A NEW QFD-BASED QUANTITATIVE MODEL FOR ADAPTABILITY EVALUATION OF ENTERPRISE INFORMATION SYSTEMS

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1 January 2. Vol. 47 No. 2-2 JAI & LLS. All rights reserved. ISSN: E-ISSN: 87-9 A NEW QFD-BASED QUANIAIVE MODEL FOR ADAPABILI EVALUAION OF ENERPRISE INFORMAION SSEMS HAIWANG CAO, 2 LIUANG WU, 2 CHAOGAI XUE Asstt Prof., Department of Electronic and Communication Engineering, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou, China 2 Department of Management Engineering, Zhengzhou University, Zhengzhou, China bdchw@6.com, 2 myrayy@zzu.edu.cn ABSRAC his paper deals wi a new quantitative model for adaptability evaluation of enterprise information systems (EIS). Firstly, a new evaluation model based on QFD is proposed, and e corresponding evaluation algorim is given. Secondly, e procedures of EIS adaptability evaluation are discussed. Finally, e application of e evaluation model and algorim is verified rough an example, which provides quantitative references for optimizing EIS adaptability. Keywords: Adaptability Evaluation, Quality Functional Deployment (QFD)Enterprise Information Systems. INRODUCION Wi e development of information technology, e maret competition is increasingly intense and e uncertainty of environment has increased. In order to enhance management efficiency and add management function, enterprise information systems (EIS) should have e ability to adapt to e changing environment inside and outside at any time, i.e., enterprise information systems adaptability (EISA). At present, ere is little reference to EISA, mainly including e analysis of EISA s influencing factors, EISA s evaluation, EISA s empirical study and how to enhance EISA. So far, e researches on EISA enhancement mainly focus on software system: () study on selfadaptability of software []-[2]. (2) study on adaptive mechanism of software system []-[4]. As an example of () above, Huang Shuangxi [] etc. studied e generic adaptive software architecture style, and as an example of (2) u Chun [] etc. studied an architecture-oriented mechanism for selfadaptation of software systems. hese researches have laid foundation for e adaptability of EIS, but ere are differences between software systems adaptability and EISA. Firstly, EIS not only include software but also business process; secondly, software systems environment means software s e operation carrier or e development platform, but EIS environment means e maret, policies outside and e operation environment inside. Hence, it is necessary to carry out furer study on e adaptability of EIS. QFD is a product design meod driven by customer requirement, and its essence is to transform customer requirement into design language of engineering staff. At present, researches on e QFD eory focus on e following aspect: () deep research on e quality of house construction []-[7]. (2) combines QFD wi oer eories. () is e application of QFD [8]. As an example of () above, Li anlai [] etc. studied e construction of quality of house, and built - step model to avoid e inconsistency. As an example of (2) above, Xiong Wei [7] etc. studied e combination of quality eory wi e QFD, and represented e relationship between product quality properties and requirement. In terms of (), Gu ingui [8] etc. studied e application of QFD in product life cycle design. hese studies have enriched e QFD eory, and expanded e application of QFD. However, it is mainly about product design, and ere is little reference to e quality property of EIS. hus, is paper studies on e adaptability evaluation of EIS based on QFD, and realizes adaptability evaluation by e built evaluation model and e given evaluation algorim, which 272

2 January 2. Vol. 47 No. 2-2 JAI & LLS. All rights reserved. ISSN: E-ISSN: 87-9 helps enterprises to mae better decision on how to analyze customer requirements and design better plan. o is end, e evaluation model based on e QFD is proposed firstly, and evaluation algorim is given as follows. 2. QFD-BASED EVALUAION MODEL AND ALGORIHM 2. Evaluation model A new evaluation model for EISA is built based on QFD, as shown in Figure Evaluation algorim An algorim is proposed to evaluate EIS adaptability based on e evaluation model above. he specific steps are as follows: Algorim : EIS adaptability evaluation algorim Step: According to questionnaire, investigation and survey, customer requirements are analyzed and e customer requirement set { } U u, u2,, um is obtained, as well as e B: interrelationship between adaptive C adaptive A: customer demands D: relation matrix between customer demands and adaptive F: e satisfactory index of adaptability E: e importance and value of adaptive he evaluation model of EISA is composed as follows. () A: It is posed by e customer demands, which can be obtained by interview or questionnaire. (2) B: It is posed by e interrelationship between () C: It is posed by e adaptive, which are obtained according to e customer requirements. (4) D: It is posed by e relationship matrix between customer requirements and adaptive. () E: It s posed by e importance and values of (6) F: It s posed by e satisfactory index of adaptability for each plan. Figure Evaluation Model Of EIS Adaptability adaptive character set { ', ',, '} U2 u u2 u m and e adaptive character values. Step2: Compute e importance of adaptive according to certain meods (as can be seen in Algorim 2). Step: Build relationship matrix of adaptive. Assuming at e relative degree is described by e following five levels:,2,,4,, and from to, e importance is increasing. he interrelationship between adaptive is described by ree types: positive relation (), negative relation (-) and no relation (blan). hus, relationship matrix and interrelationship matrix between adaptive are obtained, and en e normalized matrix can be obtained according to e following equation. 27

3 January 2. Vol. 47 No. 2-2 JAI & LLS. All rights reserved. ISSN: E-ISSN: 87-9 Where ih hj h n n R n j h R R i, 2,, m; jh,, 2,, n; ih hj () R : he relationship matrix between customer requirements and adaptive ; j : he interrelationship matrix between Step4: Conduct evaluation according to e following equations. d R x x l i i x m Im ± di i Where x : he ( ) adaptive character; (2) () l x : he adaptive character value of e l project; d i : he distance between e values of adaptive character at present and each plan; R : he normalized relationship matrix; : he satisfactory index of each plan. Algorim 2: e Importance degree of adaptive. Step: Normalize index matrix R. Step2: Construct index weight matrix A rough m meods. Step: Obtain matrix RA. Step4: Compute e maximum eigenvalue λ of ( RA) max RA and maximum eigenvector X. Step: Compute e vector W AX, and obtain e normalized vector W W/ ew, and e (,,,).. ADAPABILI EVALUAION OF EIS Based on e evaluation model and evaluation algorim, e procedures of EIS adaptability evaluation are shown in Figure.2. he specific steps are shown as follows: Step: According to questionnaire and interview, U and U 2 are obtained, as well as e value of adaptive at present and different design plans. Step2: Establish e relation matrix between customer demands and adaptive matrix and obtain e interrelationship between adaptive, en according to Eq. () in Algorim, R is obtained. Step: According to Algorim 2, e importance Obtain adaptive and related values Obtain relationship matrix Compute e importance of adaptive Compute adaptive value Step: Mae optimal decision according to e satisfactory index. Mae final decision Figure 2 he Procedures Of EIS Adaptability Evaluation of adaptive is obtained. Step4: According to Eq.(2) and Eq.(), e satisfactory index of each plan is obtained. 274

4 January 2. Vol. 47 No. 2-2 JAI & LLS. All rights reserved. ISSN: E-ISSN: 87-9 Step: Mae final decision according to e satisfactory indices. he higher e index is, e better e plan is. 4. EXPLE According to e interview and questionnaire, e customer requirement set is U {meeting customer needs rapidly, little cost, little dependence between modules, easy to transform to actual system, not easy failure}, and e customer requirements are transformed to e adaptability set, i.e., U 2 {time, cost, complexity, ris, robust}, and e satisfactory index at present and e plans can be obtained, as shown in Figure.. hen, according to e evaluation model, e adaptability of an enterprise information system is evaluated rough he current adaptive character value is ( ); and e adaptive character values of improved plan and plan 2 are 2 ( 4..8 ), 4..8, and e satisfaction index is X According to Step in algorim, e normalized relationship matrix is shown as follows: R Adaptive he importance custom character of customer er s requirements demand Meeting s.7 needs rapidly time cost complexit y - ris robust Satisfactory index at present 64.8 Satisfactory Satisfactory index in plan index in plan Little cost Little dependence between modulers Easy to transformed to actual system Not easy failure he importance of adaptive he value of adaptive at present he value of adaptive in plan he value of adaptive in plan 2 Algorim. According to algorim 2, e importance of adaptive is W Figure he Result Of An Enterprise Information System hen according to Step4, e customer satisfaction degree to plan and 2 are: j 2 j ( ),

5 January 2. Vol. 47 No. 2-2 JAI & LLS. All rights reserved. ISSN: E-ISSN: 87-9 herefore, e overall satisfaction indices are: 8.846, As can be seen from e final result, e adaptability satisfaction of plan 2 is better an at of plan, i.e., it satisfies customer requirements better. So e enterprise should adopt plan 2 in order to improve e adaptability of its information system.. CONCLUSION he rapid development of information technology means bo opportunities and challenges to enterprises, and an adaptable information system is important to an enterprise. Based on e QFD eory, a new quantitative model for adaptability evaluation of EIS is proposed, which gives more consideration on e relationship between customer requirements and adaptive. his model helps related staff to mae decisions, realizes e transformation between customer requirements and adaptive, and quantitatively represented eir relationships. Combined wi e case study, e evaluation is explained. his study enriches e adaptability evaluation of EIS, and in e meanwhile, expands e application of e QFD eory. his QFD-based meod can solve decision maing problems of plan selection, and can be used in evaluation problems of oer fields. ACKNOWLEDGEMEN Nanjing University(Natural Sciences), vol. 42, no.2, 26, pp [4] Pan Jian, Zhou u, Luo Bin etc., An ontologybased Software Self-adaptation Mechanism, Computer Science, vol.4, no., 27, pp [] Li anlai, ang Jiafu, an Jianming and Xu Jie, Progress of researches on building house of quality, Journal of Mechanical Engineering, vol.4, no., 29, pp [6] John J. Cristiano, Jeffrey K. Lier, and Chelsea C. White, III, Fellow, IEEE, Key Factors in e Successful Application of Quality Function Deployment (QFD), IEEE ransactions on Engineering Management, vol. 8, no., 2. [7] Xiong Wei, Wang Juanli and Wang Xiaotun, he quantitative research on charm quality based on QFD, Science & echnology Progress and Police, vol.27, no.24, 2, pp [8] Gu ingui, Huang Hongzhong and Sun Zhanquan, he application of QFD in life cycle design of product, China Mechanical Engineering, vol.4, no.24, 2, pp his research is supported by e National Natural Science Foundation of China under Grant numbers 766 and REFRENCES: [] Huang Shuangxi, Fan ushun and Zhao u, Research on Generic Adaptive Software Architecture Style, Journal of Software, vol.7, no.6, 26, pp [2] Koar M M, Baelawsi K and Eraear A., Control eory based foundations of selfcontrolling software, IEEE Intelligent Systems, vol.4, no., 999, pp [] u Chun, Ma Qian, Ma Xiaoxing etc., An Architecture-oriented Mechanism for Selfadaptation of Software System, Journal of 276