# Selection of effective management tools to achieve organization excellence by QFD using fuzzy techniques.

INTRODUCTIONThe success of a product or service largely depends on how they meet the customers' requirements and expectations. Consequently, more effort is involved in getting the information necessary for determining what the customer truly wants. Managerial tools are the techniques and technical features that help organizations to manage their own resources. One process-oriented design method constructed to carry out the translation process and make sure that the findings are implemented is quality function deployment (QFD). The QFD is a visual connective process that helps teams focus on the needs of the customers throughout the total development cycle. In order to prioritize tools and criteria QFD model was used. QFD is not only a technical tool, but a management philosophy that can augment management and organization effectiveness. The QFD concept is broken down into the two main activities: Product quality deployment and deployment of the quality function. Product quality deployment translates the "voice of the customer" into product control Characteristics. Whereby, deployment of the quality function activities needed to satisfy that customer required quality is achieved. Deployment of the quality function inquires the company response to the customer voice through an organized team approach.

Logical tools that people can refer to are generally from two-sided or two-choice logic (yes / no, true / false). But the real issues in the world and human thought processes and approaches to problem solving are not two choices. As a rule, two-sided logic is based on classical sets and fuzzy logic is based on fuzzy sets. A fuzzy set is a collection of objects that there isn't any specific and predefined boundary between objects in their membership or non-membership in the set. Membership is the key concept in this definition. Each element in the set is specified with a value that represents the degree of membership of the element in the set. This value is in the range of [0, 1] such that zero and one, respectively, indicate the minimum and maximum membership of the element in the set and the values between these two values indicates the degree of partial membership in the set. There are several fuzzy numbers, but some of them may be more appropriate to analyze structural ambiguity. In this paper we will use triangular fuzzy numbers.

1.1. Goals of the Research:

The purpose of this study is providing the basic approach to choose management tools based on quality function development to achieve improvement and organizational excellence using fuzzy techniques

2. Methodology:

Theoretical model of research is using Quality Function Development (QFD) that the application has been expanded to solve this problem. In other words, the purpose of the current research is to present a quality function deployment (QFD) model with both Crisp and Fuzzy approaches for the linkages between the EFQM criteria and management tools, and use of this model for identifying and prioritizing management tools that are so effective and match with organizational needs for excellence. In the conceptual model of this study an organization has been considered as a customer.

3. Result:

2.1. Information of the members of the population:

As we know, the research methodology is formed based on how the data have been gathered using different instruments. Since the data in this research have been gathered using questionnaire, the way data are placed in the research model, their solution and therefore, the results obtained from solving the model are presented through 18 steps

Step 1: Determining the organization needs to achieve excellenceL

Customer requirements (organization) according to the enabler criteria of EFQM model include:

1--Leadership Criterion

2--Policies and Strategies criterion

3--Employees' criterion

4--Partnerships and resources criterion

5--Processes criterion

As a result, the five basic needs were determined as (WHATs).

Next, the relative importance of each organization needs to achieve excellence is measured.

Step 2: Crisp group hierarchical analysis:

In this step, priority of the mentioned five criteria should be defined for the organization considering the five enabler Criteria of the EFQM organization excellence as organization needs to achieve excellence. In fact, organizations must first prioritize these needs in order to meet their needs, so that later be able to take necessary measures to better meet these needs considering the priorities. So at this stage, due to the existing methods in literature, and based on using the Huang rule, group analytical hierarchy process (GAHP) is used for determining the priority of each of these enabler criteria of EFQM model due to the hierarchical nature of the indicators or criteria. So in order to do group analytical hierarchy process (GAHP), first paired required comparisons matrix should initially be done, by substituting n = 5 that indicates the five enabler Criteria of the EFQM model. Ten paired comparisons should be done to determine the number of paired comparisons in relation (2 (1-n) x n). Therefore, the opinion of the population member was gathered in the section B of the questionnaire, compiling 10 Questions for paired comparisons. Therefore, 20 matrixes were taken from the comments of individuals.

Step 3: Determining the final decision matrix and the crisp weight of the criteria (gm):

In this step, 20 obtained paired comparison matrix are changed into a matrix of decision using 20 paired comparison matrix formed in step (2), and the geometric mean formula in group analytical hierarchy analysis process. (In addition, all members of the population have equal weight and answers of all of them have the same effect on the results). The final matrix obtained from group decision is as table 4.

After determining the final matrix of paired comparisons, the numbers in Table 4 should be normalized and the weighted average is obtained (Table 5) on order to get the relative importance of each enabler criteria of EFQM model in Jondishapour Company.

The relative importance of each enabler criteria of EFQM model for Jondishapour Company is obtained as Table 6:

Table 6: Relative importance of enabler criteria of EFQM model Percentage of crisp crisp prioritization prioritization enabler criteria of EFQM 31.487726 0.314877267 Leadership 11.3872475 0.113872475 Policy and strategy 17.639281 0.17639281 Employees 21.5490404 0.215490404 Partnerships and resources 17.9367044 0.179367044 Processes

Step 4: Group fuzzy analytical hierarchy process:

In this step, group fuzzy AHP method is considered. Accordingly linguistic words are used from the triangular fuzzy numbers as (1, m, n). The triangular fuzzy numbers used in 20 paired comparisons matrix of this step is considered as Table 4-8:

Table 7: Changing linguistic variables to fuzzy and crisp numbers The equivalent fuzzy number Crisp number Linguistic variables (0,1,2) 1 Equally important (2,3,4) 3 Mid-importance (4,5,6) 5 Great importance (6,7,8) 7 Too much importance (8,9,10) 9 The absolute importance

Step 5: Obtaining the final fuzzy paired comparisons matrix:

Now with 20 paired comparison matrix obtained from crisp data, paired comparisons matrices obtained from fuzzy data can be calculated according to Table 7.

Step 6:

Obtaining the Fuzzy relative importance of each criterion ([g.sup.fm]):

In this step, first we calculate the triangular fuzzy numbers (l, m, n) in the above fuzzy matrix as the sum of each row for each criteria in order to get the priority of each criterion in the form of triangular fuzzy numbers obtained from the above fuzzy decision matrix. Thus the triangular fuzzy numbers are calculated for each of the criteria in order to obtain the following matrix:

Table 9: The matrix of triangular fuzzy numbers U M L 2.091242 2.06262 1.83894 Leadership 0.90982 0.63738 0.63528 Policy and strategy 1.1464 1.13918 1.08236 Employees 1.3043 1.47948 1.31446 Partnerships 1.50024 1.28008 1.16388 Processes

Step 7: De-fuzzing the fuzzy relative importance:

Table 10: Matrix of de-fuzzed values % Normalize DE fuzzing=(1+2m+u)/4 31% 0.30764201 2.0138555 Leadership 11% 0.10769236 0.704965 Policy and strategy 17% 0.17212996 1.12678 Employees 21% 0.21301689 1.39443 Partnerships 20% 0.19951878 1.30607 Processes 6.5461005 Total

Step 8:

Using the Shannon entropy method to determine the weights of criteria [e.sub.m]

In this step, the weight of each criteria of EFQM model is obtained as Table 11 using the competitive evaluation matrix obtained in the previous step, and the application of Shannon entropy method.

Step 9. Determining the final crisp weight of criteria (Wm):

In this step, having the weight of criteria ([e.sub.m]) obtained from step (8), and improvement ratio ([u.sub.m]) obtained from step (10), and the crisp relative importance ([g.sub.m]) of each criterion obtained from step (6), the final importance in two crisp modes (using the crisp relative importance), the final weight of the enabler criteria of EFQM model is obtained as Table 4-15 by multiplying the values corresponding to each of the criteria in the above steps:

Table 12: Determining the final weight of each criterion Fi = [u.sub.m] x [e.sub.m] x [g.sub.m] Final Ranking 0.075249026 Leadership 0.027202475 Policy 0.042142323 Employees 0.051488692 Partnerships 0.042859436 Processes 0.238941951 Total

With the values in Table 12, the values of the normalized crisp priority of each criterion are obtained as Table 13.

Thus, the final prioritization of the enabler criteria of EFQM model can be shown as Figure 4 for Jondishapour Company.

Step10. Determining the final fuzzy weight of criteria:

But the fuzzy values using the fuzzy relative importance obtained from step (6) that is in fact the values of the left and right sides of fuzzy numbers (1, m, u) are as Table 14.

Now, using the formulas provided for non-fuzzing triangular fuzzy numbers, which were presented in the previous steps, with the above triangular fuzzy numbers, de-fuzzied and normalized values according to Table 15, the final weight of each criterion is obtained.

So the final fuzzy prioritization of enabler criteria of the EFQM model in the Jondishapour Company can be indicated in Figure 5.

Proving the research hypotheses:

First hypothesis: There isn't a significant difference between the results of the prioritization of affecting criteria on implementation of the EFQM model in Jondishapour Company using fuzzy and crisp approaches.

[H.sub.0]: P=0

[H.sub.1]: P[not equal to]0

There isn't a significant difference between the results of prioritization enabler criteria using fuzzy and crisp approaches.

Step 11. Comparing the results of crisp and fuzzy prioritization of enabler criteria of EFQM model

Having the values of crisp and fuzzy prioritization of enabler criteria of EFQM model that is presented as follows in table 16, we can perform calculations:

Table 16: Comparing the final results obtained from crisp and fuzzy prioritization of criteria Crisp Fuzzy Criteria 31.49 30.76 Leadership 11.38 10./76 Policy 17.64 17.21 Employees 21.55 21.30 Partnerships 17.94 19.95 Processes

Now, we'll calculate the standard deviation (SD) and coefficient of variation (CV) and correlation coefficients for both the results of crisp and fuzzy prioritization of enabler criteria of EFQM.

S = [square root of ([SIGMA](x - [??])/n- 1)] = [square root of 210.63172/4] = 7.2565 CV = S/X = 0.3629015

Calculating the standard deviation (SD) for both the results of crisp and fuzzy prioritization of EFQM enabler criteria showed that both the standard deviation of the mean is almost identical. Standard deviation of the results of crisp prioritization of criteria is equal to 7/3915 and standard deviation of the results of fuzzy prioritization of criteria is equal to 7/2565. Since the rate of change of the crisp values is obtained 37% and 36% for the fuzzy values, indicates that the results of crisp and fuzzy prioritization of enabler criteria is identical or in other words, there isn't a significant difference between the results of the crisp and fuzzy approaches. The correlation coefficient is calculated using the following formula and table 19, which indicates that there is a high correlation (p = 0.9880), between crisp and fuzzy values. And the correlation coefficient indicates that there isn't a statistically significant difference between the results of the prioritization of enabler criteria of EFQM, in the two fuzzy and crisp approaches. Therefore, [H.sub.0] is rejected and hypothesis [H.sub.1] is accepted.

Calculating the correlation coefficient between fuzzy (x) and crisp (y) values of criteria:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

This shows a high correlation coefficient between fuzzy (x) and crisp (y) values. Therefore, there isn't a significant difference between the results of both the approaches.

Step 12: Determining the management tools affecting the implementation of EFQM excellence model:

Organizations as customers, who are in search of excellence, must also provide the necessary technical specifications to achieve excellence. But what are these necessary technical specifications? In answering this question, it must be said that in fact, management tools are those technical techniques and specifications that organizations manage their resources with their help and can use them to achieve excellence in their performance and achieve excellence indicators.

Therefore, in this step, researcher has referred to organizations' excellence handbook that has been written by the Foundation of EFQM, and has determined the main management tools as the following 15-fold classification which is defined as (HOWs) in the QFD matrix.

Step 13: establishing the relationship matrices between the tools and criteria:

In this step, experts' opinion was obtained through questionnaire and submitting matrices that were constituted of management tools and enabler criteria of EFQM model.

Step 14: Determining the final matrix of the relationship between the tools and criteria:

Having the 30 relationship matrices obtained from experts' opinion, we convert these matrices to a final relationship matrix using the simple average formula which is as follows, in table 20.

Each crisp member of the matrix is shown with ([r.sub.mn]) and each fuzzy member of the matrix is shown with ([r.sup.f.sub.mn]), so that m is the desired criterion and n is the desired tool and ([r.sub.mn]) expresses the relationship between the desired tool and criterion.

Step 15: Initial prioritization of tools using SAW method:

In this step, we'll obtain the initial prioritization (primary prioritization = [SIGMA][w.sub.m] x [r.sub.mn]) of each tool having the values obtained from the previous step and using the simple weighted sum of the values (-SAW: simple average weight). Then, obtained values of the each column are aggregated to obtain the initial prioritization of each tool.

Step 16:

Determining the final crisp weights of the management tools and final crisp prioritization of management tools

The final crisp and fuzzy prioritization of each management tools is obtained as table 22 given the descriptions provided, by multiplying the initial values of prioritization of tools, improvement ratio, and the weight of each management tools.

Step 17:

Determining the final fuzzy weights of the management tools, de-fuzzying the fuzzy weights and fuzzy prioritization of management tools

In this step, the final fuzzy prioritization of each management tool is obtained as table 22, by multiplying the initial fuzzy values of prioritization of tools, improvement ratio, and the weight of each management tools obtained from Shannon entropy.

The second hypothesis: There isn't a significant difference between the results of the prioritization of affecting management tools on implementation of the EFQM model in Jondishapour Company using fuzzy and crisp QFD approaches.

[H.sub.0]: P=0

[H.sub.1]: P[not equal to]0

Step 18. Comparing the results of crisp and fuzzy prioritization of management tools:

We can perform the calculations having the crisp and fuzzy values of prioritization of enabler criteria of EFQM model that is presented in table 23 as follows:

Table 23: calculating the crisp and fuzzy correlation coefficient of each management tools Y^2 X^2 Xy Fuzzy 2783.618 2815.364 2799.446 52.76 2999.753 3019.503 3009.612 54.77 2832.368 2817.486 2824.918 53.22 2417.689 2402.96 2410.313 49.17 2799/467 2781/508 2790/473 52/91 1790.982 1767.362 1779.133 42.32 2841/956 2844/089 2843/022 53/31 3858.894 3891.264 3875.046 62.12 1769.044 1731.392 1750.117 42.06 2539.152 2534.116 2536.633 50.39 2642.988 2607.124 2624.995 51.41 2374.613 2325.168 2349.761 48.73 2622.464 2711.285 2666.505 51.21 2419.656 2414.74 2417.197 49.19 2201.486 2185.563 2193.51 46.92 38894.13 38848.92 38870.68 760.49 Y^2 Crisp Tools 2783.618 53.06 Business process Management 2999.753 54.95 Strategic Management 2832.368 53.08 Production Management 2417.689 49.02 Supply chain Management 2799/467 52/74 Consumer-relationship Management 1790.982 42.04 Change Management 2841/956 53/33 Financial Management 3858.894 62.38 Project Management 1769.044 41.61 Energy Management 2539.152 50.34 Information Management 2642.988 51.06 Technology Management 2374.613 48.22 Knowledge Management 2622.464 52.07 Human resource Management 2419.656 49.14 Total quality Management 2201.486 46.75 Inventory Management 38894.13 759.79 Total

Calculation of the standard deviation (SD) for both results of the crisp and fuzzy prioritization of management tools showed that the standard deviation of the mean of each group is almost identical. The standard deviation for the crisp prioritization of tools is equal to 5/095744 and it is equal to 4/912 for the fuzzy prioritization of tools. Therefore, since the change rate is 10% for crisp values and 9% for fuzzy values, it shows that the results of crisp and fuzzy prioritization of management tools are the same or in other words, there isn't a significant difference between the results obtained from crisp and fuzzy approaches. The correlation coefficient is calculated using the following formula and table 23, which indicates that there is a high correlation (p = 0/9983) between crisp and fuzzy values. And the correlation coefficient indicates that there isn't a statistically significant difference between the results of the prioritization of management tools in the two crisp and fuzzy approaches. Therefore, hypothesis H0 is rejected and hypothesis H1 is accepted. Calculation of the correlation coefficient between the crisp (x) and fuzzy (y) values:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

This shows a high correlation coefficient between fuzzy (x) and crisp (y) values. Therefore, there isn't a significant difference between the results of both the approaches.

Discussion:

Identical results of the prioritization of management tools affecting implementation of the EFQM model with crisp and fuzzy approaches confirmed that the model is able to resolve the ambiguity of the experts' opinion.

The results show that the highest level of performance in implementing the EFQM enabler criteria in Jondishapour Company is in the criteria of policy, strategy and employees. It means that the company should improve these criteria and consequently prioritize them more in this context so as to be able to achieve competitive advantage against domestic and foreign competitors. Another point is that foreign competitors have acquired the most points and this indicates that there is a gap between domestic and foreign competitors [3].

Conclusion:

The results represents that the company should improve these criteria and consequently prioritize them more in this context so as to be able to achieve competitive advantage against domestic and foreign competitors.

ARTICLE INFO

Article history:

Received 15 April 2014

Received in revised form 22 May 2014

Accepted 25 May 2014

Available online 15 June 2014

REFERENCES

[1] Ignacio, Jose, 2005. theoretical foundation of EFQM model, the resource based view, total quality management, vol. 16.

[2] Yousefie, S., M. Mohammadi, 2010. selection effective management tools on setting European Foundation for Quality management(EFQM) model by a quality function deployment (QFD) approach.

[3] Shin, J.S. and K.J. Kim, 2000. Effect and choice of the weighting scale in QFD, Quality Engineering, 12(3): 347-356.

[4] Sila, I. and M. Ebrahimpour, 2003. Examination and comparison of the critical factors of total quality management (TQM) across countries. International Journal of Production Research, 41(2): 235-268.

[5] Elshennawy, A.K., 2004. Quality in the new age and the body of knowledge for quality engineers. Total Quality Management, 15(5)(6): 603-614.

(1) Mohammad Ghezel Ayagh, (2) Hassan Soltani, (3) Siavash Rezaei, (4) Elahe Ahmadian, (5) Abdolmajid Farokhian

(1) Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Box. 76169133, Kerman, Iran. (2) Department of Management, Faculty of Management, Neyriz Branch, Islamic Azad University, Box. 311-74915, Neyriz, Iran. (3,4,5) Department of Management, Faculty of Management, Kerman branch, Islamic Azad University, Box. 7635131167, Kerman, Iran.

Corresponding Author: Siavash Rezaei, Department of Management, Faculty of Management, Kerman branch, Islamic Azad University, Box. 7635131167, Kerman, Iran.

Table 1: Frequency distribution and percentage of Experts' gender Frequency percentage Frequency Gender 0.6 18 Male 0.4 12 Female 1 30 Total Table 2: Distribution of Experts based on Education status Frequency percentage Frequency Level of education 0.1 3 Diploma 0.6666667 20 B.A. 0.2333333 7 M.A. and higher 1 30 Total Table 3: Distribution of Experts based on work experience Frequency percentage Frequency work experience 0.36666667 11 1 - 3 years 0.36666667 11 3 - 6 years 0.166667 5 6 - 9 years 0.1 3 More than 9 years 1 30 Total Table 4: The final matrix of paired comparisons Processes Partnerships Employees Policy and strategy 1.6093 1.4631 4.8379 1.4027 0.5469 1.3258 0.6015 1 1.6093 0.7598 1 2.1202 0.457 1 2.1878 3.0692 1 0.3329 2.6177 1.8285 Processes Leadership The final matrix 1.6093 1 Leadership 0.5469 0.7128 Policy and strategy 1.6093 0.2066 Employees 0.457 0.6834 Partnerships 1 0.6213 Processes Table 5: the normalized matrix of paired comparisons Weighted Average Processes Partnerships 0.314877267 0.308153 0.376932 0.113872475 0.104703 0.083934 0.17639281 0.308153 0.195744 0.215490404 0.087508 0.257626 0.179367044 0.191483 0.085764 Weighted Average Employees Policy and strategy 0.314877267 0.430231 0.148906132 0.113872475 0.053491 0.10614925 0.17639281 0.088929 0.225057586 0.215490404 0.194559 0.3257932 0.179367044 0.23279 0.194093857 Weighted Average Leadership The normalized final matrix 0.314877267 0.310164 Leadership 0.113872475 0.221085 Policy and strategy 0.17639281 0.06408 Employees 0.215490404 0.211966 Partnerships 0.179367044 0.192705 Processes Table 8. The final fuzzy paired comparison matrix Processes 1/9293 1/6093 1/4142 1/5422 1/6817 0/5468 0/5569 0/3996 1/9293 1/6093 1/4142 0/84089 0/5183 0/457 0/4936 1 1 1 1 0/4143 Partnerships 1/9293 1/4631 1.3642 6.3921 1/6817 0/3258 0.3311 0.6967 1/9293 0/7598 0.7711 1 0/5183 1 1 2.8703 1 0/3329 0.3081 3.2447 Employees 1/9293 4.8379 4.1195 1.4877 1/6817 0.6015 0.6163 1 1/9293 1 1 2.4136 0/5183 2.1878 1.9293 4.2703 1 2.6177 2.2133 2.1351 Policy and strategy 1/9293 1.4028 1.2968 1 1/6817 1 1 0.7711 1/9293 2.1202 2.296 0.2427 0/5183 3.0692 2.502 0.7329 1 1.8285 1.6817 0.7071 Leadership 1/9293 1 1 Leadership 1/6817 0.7128 0.6721 Policy and strategy 1/9293 0.2066 0.1969 Employees 0/5183 0.6834 0.9484 Partnerships 1 0.9213 0.6163 Processes Table 11: Weights of criteria obtained using Shannon entropy method Weight of criteria [e.sub.i] = E([W.sub.i])/ [SIGMA] E([W.sub.i]) Enabler criteria of EFQM 0.208703699 Leadership 0.187576193 Policy and strategy 0.192186203 Employees 0.21071503 Partnerships 0.200818874 Processes Table 13: Values of normalized crisp priority Crisp weight of criteria Fm Criteria 31.49 0.314926 Leadership 11.38 0.1138455 Policy 21.54 0.1763705 Employees 17.93 0.2154862 Partnerships 0.1793717 Processes Table 14: Determining the final fuzzy weight of criteria Deffuzing Normalize (1+2m+u)/4 U M 0.30764201 0.0138555 0.499875359 0.49304366 0.10769236 0.704965 0.217476378 0.15235776 0.17212996 1.12678 0.274032666 0.27230681 0.21301689 1.39443 0.311776698 0.35365129 0.19951878 1.30607 0.358613719 0.30598720 6.5461005 Normalize L Criteria 0.30764201 0.439566 Leadership 0.10769236 0.151836 Policy 0.17212996 0.258710 Employees 0.21301689 0.314191 Partnerships 0.19951878 0.278191 Processes Table 15: Non-fuzzied and normalized values Final fuzzy percentage Normalize Criteria 30.764201 0.30764201 Leadership 10.769236 0.10769236 Policy 17.212996 0.17212996 Employees 21.301689 0.21301689 Partnerships 19.951878 0.9951878 Processes 100 1 Total Table 17: calculating the standard deviation (x-x-)^2 x-[x.sup.-] Fuzzy Criteria 115.8637 10.764 30.76 Leadership 85.303696 -9.236 10.76 Policy 7.761796 -2.786 17.21 Employees 1.700416 1.304 21.3 Partnerships 0.002116 -0.046 19.95 Processes 210.63172 19.996 Table 18: Calculating the standard deviation of crisp value of criteria (x-x)^2 x-[x.sup.-] Crisp Criteria 132.08 11.49 31.49 Leadership 74.23 8.62 11.38 Policy 5.58 2.36 17.64 Employees 2.40 1.55 21.55 Partnerships 4.26 2.06 17.94 Processes 218.54 20.00 Table 19: Calculation of correlation coefficient between crisp and fuzzy values of criteria y^2 x^2 x^y Crisp Fuzzy Criteria 991.7839 946.1776 968.7124 31/49 30.76 Leadership 129.6091 115.7776 122.4983 11.38 10.76 Policy 311.0637 296.1841 303.5345 17.64 17.21 Employees 464.3422 453.69 458.9852 21.55 21.3 Partnerships 321.7431 398.0025 357.8471 17.94 19.95 Processes 2218.546 2209.832 2211.577 100.00 100.00 Table 20: Final relationship matrix between tools and criteria Business Supply Production Strategic Process Chain Customer Management Management Management Management Relationship 6.609 7.19 6.70 5.64 5.90 16 59 48 95 6.896 7.34 6.29 5.94 6.55 38 32 84 62 5.954 5.10 5.82 5.09 6.59 06 67 122 93 5.245 6.07 6.03 5.90 6.51 6 512 95 75 6.658 6.42 6.03 6.34 5.72 2 716 9 28 31.36 32.1 30.8 28.9 31.3 34 980 428 05 Production Change Financial Project Energy Management Management Management management management 6.609 5.43 6.10 7.80 5.42 06 2 95 7 6.896 5.76 6.22 7.36 6.46 39 5 95 8 5.954 5.98 6.26 6.59 5.02 55 2 51 4 5.245 5.46 6.68 7.03 5.50 29 8 93 0 6.658 6.51 6.10 7.56 6.64 34 2 51 2 31.36 29.1 31.3 36.3 29.0 56 8 78 6 Human Production Information Technology Knowledge Resource Management management management management Management 6.609 5.85 5.683 4.891 7.092 5 6 9 2 6.896 5.98 6.638 5.648 5.363 93 4 3 3 5.954 5.61 5.233 6.225 5.500 14 4 5 5.245 5.64 6.426 5.867 5.792 83 6 2 7 6.658 6.38 6.426 6.266 5.911 87 6 3 4 31.36 29.6 30.39 28.89 29.66 23 8 9 01 Total Production Quality Inventory Management Management Management Criteria 6.609 6.1828 5.719 Leader 2 ship 6.896 6.6426 5.989 Policy 2 5.954 5.8363 4.648 Emplo 1 yee 5.245 5.0579 4.899 Partne 6 rships 6.658 5.3267 6.350 Proces 9 ses 31.36 29.046 27.60 Total 3 71 Table 21: Initial prioritization of Tools Fuzzy Crisp Tools Number 5.5179 5.4977 Inventory Management 1 5/7841 5.778 Total quality Management 2 6/0221 6.1228 Human resource Management 3 5.7298 5/6699 Knowledge Management 4 6.04528 6.2244 Technology Management 5 5/9253 5.9196 Information Management 6 5/7701 5.7086 Energy Management 7 7.3052 7.3354 Project Management 8 6/2687 6/2709 Financial Management 9 5/806 5/7676 Change Management 10 6.2224 6/2023 Customer Relationship management 11 5.7821 5.765 Supply chain Management 12 6.2039 6/2393 Business process Management 13 6.4408 6.4617 Strategic Management 14 6.2587 6.2413 Production Management 15 Table 22: Final fuzzy priority of each management tool Fuzzy Crisp Tools 0.527570716 0.530581 Business process Management 0.547716 0.549493 Strategic Management 0.532230 0.530751 Production Management 0.491701 0.490247 Supply chain Management 0.529143 0.527434 Consumer-relationship Management 0.4232004 0.420401 Change Management 0.5330812 0/533268 Financial Management 0.621223 0.623791 Project Management 0.420583 0.416100 Energy Management 0.503878 0.5033942 Information Management 0.5140818 05106065 Technology Management 0.487253 0.482160 Knowledge Management 0.512110 0.520674 Human resource Management 0.491671 0.491352 Total quality Management 0.469234 0.467516 Inventory Management

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Title Annotation: | quality function deployment |
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Author: | Ayagh, Mohammad Ghezel; Soltani, Hassan; Rezaei, Siavash; Ahmadian, Elahe; Farokhian, Abdolmajid |

Publication: | Advances in Environmental Biology |

Article Type: | Report |

Date: | Jun 1, 2014 |

Words: | 4927 |

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