Implementing DEA into the EFQM model for performance evaluation of organizations:
A case study in Iran Khodro Industrial Group
Owais Torabi, Din Mohammad Imani
Abstract In the current volatile and exacting business environment, managers are therefore
desirous to demonstrate that their organizations are excellent which may in the main be
achieved through continuous performance improvement. The foremost applicable and
appropriate tools that by the assessment of organizations shows however successful they are in
the structure excellence path is European Foundation for Quality Management (EFQM)
Excellence Model. in this paper, a hybrid approach based on data envelopment analysis (DEA)
and EFQM proposed to performance evaluation of the organizations. this hybrid approach
evaluated of the organizations based on EFQM model and also ranking efficiency them based
on DEA model. The inputs and outputs of DEA model are the EFQM model criteria. Proposed
hybrid model used to performance evaluation of thirty companies in Iran Khodro Industrial
Group. Comparison the results both models indicates correlation coefficient of almost 0.90 at
significance level of 0.01. The results show appropriate correlation between the two
models.
Keywords Performance evaluation. European foundation for quality management (EFQM).
Data envelopment analysis (DEA). Decision making unit; Ranking
efficiency
1 Introduction
Each organization, regardless of its activity, experience, structure or success to meet its own
structure goals, requires measuring its success to attain its ideal goals and business strategies.
In different organizations, there are various models for evaluating performance such as
European Foundation for Quality Management (EFQM). EFQM model was developed in
Europe in 1998. At present, it's applied as an executive tool to assist organizations how much
they are in the path of structure excellence and evaluate their balanced growth. This model
helps organizations to spot discrepancies by scrutiny their current and ideal positions, outline
some solutions to optimize their current position, implement them in step with these
discrepancies, and test their causes (Gorjietal 2011).
O.Torabi
・ D. M. Imani
Department of Noor, Iran University of Science and Technology, Tehran
e-mail: [email protected]
D. M. Imani
e-mail:
EFQM model is an integrative business system that covers whole management activities
composed of inputs and outputs (Black and Crumley 1997; Seghezzi 2001). In spite of the
overall acceptance of the EFQM model among academics, researchers and practitioners warn
that organizations have encountered difficulty when trying to measure their overall
performance in an exceedingly bid to identify strengths, still as areas for improvement and to
priorities efforts (Kanji 2001; Zerafat et al. 2008). Coulambidou and Dale (1995) in a very
survey of the British part of a significant European project on the benefits of self-assessment,
difficulty with measure, together with found that the majority of the companies experienced
variations in evaluation (Coulambidou and Dale 1995). Others problems also are attributed to
the simplicity of the method involved in computing these performanced ignore interactions of
criteria and sub-criteria, which might lead to wrong score assignments and therefore to a
discrepancy within the assessment result (Siow et al. 2001; Yang et al. 2001).
Based on the outcomes of the self-assessment, organizations will gain a lot of Information
by comparing their results with other organizations however, some organizations derive very
little benefit from self-assessment processes (Conti 2001). This can be due to the problems that
will arise, such as: the dearth of support by the quality department; and the problem in
implementing the advance actions (Ritchie and Dale 2000).
Data envelopment analysis (DEA) is one in all the necessary branches of operations
research science and originally proposed by Charnes, Cooper and Rhodes (1978) (Charnes et
al.1998). DEA is a non-parametric programming technique for efficiency evaluating a bunch
of homogenous decision making units (DMUs) with multiple inputs and multiple outputs
(Azadi et al. 2013; Mavi et al. 2015; Ramanathan and Ramanathan 2011). The primary CCR
model was applicable solely to technologies characterised by constant returns to scale (CRS)
globally. Although turned out to be a significant breakthrough, Banker, Charnes, and Cooper
(BCC) (Emrouznejad et al. 2008; Khodabakhshi and Aryavash 2014). Extended the CCR
model to accommodate technologies which exhibit variable returns to scale (VRS). Extended
CCR model to BCC model by Banker et al. , that admits the VRS and distinguishes between
technical inefficiencies and scale (Banker et al. 1984).
The main idea of DEA is to generate a group of optimal weights for each DMU in a group
of DMUs to maximize the ratio of its total of weighted outputs to its total of weighted inputs
while keeping all the DMU ratios at the most 1(Ghasemi et al. 2014). For its effectiveness in
distinctive ranking the DMUs, DEA has been wide applied in benchmarking and efficiency
evaluation of colleges (Charnes et al. 2013); branches of bank (Paradi et al. 2011); hospitals
(Mitropoulos et al. 2015) and others to demonstrate the effectiveness of this method in
determining the best crossing point and DMU.
In this paper, hybrid approach based on DEA and EFQM models used to performance
evaluation of organizations. The remained of paper organized as follow: the section 1 contain
the Introduction, the section 2 contain the Methods and new hybrid approach presented in
section 3, in section 4 we Case study. Conclusion and future remarks preseuted in section 5.
2 Methods
2.1 EFQM model
The EFQM is one of the models that deal with the assessment of function of an organization
using a self-assessment for measuring the concepts qualitative. Consequently, complete
understanding and proper usage of this model in an organization depend on the extensive
recognition of that model and completely different methods of self-assessment. The method of
self-assessment on the idea of this model in an organization needs to use the experienced
auditors (Vernero et al. 2007). EFQM model consists of 9 criteria. 5 criteria are known as
enablers and 4 others are called results. “Enablers” cover what an organization performs and
“results” include what an organization obtains. Enablers include leadership, people, policy &
strategy, partnership & resources and processes. Results are for people, customer, society and
key performance.
EFQM Model that is a non-prescriptive model has 9 criteria and considered as the core of
the model and the evaluating base of an organization. 5 of these criteria are called Enablers;
that cause strengthen the organization to achieve the excellent results. 4 other criteria are the
results that the organization should achieve them in different fields. Results are obtained by
enablers and enablers are improved by the results from the feedback (Leticia and Santos 2007).
The criteria in evaluating the organizational performance based on EFQM model have
1000 points (500 in enablers and 500 in results) and also the higher point in an organization,
the upper performance. The points of the criteria are shown in Fig. 1.
Enablers (500)
Results (500)
People Results
People (90)
Leadership
Policy and
(100)
Strategy (80)
Processes
(140)
(90)
Key
Customer Results
Performance
(200)
Results
Partnership and
Society Results
Resources (90)
(60)
(150)
Learning, creativity & innovation
Fig. 1 EFQM model
The numbers within the parentheses are the points assigned to the 9 criteria of the model
that shows the extent of achievement of the aims. For instance, the number 100 shows the
maximum points in leadership of the organization. The model acknowledges there are several
approaches to achieving appropriate excellence in all aspects of performance. It is supported
the premise that: excellence results with respect to people, customer, society and performance
are achieved through leadership driving policy and strategy, that's delivered through People,
Partnerships & resources and Processes.
2.2 DEA Model
DEA model developed by Charnes, Cooper and Rhodes (1978) and used applied linear
programming for the comparative evaluation of DMUs efficiencies. DEA goal is to check a
DMUs certain number performing similar tasks wich distinguish themselves in the number of
used inputs and manufactured outputs. There are a unit essentially two classic DEA models:
the CRS model, additionally called CCR (Charnes et al, 1978), and the VRS model or BCC
(Banker et al, 1984). The primary model considers constant returns to scale; the other assumes
variable returns to scale and no proportion among inputs and outputs (Chen et al. 2006; Grau
2011). After some mathematical methods, the model is rewritten, yielding in a linear
programming problem (LPP) shown in model (1).
𝑠
𝑀𝐴𝑋 𝑍0 = ∑ 𝑢𝑟 𝑦𝑟0
𝑟=1
St.
𝑚
∑ 𝑣𝑖 𝑥𝑖0 = 1
𝑖=1
𝑠
(1)
𝑚
∑ 𝑢𝑟 𝑦𝑟𝑗 − ∑ 𝑣𝑖 𝑥𝑖𝑗 ≤ 0
𝑟=1
(𝑖 = 1; 2; … ; 𝑚)
(𝑗 = 1; 2; … ; 𝑛)
𝑖=1
𝑢𝑟 ≥ 0; 𝑣𝑖 ≥ 0
(r = 1; 2;…; s)
As a LPP resolved for every DMU, if we've n DMUs n LPPs should resolved, with s+r call
variables. The model simply given is that the basis for all other DEA models.
3 DEA-PEM model
EFQM model used to organization evaluate. EFQM model based on inputs and outputs of DEA
model to efficiency evaluation of companies in organization shown in Fig. 2.
In this study, according to EFQM model which includes two parts of enablers and the
results of the organization, and using DEA model, the enablers used as inputs and the results
as outputs. At first, enablers and results scores calculated by total weighting method, and then
considering enablers as inputs and results as output. The inputs include Leadership that shown
to (𝒙𝟏 ), People (𝒙𝟐 ), Policy and Strategy (𝒙𝟑 ), Partnership and Resources (𝒙𝟒 ), Process
(𝒙𝟓 ) and also Outputs include People Results that has been shown(𝒚𝟏 ), Customer Results
(𝒚𝟐 ), Society Results (𝒚𝟑 ) , Key Performance Results (𝒚𝟒 ).
Inputs
Outputs
People Results(𝒚𝟏 )
Leadership(𝒙𝟏 )
People(𝒙𝟐 )
Customer Results(𝒚𝟐 )
DMUs
Policy and
Society Results(𝒚𝟑 )
Strategy(𝒙𝟑 )
Partnership and
Key
Performance
Results(𝒚𝟒 )
Resources(𝒙𝟒 )
Processes(𝒙𝟓 )
Fig. 2 The schematic structure of DEA-EFQM model
Optimal weights for each element considered. The standard questionnaire method of the
EFQM Model used to determine the value of each of inputs and outputs. After the distribution,
completion and collection and calculate the raw score for each inputs and outputs, the modeling
carried out as follows:
Suppose that we have n DMU, which each ith unit uses 5inputs vector, Xij to obtain 4 outputs
vector, Yrj. Thus, the DEA modeled as follows.
In a CCR multiple model, ur and vi are non-negative variables (greater than or equal to
zero), and there the possibility that the value of one variable is zero. To address this problem,
in 1979, a year after publication of Charnes, Cooper and Rhodes article, it suggested that value
of decision variables of the model (vi,𝑢r) considered greater than a very small amount like
and the CCR multiple model was amended as follows. (Avikran et al. 2008; Dash Wu et al.
2011).
𝑦
𝑀𝐴𝑋 𝑍0 = ∑ 𝑢𝑟 𝑦𝑟0
𝑟=1
St.
𝑥
∑ 𝑣𝑖 𝑥𝑖0 = 1
(𝑖 = 1; 2; … ; 𝑚)
𝑖=1
𝑦
𝑥
∑ 𝑢𝑟 𝑦𝑟𝑗 − ∑ 𝑣𝑖 𝑥𝑖𝑗 ≤ 0 (𝑗 = 1; 2; … ; 𝑛)
𝑟=1
𝑖=1
𝑢𝑟 ≥ 𝜀; 𝑣𝑖 ≥ 𝜀
𝜀(𝑣𝑒𝑟𝑦 𝑠𝑚𝑎𝑙𝑙 𝑎𝑚𝑜𝑢𝑛𝑡 )
(𝑟 = 1; 2 … ; 𝑠)
(2)
The model's dual problem can be written as follows:
𝑠
𝑀𝑖𝑛 𝑌0 = 𝜃 −
𝜀(∑ 𝑠𝑟+
𝑟=1
𝑚
+ ∑ 𝑠𝑖− )
𝑖=1
St.
𝑛
∑ 𝜆𝑗 𝑦𝑟𝑗 − 𝑠𝑟+ = 𝑦𝑟0
( 𝑖 = 1; 2; … ; 𝑚)
(3)
𝑗=1
𝑛
∑ 𝜆𝑗 𝑥𝑖𝑗 + 𝑠𝑖− = 𝜃𝑥𝑖0
(𝑗 = 1; 2; … ; 𝑛)
𝑗=1
𝜆𝑗 ; 𝑠𝑟+ ; 𝑠𝑖− ≥ 0
( 𝑟 = 1; 2; … ; 𝑠)
𝜃(𝑓𝑟𝑒𝑒 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠)
Surplus variable of sr+ shows the shortage in production for the specified output of r, and sris another additional variable which shows the input i used.
The model (2) is an input oriented model which provided based output oriented
model.
4 The case study
In order to test the proposed model used from EFQM model criteria. Data collected through
EFQM Model standard questionnaire. Data related to evaluation of 30 companies operating in
Iran Khodro Industrial Group which presented in Table 1. The questionnaire completed by a
team of senior and middle managers in a joint meeting who were aware of performance of the
organization in various fields.
Table 1
DMUs
The gathered information of EFQM model
(𝒙𝟏 )
100
(𝒙𝟐 )
90
(𝒙𝟑 )
80
(𝒙𝟒 )
90
(𝒙𝟓 )
140
(𝒚𝟏 )
90
(𝒚𝟐 )
200
(𝒚𝟑 )
60
(𝒚𝟒 )
150
DMU1
DMU2
DMU3
DMU4
DMU5
DMU6
DMU7
DMU8
DMU9
DMU10
DMU11
DMU12
DMU13
DMU14
DMU15
55
60
49
51
49
60
70
49
48
71
82
61
48
48
58
46
55
40
52
44
55
56
44
47
60
80
60
45
45
50
40
56
39
55
48
45
60
42
40
58
74
59
43
41
49
42
50
42
50
40
45
62
45
42
59
79
57
45
44
52
70
61
55
59
56
70
73
50
51
70
80
62
50
47
59
44
55
38
52
47
46
61
46
45
59
69
58
45
45
49
60
70
52
55
65
72
77
50
51
75
85
68
51
51
60
30
38
32
38
34
40
38
36
28
34
37
36
30
28
29
42
51
50
51
64
62
73
49
49
65
81
65
49
49
57
DMU16
DMU17
DMU18
DMU19
DMU20
DMU21
DMU22
DMU23
DMU24
DMU25
DMU26
DMU27
DMU28
DMU29
DMU30
82
70
61
48
55
71
68
47
55
39
56
79
84
45
60
70
69
59
45
50
68
60
46
53
35
57
69
75
40
58
72
65
60
40
48
66
59
42
52
36
56
66
74
47
53
74
67
64
46
52
69
60
47
52
37
50
65
73
48
55
77
72
65
49
57
73
69
50
60
40
60
75
85
49
63
56
60
56
45
50
61
60
41
51
36
51
70
74
50
45
85
78
68
50
60
77
69
53
66
45
62
78
89
59
69
40
41
38
37
34
31
29
31
32
28
29
30
30
29
27
83
75
66
49
58
72
66
56
62
44
60
77
81
57
65
In the next step, the data total for all EFQM model criteria calculated for each organization,
and they ranking of ascending, the results of the rankings presented in Table 3.
Data obtained from the questionnaire, on five factors of Enablers as inputs, and four factors
of results as outputs considered. EFQM Model determines weight for each criteria and totalize
of criteria weight to obtained final score. However, in proposed model this article there need
to criteria weight to achieved a final score. And the other point is that, in EFQM model
evaluation, whatever the data are larger, the organization is more efficient, but in the model of
DEA, fewer inputs have a positive relationship with organization performance (Grau 2011).
Thus, for modeling, data on five factors of Enablers (inputs) reversed (after subtracting 1) and
then used in the model. In Table 2, data of the inputs and outputs of the DEA model presented
by inverting inputs.
Table2 Data on the inputs and outputs of DEA by inverting inputs
(𝒙𝟏 )
(𝒙𝟐 )
(𝒙𝟑 )
(𝒙𝟒 )
(𝒙𝟓 )
DMUs
DMU1
DMU2
DMU3
DMU4
DMU5
DMU6
DMU7
DMU8
DMU9
DMU10
DMU11
DMU12
DMU13
DMU14
DMU15
DMU16
DMU17
DMU18
DMU19
DMU20
DMU21
DMU22
DMU23
DMU24
DMU25
DMU26
DMU27
DMU28
DMU29
DMU30
0.45
0.40
0.51
0.49
0.51
0.40
0.30
0.51
0.52
0.29
0.18
0.39
0.52
0.52
0.42
0.18
0.30
0.39
0.52
0.45
0.29
0.32
0.53
0.45
0.61
0.44
0.21
0.16
0.55
0.40
0.49
0.39
0.55
0.42
0.51
0.39
0.38
0.51
0.48
0.33
0.11
0.33
0.50
0.50
0.44
0.22
0.23
0.34
0.50
0.44
0.24
0.33
0.49
0.41
0.61
0.37
0.23
0.17
0.55
0.35
0.50
0.30
0.51
0.31
0.40
0.44
0.25
0.47
0.50
0.27
0.07
0.26
0.46
0.51
0.39
0.10
0.19
0.25
0.50
0.40
0.17
0.26
0.47
0.35
0.55
0.30
0.17
0.07
0.41
0.34
0.53
0.44
0.53
0.44
0.55
0.50
0.31
0.50
0.53
0.34
0.12
0.37
0.50
0.51
0.42
0.18
0.25
0.29
0.49
0.42
0.23
0.33
0.48
0.42
0.59
0.44
0.28
0.19
0.47
0.39
0.50
0.57
0.61
0.58
0.60
0.50
0.48
0.64
0.63
0.50
0.43
0.56
0.64
0.66
0.58
0.45
0.49
0.53
0.65
0.59
0.48
0.51
0.64
0.57
0.71
0.57
0.46
0.39
0.65
0.55
(𝒚𝟏 )
(𝒚𝟐 )
(𝒚𝟑 )
(𝒚𝟒 )
0.49
0.61
0.42
0.58
0.52
0.51
0.68
0.51
0.50
0.65
0.77
0.65
0.50
0.50
0.55
0.62
0.67
0.62
0.50
0.56
0.68
0.67
0.46
0.57
0.40
0.57
0.78
0.82
0.56
0.50
0.30
0.35
0.26
0.28
0.33
0.36
0.39
0.25
0.26
0.38
0.43
0.34
0.26
0.26
0.30
0.43
0.39
0.34
0.25
0.30
0.39
0.35
0.28
0.33
0.23
0.31
0.39
0.45
0.30
0.35
0.50
0.63
0.53
0.63
0.57
0.67
0.63
0.60
0.47
0.57
0.62
0.60
0.50
0.47
0.48
0.67
0.68
0.63
0.62
0.57
0.52
0.48
0.52
0.53
0.47
0.48
0.50
0.50
0.48
0.45
0.28
0.34
0.33
0.34
0.43
0.41
0.49
0.33
0.33
0.43
0.55
0.43
0.33
0.33
0.38
0.55
0.50
0.44
0.33
0.39
0.48
0.44
0.37
0.41
0.29
0.40
0.51
0.54
0.38
0.43
After obtaining the data in Table 2, the efficiency results any organization calculated by
modified CCR multiple model (input-oriented). In this model, both inputs and outputs-oriented
trends led to a result. The results of the modified CCR multiple model (input-oriented),
presented in Table 3.
As seen in Table 3, one of the ranking problems based on DEA model is that
simultaneously, several DMU have been performance equal to 1. As a result, there is no
possibility to rank the efficient units. In this regard, in 1993, Anderson and Peterson proposed
a method for ranking efficient units, which this method makes it possible to Determination of
unit most efficient. In this method, the units efficient score greater than 1. Thus, efficient units
are rating such as inefficient units the model called Anderson-Peterson (Mehrabian et al. 1999).
The calculation results of efficient units, according of Anderson - Peterson model, and the
result of rating organizations shown in Table 3.
Table 3 Results of the evaluation organization using CCR model (Ɛ = 0.001); EFQM and AndersonPeterson model
Rating according
Rank
Efficiency
Rank
Efficiency
Rank
DMUs
to EFQM
DMU1
DMU2
DMU3
DMU4
DMU5
DMU6
DMU7
DMU8
DMU9
DMU10
DMU11
DMU12
DMU13
DMU14
DMU15
DMU16
DMU17
DMU18
DMU19
DMU20
DMU21
DMU22
DMU23
DMU24
DMU25
DMU26
DMU27
DMU28
DMU29
DMU30
429
496
397
463
447
495
570
411
401
551
667
526
406
398
463
639
597
537
409
464
588
540
413
483
340
481
609
665
424
495
according
CCR multiple
model
21
12
29
18
20
13
7
24
27
8
1
11
26
28
19
3
5
10
25
17
6
9
23
15
30
16
4
2
22
14
0.6744
0.7449
0.5826
0.7287
0.6373
0.8988
0.8935
0.6287
0.5075
0.7821
1.000
0.7307
0.5820
0.4844
0.5679
1.000
0.9374
0.8026
0.6396
0.6520
0.7766
0.6838
0.5450
0.6335
0.4438
0.5803
0.8287
1.000
0.5076
0.5957
based on the
Anderson
Peterson model
15
11
22
13
18
5
6
20
28
9
1
12
23
29
25
1
4
8
17
16
10
14
26
19
30
24
7
1
27
21
0.6744
0.7449
0.5826
0.7287
0.6373
0.8988
0.8935
0.6287
0.5075
0.7821
1.8791
0.7307
0.5820
0.4844
0.5679
1.0800
0.9374
0.8026
0.6396
0.6520
0.7766
0.6838
0.5450
0.6335
0.4438
0.5803
0.8287
1.1976
0.5076
0.5957
15
11
22
13
18
5
6
20
28
9
1
12
23
29
25
3
4
8
17
16
10
14
26
19
30
24
7
2
27
21
In order to assess similarity of the results of the evaluation and ranking obtained based on
two models of EFQM and DEA (Anderson-Peterson), the results shown in Table 4.
Table4 Comparison of the ranking of organizations based on two models of EFQM, DEA
DMUs
DMU1
DMU2
DMU3
DMU4
DMU5
DMU6
DMU7
DMU8
DMU9
DMU10
DMU11
DMU12
DMU13
DMU14
DMU15
DMU16
DMU17
DMU18
DMU19
DMU20
DMU21
DMU22
DMU23
DMU24
DMU25
DMU26
DMU27
DMU28
DMU29
DMU30
EFQM model
DEA(Anderson-Peterson
model)
21
12
29
18
20
13
7
24
27
8
1
11
26
28
19
3
5
10
25
17
6
9
23
15
30
16
4
2
22
14
15
11
22
13
18
5
6
20
28
9
1
12
23
29
25
3
4
8
17
16
10
14
26
19
30
24
7
2
27
21
Comparison the results both models indicates correlation coefficient of almost 0.90 at
significance level of 0.01. The results show appropriate correlation between the two models in
Fig . 3.
Fig.3
correlation between PEM and DEA
5
Conclusion
EFQM and DEA models are two tools of important for measuring organizational performance.
The EFQM is one of the models that deal with the assessment of an organization using a selfassessment for measuring the concepts that some of them are more and more qualitative. While
DEA is a non-parametric programming technique for ranking efficiency. The main plan of
DEA is to generate a set of optimal weights for every DMU in a set of DMUs to maximize the
ratio of its total of weighted outputs to its total of weighted inputs while keeping all the DMU
ratios at most 1.
This paper evaluated the organizations based on EFQM model criteria and also ranking
efficiency them based on DEA model. In this hybrid approach used to performance evaluation
of thirty companies in Iran Khodro Industrial Group. Comparison the results both models
indicates correlation coefficient of almost 0.90 at significance level of 0.01. The results show
appropriate correlation between the two models. Finally, one of the main disadvantages of
EFQM Model that give same values as the default to all organizations is resolve. In this
conceptual-mathematical model, I have shown that the DEA mathematical model can be
combined with conceptual EFQM Model to produce an optimal ranking as a new ranking based
on the EFQM score and help the benchmarking process.
In this survey, we show that the proposed model has appropriate performance for
assessment of organizations. With the approval of the model, it provided appropriate context
for examination and research of performance excellence models and other similar models.
Referenc
Azadi, M., Saen, R. F., & Momeni. E., (2013). Developing a new neutral DEA model for media selection in the
existence of imprecise data. International Journal of Operational Research, 18(1), 16-34.
Avkiran, N. K., Tone, K., & Tsutsui, M. (2008). Bridging radial and non-radial measures of efficiencyin DEA.
Annals of Operations Research, 164, 127–138.
Banker, R. D., Charnes, A., & Cooper, W. W., (1984). Some models for estimating technical and scale
inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
Black, S.A., & Crumley, H.C., (1997). Self-assessment: what’s in it for us?. Total Quality Management, 8(2), 9699.
Charnes, A., Cooper, W. W., & Rhodes, E., (1978). Measuring the efficiency of decision making units. European
journal of operational research ,2(4), 429-444.
Chen, Y., Liang, L., & Yang, F. (2006). A DEA game model approach to supply chain efficiency. Annals of
Operations Research, 145, 5-13.
Coulambidou, L., & Dale, B.G., (1995). The use of quality management self-assessment in the UK: a state-ofthe-art study. Quality World Technical Supplement, 9(3), 110-118.
DashWu, D., Zhou, Z., & Birge, J. R. (2011). Estimation of potential gains from mergers in multiple periods: a
comparison of stochastic frontier analysis and Data Envelopment Analysis. Annals of Operations
Research, 186, 357–381.
Emrouznejad, A., Parker, B. R., & Tavares, G., (2008). Evaluation of research in efficiency and productivity: A
survey and analysis of the first 30 years of scholarly literature in DEA. Socio-economic planning
sciences, 42(3), 151-157.
Ghasemi, M. R., Ignatius, J., & Emrouznejad, A., (2014). A bi-objective weighted model for improving the
discrimination power in MCDEA. European Journal of Operational Research, 233(3), 640-650.
Gorji, M., Siami, S., & Jenabagha, N., (2011). Organizational Performance Assessment Based on Excellence
Model (EFQM) in the Fields of Staff and Customers Results. International Conference on Management
and Service, 8, 85-89.
Grau., N., (2011). Standards and excellence in project managrement–two sides of the same coin? Serbian project
management journal ,1(1), 1-16.
Khodabakhshi, M., & Aryavash, K. (2014). The fair allocation of common fixed cost or revenue using DEA
concept. Annalsof Operations Research, 214.
Kanji, G.K., (2001). Forces of excellence in Kanji’s business excellence model. Total Quality Management, 12(2),
259-272.
Leticia, M., & Santos, V., (2007). TQM and firms performance: An excellence model research based survey.
Journal of a business Science and Applied management, 12(2), 21-42.
Mavi, R. K., Saen, R. F., Mavi, N. K., Taleshi, S. S., & Majd, Z. R., (2015). Ranking bank branches using DEA
and multivariate regression models. International Journal of Operational Research, 24(3), 245-261.
Mehrabian, S., Alirezaee, M. R. & Jahanshahloo, G. R., (1999). A complete efficiency ranking of decision making
units in data envelopment analysis. Computational optimization and applications, 14(2), 261-266.
Mitropoulos, P., Talias, Μ. A., & Mitropoulos, I., (2015). Combining stochastic DEA with Bayesian analysis to
obtain statistical properties of the efficiency scores: An application to Greek public hospitals. European
Journal of Operational Research, 243(1), 302-311.
Paradi, J. C., Rouatt, S., & Zhu, H., (2011). Two-stage evaluation of bank branch efficiency using data
envelopment analysis. Omega. 39(1) 99-109.
Ramanathan, R., & Ramanathan, U., (2011). A performance measurement framework combining DEA and
balanced scorecard for the UK health sector. International Journal of Operational Research, 12(3), 257278.
Ritchie, L., & Dale, B.G., (2000). Self-assessment using the business excellence model: a study of practice and
process. International Journal of Production Economics, 66(3), 241-254.
Seghezzi, H.D., (2001). Business excellence: what is to be done?. Total Quality Management, 12(7), 861-866.
Siow, C.H.R., Yang, J.B., & Dale, B.G., 2001. A new modelling framework for organisational selfassessment:
development and application. Quality Management Journal, 8(4), 34-47.
Vernero, S., Nabitz, U., Bragonzi, G., Rebelli, A., & Molinari, R., (2007). A two-level EFQM self-assessment in
an Italian hospital. Journal of Health Care Quality Assurance, 120(3), 215-231.
Yang, J.B., Dale, B.G., & Siow, C.H.R., (2001). Selfassessment of excellence: an application of the evidential
reasoning approach. International Journal of Production Research, 39(16), 3789-3812.
Zerafat Angiz Langroudi, M., & Jandaghi, GH., (2008). Validity Examination of EFQM’s Results by DEA
Models. Journal of Applied Quantitative Methods, 3(3), 207-214.