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

INTRODUCTION

The 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
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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