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Poster Paper Proc. of Int. Colloquiums on Computer Electronics Electrical Mechanical and Civil 2011

A Novel Model for QFD of Categorical Data Using Target Based HOQ Prathiba R1, Krishna S2, Bharathi M1, K B Raja2 1

Department of Computer Science and Engineering, SJCIT, Chickballapur, Karnataka, India prathu_manju@yahoo.com 2 Department of Electronics and Communication Engineering, UVCE, Bangalore, Karnataka, India kisna.philly@gmail.com Problem statement: Two main problems are often encountered while building a HOQ viz., (i) The lack of an intelligent tool to determine relationship (correlation) matrix between customer needs and engineering characteristics in order to set technical targets that need to be realized using QFD. Existing methods work efficiently and reliably on data with fewer variables, preferably numerical data, and (ii) The relative importance rating assigned to each of the customer needs. To find relative importance in customer needs several works have been proposed but computation in such methods is generally complex and relatively difficult to implement and realize. In several frameworks that model QFD, the relative importance rating and the relationships between the customers’ needs are usually assigned manually based on heuristics and logic specifically in case of nominal or categorical data. Design Strategy: In this paper NQTHOQ model is proposed. Intelligent data interpretation model is applied on a given data to extract required attributes and identify targets. Association rules and variable importance are used to determine parameters needed to build THOQ to compute importance rank (technical targets). Organization: Section II gives Literature survey, Section III describes the proposed model and implementation on Airport survey data and finally Section IV concludes paper.

Abstract—Quality Function Deployment (QFD) is an effective methodology for analysis of customer requirements in order to improve the performance of a service. In this paper A Novel Model for QFD of Categorical Data using Target based House Of Quality (NQTHOQ) model is proposed. The categorical data is considered for QFD analysis. Given model minimizes the analyst’s opinion to compute some of the parameters that constitute HOQ of QFD. The association rules are used for realizing relationship matrix between customer requirements and engineering characteristics. The chi-squared attribute evaluator is used to determine variable importance of each customer requirements based on target. The novel concept of THOQ is built using association rules and variable importance to compute importance rank of HOQ which constitute QFD. Index Terms—QFD, VOC, HOQ, Chi-squared attribute evaluator

I. INTRODUCTION QFD is an efficient tool for understanding the customer perspective and to transform it to the capabilities of an organization and helps to determine opportunities that can be developed effectively to achieve total customer satisfaction [1]. QFD helps the product development team to take effective and reliable decisions to satisfy customer needs depending on the available and deplorable resources. Quality of a product or a particular service affects the customers and thus performance of an organization. Hence customers can judge the performance of the product or service. To know about the quality of its product or service company need to take the opinion of the customers [2]. The data collected by the customers is called Voice Of Customers (VOC) alluding to the opinion of the customer. Customer information, in general is collected or compiled by conducting surveys, from focus groups, interviews, listening to sales people, trade shows and so on [3]. QFD translates customer requirements into engineering characteristics through House Of Quality (HOQ). The needs and relative importance of customers serve as inputs for HOQ to analyze the overall performance of the product under evaluation. HOQ is considered to be strongest and preliminary analysis tool which is used to understand customer requirements and to henceforth establish priorities of the various technical requirements.

II. LITERATURE SURVEY Lianzhang Zhu and Xiaoqing (Frank) Liu [4] used artificial neural network to set technical targets in quality function deployment for SOA web service system using Bayesian regularized artificial neural network technique. Yen He Zhongchun Mi [5] introduced QFD method for engineering project performance evaluation. The multistage model of house of quality is built. This method uses both external and internal customer needs and project performance is being evaluated. Xiaoliang Liu and Xiao Liu [6] proposed a combined method of neural networks and fuzzy quality function deployment and uses trapezoidal fuzzy numbers to identify changes in customer needs to report it to designers. Liang Tsung Lin, et al., [7] proposed QFD using forecasting technique to enhance the competitiveness of organization’s product in market. Time series-based exponential smoothing is proposed to update customer needs, since customer needs change rapidly. This method identifies future customer needs

Poster Paper Proc. of Int. Colloquiums on Computer Electronics Electrical Mechanical and Civil 2011 III. PROPOSED MODEL

Signage: for better customer communication. Parking space: for customer parking facility Maintenance: for cleanliness and management. Circulation: for customers inside airport and to the airport commuting. C. Association Rules for Correlation In the proposed model, we use association rules, a data mining algorithm to realize correlation between large categorical data statistically thus avoiding the opinion of the designer which varies with designers. One of the important algorithms for processing and interpreting large data is the association rule mining algorithm, which discovers correlations between items in databases. Apriori algorithm [9] is used for association rules in the proposed model We employ the lift measure of each of the rules generated to determine the correlation between the several categorical variables involved. Lift is a simple correlation measure that is given as follows, the occurrence of itemset A is independent of occurrence of itemset B if P(AUB) = P(A)P(B) otherwise, itemsets A and B are dependent and correlated as events [10]. This definition can easily be extended to more than two itemsets. The lift between the occurrence of A and B can be measured by computing Equation (1).

Figure 1. Block diagram for proposed model

A. Data Collection model Data collection mainly contributes to the Voice of Customer (VOC) which might include customer suggestions, advices, complaints and similar categorical values usually termed as variables. In order to collect such data, several questionnaires are distributed to customers, conducting surveys or interviews. The preparation of questionnaires often plays an important role as invalid questions could lead to wrong inferences and thus an incorrect insight into the relation between the customer needs and the market segment addressed. In this model San Francisco International Airport (SFO) customer survey data [8] is used for QFD analysis.

If the resulting value of Equation (1) is less than 1, then the occurrence of A is negatively correlated with the occurrence of B. if the resulting value is greater than 1, then A and B are positively correlated. If the resulting value is equal to 1, then A and B are independent and there is no correlation between them.

B. Intelligent Data Interpretation model (IDI) The IDI model is the data analysis model which refers to identifying a target around which the QFD needs to be designed and the corresponding variables that could contribute to the prediction of these targets. IDI is based on the concept of importance that need to be given to a particular set of variables changes with the target chosen. Airport data set has 52 variables including the IDs of the customer and the interviewer. The attributes considered for QFD analysis are Boarding Area, Terminal, Airline, Language, State/Country, Rate Airport, Rate Art, Rate Restaurant, Rate Circulation, Rate Arrival Booth, Rate Departure Booth, Rate Signage, Rate Directional Signs, Rate Parking, Rate Rental Car Centre, Rate Retail, Form Of Transportation, Rate Mode Of Transportation, Purpose Of Travel, Rate Ease Of Finding Way, Rate Security, Airport Cleanliness, Boarding Area Cleanliness, Garage Cleanliness, Boarding Area Cleanliness, Rental Car Centre Cleanliness, Restaurant Cleanliness, Restroom Cleanliness. Among these, we consider the definite information for targets. The six attributes Boarding Area, Terminal, Airline, Language, State/Country, and Purpose of Travel are not subject to opinion of a customer or VOC, hence they are considered as targets. Data Categorization: The customer requirements are defined by grouping the deduced VOC attributes into four categories as communication, parking, cleanliness, and transportation Each of the customer requirements is translated into measurable engineering or technical characteristics, based on customer needs listed. The customer needs and quality characteristics defined for each customer needs given as ÂŠ 2011 ACEEE DOI: 02.CEMC.2011.01.583

1) Functional relationship for HOQ Depending on the value of lift in the rules generated, we consider a certain number of rules such that we would be able to realize maximum number of relations between variables i.e. enough number of rules that are relevant for determining correlation between most variables considered. The available lift values are scaled into four correlations as high, medium, low and uncorrelated usually on a scale of 10 as 9, 3, 1 and 0 respectively. Eg: Rate Arrival Booth=5 ==> Rate Parking Shuttle=5 < Lift:(1.13)>

Here in Example given, rating of the arrival booth belongs to the customer requirement of communication and rating of parking shuttle belongs to that of parking. Thus the correlation between these two needs is medium or 3. Based on all the optimum rules generated, the correlation values assigned in the relationship matrix are given in the Table I

Poster Paper Proc. of Int. Colloquiums on Computer Electronics Electrical Mechanical and Civil 2011 E. Target based House Of Quality House of quality [12] is the strong tool employed for quality function deployment. THOQ is one which is built based on each target. The correlations obtained from association rules are used to determine functional relationship and the relative importance of each customer need obtained from chi-squared attribute evaluator. Using the correlation and variable importance of customer needs THOQ is built for each target.

TABLE I. C ORRELATIONS ASSIGNED BASED ON LIFT VALUES

D. Variable Importance This idea serves as the linchpin for the proposed model defining the concept of THOQ. With a QFD perspective, variable importance refers to the relative importance of all the customer needs with respect to a specific target. TABLE II.

RELATIVE IMPORTANCE RESULTS BASED ON TARGETS

Figure 2. THOQ Target: Boarding Area

Figure 2 shows the HOQ with target as Boarding Area. Maintenance and parking space has been allotted the highest important rank, thus the airport management has done considerably to allot more resources in this area. This can also be logically supported by the fact that since most passengers wait for long at the boarding areas, they always expect a clean environment and the management is doing fine in that area. However, care needs to be taken to improve the signage specifically at the boarding areas.

In most works related to QFD, the variable importance of the customer requirement is assigned manually by the analyst, Moreover the variable importance varies with respect to the aim for which the HOQ is being constructed, i.e., different importance values could be assigned to different attributes with respect to the identified target. Chi-squared attribute evaluation method is employed in this work to determine the relative importance of each variable needed to predict the target [11]. The idea behind this design is that, a questionnaire would be valid only if it could predict its customer or the service provided to him only based on his opinions. The targets are Boarding Area, Terminal, Airlines and Language. This is an attempt to predict the given target based on the opinion of the passenger that is modeling the passenger and also providing a quantitative means to realize the validity of the questionnaire with the specific target. Relative importance is obtained from chi-squared attribute evaluation algorithm based on each target. Table II shows variable importance based on targets: Boarding Area, terminal, airlines and language

ÂŠ 2011 ACEEE DOI: 02.CEMC.2011.01. 583

Figure 3. THOQ Target: Terminal

From Figure 3, we can infer that with perspective of airlines in mind, there is scope of improvement with signages. Figure 4 shows the THOQ with target as Terminal. Maintenance and parking space has been allotted the most important rank for a given terminal, thus the airport management has allotted considerable amount of resources in this area. This can also be logically supported by the fact that since most terminals are always busy with the pre departure routines and hence, passengers sense the level of maintenance need to be improved, the management has taken a good care. 91

Poster Paper Proc. of Int. Colloquiums on Computer Electronics Electrical Mechanical and Civil 2011 The chi-squared attribute evaluator is used to determine variable importance of each customer requirement based on target. The THOQ is built using association rules and variable importance to compute importance rank. The concept of Target based House of Quality (THOQ) is proposed which gives different HOQs and hence different inferences regarding engineering characteristics that need be changed with the target’s perspective. REFERENCES [1] Akao Y, “Quality Function Deployment: Integrating Customer Requirements into Product Design,” Translated by Glenn Mazur Cambridge.MA: Productivity Press, pp. 1–15, 1990 [2] K J Kim, H Moskowitz, A Dhingra, and G Evans, “Fuzzy Multicriteria Models for Quality Function Deployment,” European journal of operational research, Vol. 121, No. 3, pp. 504-518, 2000. [3] Karen Becker, “Customers Loyality: Are You Hearing Voices,” Quality Progress, pp. 28-35, February 2005. www.asq.org. [4] Lianzhang Zhu and Xiaoqing (Frank) Liu, “Technical Target Setting in QFD for Web Service Systems Using an Artificial Neural Network,” IEEE Transactions on Services Computing, Vol. 3, No. 4, pp. 338-352, December 2010. [5] Yen He and Zhongchun Mi, “QFD Method and its Application in Engineering Project Performance Evaluation,” International Conference on Electronic Commerce and Business Intelligence, pp. 184-187, 2009. [6] Xiaoliang Liu and Xiao Liu, “A Neural Network Approach and Fuzzy Theory for A New Product Development,” IEEE, International Symposium on Information Engineering and Electronic Commerce, pp. 1-4, July 2010. [7] Liang-Tsung Lin, Ching-pou Chang and Kuen-Ho Chiang, “Using Forecasting Technique in Quality Function Deployment to Facilitate Dynamic Customer Needs,” Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1455-1458, December 2010 [8] Christopher Birch “DataSF Data Dictionary for 2009 SFO Customer Survey Report” can be downloaded from DataSF. December 15, 2010. www.flysfo.com [9] Srikant, R and Agrawal R, “Mining Generalized Association Rules,” Future Generation Computer Systems, Vol.13, pp.2-3, 1997. [10] Jiawei Han and Michelin Kamber, “Data Mining: Concepts and Techniques,” Second Edition, Morgan Kaufmann publishers, An imprint of Elsevier, 2006. [11] Huan Liu and Rudy Setiono, “Chi2: Feature Selection and Descretization of Numeric Attributes,” IEEE Conference on Tools with Artificial Intelligence, pp. 388-391, August 2002. [12] Philip Mayfield, “Introduction to Quality Function Deployment,”www.SigmaZone.com

Figure 4. THOQ Target: Airlines

Similarly from Figure 5, we can infer that with target as Language, there is a scope of improvement with signage and circulation.

Figure 5. THOQ Target: Language.

In all these inferences parking space has been allotted highest importance rank this may be for two reasons (i) Management has allotted enough resources. (ii) People are not concerned about the parking space usually rated as high or the questionnaire is not relevant. Note that these reasons support inferences in a logical way; there might other reasons too for this analysis. CONCLUSIONS The proposed NQTHOQ model works most effective on categorical data. The association rules used in model acts as an efficient tool for realising the relationship matrix required for HOQ.