unique to expert systems and have powerful built-in pattern matching capabilities, “functions” are common even in traditional computer programs and are more “data-processing” in nature. Data within the QFD matrix is subjected to “sets” of analyses (one for each inconsistency and/or strategic opportunity, or heuristic) to obtain the final report, or output. A “set “ can be a function, a rule, or a combination of these. For example, when isolating redundant or unnecessary design parameters, the relevant function parses all “DDP” slot values of each instance of the class “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 tailor sensitivity (or tolerance) of the analyses. For example, when analyzing the QFD matrix for strategic opportunities, the expert system parses and flags customer requirements for which the satisfaction levels lie below a user-specified threshold. Table 3 shows the editable thresholds and their relation to some of the analysis sets. Table 3. Threshold levels. Test

Name

Thresholds

Set 3

Weak Correlation Test

Set 4

Percentage Fill Test

Set 5

Customer Requirement ImportanceBenchmarking Inconsistency Test Benchmark-Technical Assessment Inconsistency Test Potential Strategic Opportunities Test

Correlation Threshold CR Importance Threshold Overfill Threshold Underfill Threshold Benchmarking Threshold CR Importance Threshold Inconsistency Threshold

Set 6 Set 7

Benchmarking Threshold

Implementation of the Expert System-Based Analyzer and Advisor: KAPPA-PC, a commercial expert system modeling tool, was selected as the implementation environment. It has all the necessary features of knowledge-based systems and runs in a Microsoft Windows environment. This software package 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 the two packages was relatively simple using the Dynamic Data Exchange (DDE) protocol. The data being transferred between the Fuzzy QFD software package and the KAPPA-PC based advisor consists of the de-fuzzified (discrete) Quality Function Deployment matrix data from Fuzzy QFD. Results of the analysis from the expert system are transferred back to Fuzzy QFD. POSSIBLE EXTENSIONS TO THIS WORK A 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 reasoning to take the analysis to a higher level of competency. It is feasible to believe that rules can be formulated that act on conclusions reached by existing rules. This method allows the expert system advisor to make secondary deductions. The architecture of this expert system is ideally suited for this purpose. To drive the voice of the customer

8

throughout the process, a series of matrices are usually utilized. Transforming the hows and associated target values from one level into whats at the next level down is facilitated. However, to keep the multitiered process manageable, the analysis is often limited to the most important parameters. Also, there may be scope for consistency and requirements traceability checking across multiple houses, as well as inside each of them. The Fuzzy QFD software employs fuzzy logic to represent various facets of the Quality Function Deployment method. During the transfer of data from Fuzzy QFD to the expert system advisor, the data is de-fuzzified into discrete numbers. This entails loss of fuzzy representation and disregard for aspects of imprecision and subjectivity. Accordingly, this work can be extended by using an adaptive fuzzy expert system. REFERENCES Fabrycky, W. J., Modeling and Indirect Experimentation in System Design Evaluation, Systems Engineering, 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 Conceptual System 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, Masters Thesis, Virginia Tech, Blacksburg, Virginia, U.S.A., March 1995. Verma, D. and J. Knezevic, Development of a Fuzzy Weighted Mechanism for Feasibility Assessment of System Reliability During Conceptual Design, International Journal of Fuzzy Sets and Systems, Vol. 83, No. 2, October 1996.

9