Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach
Vineet JAIN1,*(),Tilak RAJ2
1. Department of Mechanical Engineering, Amity University Haryana, Gurgaon 122413, India 2. Department of Mechanical Engineering, YMCA University of Science and Technology, Faridabad 121006, India
Productivity has often been cited as a key factor in a flexible manufacturing system (FMS) performance, and actions to increase it are said to improve profitability and the wage earning capacity of employees. Improving productivity is seen as a key issue for survival and success in the long term of a manufacturing system. The purpose of this paper is to make a model and analysis of the productivity variables of FMS. This study was performed by different approaches viz. interpretive structural modelling (ISM), structural equation modelling (SEM), graph theory and matrix approach (GTMA) and a cross-sectional survey within manufacturing firms in India. ISM has been used to develop a model of productivity variables, and then it has been analyzed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. CFA is carried by SEM. EFA is applied to extract the factors in FMS by the statistical package for social sciences (SPSS 20) software and confirming these factors by CFA through analysis of moment structures (AMOS 20) software. The twenty productivity variables are identified through literature and four factors extracted, which involves the productivity of FMS. The four factors are people, quality, machine and flexibility. SEM using AMOS 20 was used to perform the first order four-factor structures. GTMA is a multiple attribute decision making (MADM) methodology used to find intensity/quantification of productivity variables in an organization. The FMS productivity index has purposed to intensify the factors which affect FMS.
. [J]. Frontiers of Mechanical Engineering, 2014, 9(3): 218-232.
Vineet JAIN,Tilak RAJ. Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach. Front. Mech. Eng., 2014, 9(3): 218-232.
Qualitative measure of interdependence of FMS factor
Assigned value (fij)
1
Very strong
5
2
Strong
4
3
Medium
3
4
Weak
2
5
Very weak
1
Tab.3
Variables
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
V
V
O
O
O
V
O
V
V
V
V
V
V
V
V
V
V
V
O
2
O
O
O
O
O
O
O
O
O
O
O
V
O
O
O
O
O
V
3
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
4
V
V
V
V
O
O
O
V
V
V
A
V
V
V
V
V
5
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
6
A
A
V
V
O
A
O
V
V
O
A
A
A
A
7
O
O
V
V
O
A
O
V
V
O
A
A
A
8
O
O
V
V
O
A
O
V
V
V
A
O
9
A
A
A
A
A
A
V
O
V
A
A
10
V
V
V
V
O
V
O
V
V
V
11
V
V
V
V
A
A
O
V
V
12
O
O
O
O
O
A
A
O
13
O
O
A
A
A
A
O
14
O
O
O
O
O
O
15
V
V
V
V
X
16
V
V
V
V
17
A
A
A
18
A
A
19
X
Tab.4
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
1
0
1
1
1
1
1
1
1
1
1
1
1
0
1
0
0
0
1
1
2
0
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
3
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
1
1
1
1
1
1
0
1
1
1
0
0
0
1
1
1
1
5
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
1
0
0
0
0
0
1
1
0
0
0
1
1
0
0
7
0
0
0
0
0
1
1
1
1
0
1
1
1
0
0
0
1
1
0
0
8
0
0
0
0
0
1
1
1
0
0
1
1
1
0
0
0
1
1
0
0
9
0
0
0
0
0
1
0
0
1
0
0
1
0
1
0
0
0
0
0
0
10
0
0
0
1
0
1
1
1
1
1
1
1
1
0
1
0
1
1
1
1
11
0
0
0
0
0
0
0
0
1
0
1
1
1
0
0
0
1
1
1
1
12
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
13
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
14
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
0
0
0
0
0
15
0
0
0
0
0
1
1
1
0
0
1
1
1
0
1
1
1
1
1
1
16
0
0
0
0
0
0
0
0
1
0
1
0
1
0
1
1
1
1
1
1
17
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
18
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
1
0
0
19
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
1
20
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
1
Tab.5
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1*
1
1*
1*
1*
1
1
2
0
1
1
0
0
0
0
0
0
1
0
0
1*
0
1*
0
1*
1*
1*
1*
3
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
1
1
1
1
1
1
0
1
1
1
1*
0
0
1
1
1
1
5
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
1
0
0
0
0
0
1
1
0
0
0
1
1
0
0
7
0
0
0
0
0
1
1
1
1
0
1
1
1
1*
0
0
1
1
1*
1*
8
0
0
0
0
0
1
1
1
0
0
1
1
1
0
0
0
1
1
1*
1*
9
0
0
0
0
0
1
0
0
1
0
0
1
0
1
0
0
0
0
0
0
10
0
0
0
1
0
1
1
1
1
1
1
1
1
0
1
1*
1
1
1
1
11
0
0
0
0
0
0
0
0
1
0
1
1
1
0
0
0
1
1
1
1
12
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
13
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
14
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
0
0
0
0
0
15
0
0
0
0
0
1
1
1
0
0
1
1
1
0
1
1
1
1
1
1
16
0
0
0
0
0
0
0
0
1
0
1
0
1
0
1
1
1
1
1
1
17
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
18
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
1
0
0
19
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
1
20
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
1
Tab.6
No.
Variables
Reachability set
Antecedent set
Intersection set
level
3
Unit labor cost
3
1, 2, 3
3
I
5
Customer satisfaction
5
1, 4, 5
5
I
12
Unit manufacturing cost
12
1, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15
12
I
13
Throughput time
13
1, 2, 4, 6, 7, 8, 10, 11, 13, 15, 16, 17, 18
13
I
14
Set up cost
9, 14
1, 4, 7, 9, 14
9, 14
II
17
Reduction in material flow
17
1, 2, 4, 6, 7, 8, 10, 11, 15, 16, 17, 18, 19, 20
17
III
18
Reduced work in process inventory
18
1, 2, 4, 6, 7, 8, 10, 11, 15, 16, 18, 19, 20
18
IV
6
Reduction in scrap percentage
6
1, 4, 6, 7, 8, 10, 15, 19, 20
6
V
19
Capacity to handle new product
19, 20
1, 2, 4, 7, 8, 10, 11, 15, 16, 19, 20
19, 20
VI
20
Ability to manufacture a variety of product
19, 20
1, 2, 4, 7, 8, 10, 11, 15, 16, 19, 20
19, 20
VI
11
Manufacturing lead time & setup time
11
1, 4, 7, 8, 10, 11, 15, 16
11
VII
7
Reduction in rework percentage
7, 8
1, 4, 7, 8, 10, 11, 15
7, 8
VIII
8
Reduction of rejection
7, 8
1, 4, 7, 8, 10, 11, 15
7, 8
VIII
16
Use of automated material handling devices
15, 16
1, 10, 15, 16
15, 16
VIII
4
Effect of tool life
4
1, 4, 10
4
IX
10
Trained worker
10
1, 10
10
X
1
Training
1
1
1
XI
2
Financial incentive
2
2
2
XI
Tab.7
Variables
3
5
12
13
9
14
17
18
6
19
20
11
7
8
15
16
4
10
1
2
Drive Power
3
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
5
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
12
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
13
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
9
0
0
1
0
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
4
14
0
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
17
0
0
0
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
3
18
0
0
0
1
1
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
4
6
0
0
1
1
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
5
19
0
0
0
0
1
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
6
20
0
0
0
0
1
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
6
11
0
0
1
1
1
0
1
1
0
1
1
1
0
0
0
0
0
0
0
0
8
7
0
0
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
12
8
0
0
1
1
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
10
15
0
0
1
1
0
0
1
1
1
1
1
1
1
1
1
1
0
0
0
0
12
16
0
0
0
1
1
0
1
1
0
1
1
1
0
0
1
1
0
0
0
0
9
4
0
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
0
0
0
14
10
0
0
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
0
0
15
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
19
2
1
0
0
1
0
0
1
1
0
1
1
0
0
0
1
0
0
1
0
1
9
Dependence power
3
3
11
13
12
5
14
13
10
11
11
8
6
6
5
4
3
3
1
1
Tab.8
Fig.1
Sr. No.
Dimensions
Variables/Items
Factor loading
Cronbach’s alpha
1
People
TrainingFinancial incentiveUnit labor cost
0.8490.8540.851
0.909
2
Quality
Effect of tool lifeCustomer satisfactionReduction in scrap percentageReduction in rework percentageReduction of rejection
0.8560.8080.8210.8420.789
0.953
3
Machine
Equipment utilizationTrained workerManufacturing lead time & setup timeUnit manufacturing costThroughput timeSet up cost
0.8030.8510.8340.7800.7540.640
0.942
4
Flexibility
AutomationUse of automated material handling devicesReduction in material flowReduced work in process inventoryCapacity to handle new productAbility to manufacture a variety of product
0.8480.8450.8300.8390.7640.818
0.937
Tab.9
Fig.2
Sr. No.
Dimensions
Variables/Items
Standardized estimate
p-value (* significant at p<0.001)
AVE
CR
1
People
Training
0.894
*
0.78
0.91
Financial incentive
0.945
*
Unit labor cost
0.803
*
2
Quality
Effect of tool life
0.939
*
0.80
0.95
Customer satisfaction
0.883
*
Reduction in scrap percentage
0.916
*
Reduction in rework percentage
0.886
*
Reduction of rejection
0.841
*
3
Machine
Equipment utilization
0.881
*
0.72
0.94
Trained worker
0.948
*
Manufacturing lead time & setup time
0.911
*
Unit manufacturing cost
0.885
*
Throughput time
0.727
*
Set up cost
0.723
*
4
Flexibility
Automation
0.907
*
0.73
0.94
Use of automated material handling devices
0.906
*
Reduction in material flow
0.880
*
Reduced work in process inventory
0.865
*
Capacity to handle new product
0.827
*
Ability to manufacture a variety of product
0.725
*
Tab.10
People
Quality
Machine
Flexibility
People
0.780
Quality
0.270
0.800
Machine
0.325
0.454
0.720
Flexibility
0.225
0.270
0.297
0.730
Tab.11
Fig.3
Permanent function at the subsystem/system level
Maximum value
Minimum value
Current value
PerP1*
1363
94
888
PerP2*
232864
6001
128676
PerP3*
1447520
770
629856
PerP4*
1472780
2756
634644
PerP*
676.64×1018
11.97×1011
45.67×1018
Tab.12
1
Cosmetatos G P, Eilon S. Effects of productivity definition and measurement on performance evaluation. European Journal of Operational Research, 1983, 14(1): 31–35
https://doi.org/10.1016/0377-2217(83)90286-2
2
Russell R S, Taylor-Iii B W. Operations Management along the Supply Chain. 6th ed. New York: John Wiley & Sons, 2009
3
Kilic H, Okumus F. Factors influencing productivity in small island hotels: Evidence from Northern Cyprus. International Journal of Contemporary Hospitality Management, 2005, 17(4): 315–331
https://doi.org/10.1108/09596110510597589
4
Tangen S. Understanding the concept of productivity. In: Proceedings of the 7th Asia Pacific Industrial Engineering and Management Systems Conference. Taipei, 2002
5
Jahromi M, Tavakkoli-Moghaddam R. A novel 0–1 linear integer programming model for dynamic machine-tool selection and operation allocation in a flexible manufacturing system. Journal of Manufacturing Systems, 2012, 31(2): 224–231
https://doi.org/10.1016/j.jmsy.2011.07.008
6
Cordero R. Changing human resources to make flexible manufacturing systems (FMS) successful. Journal of High Technology Management Research, 1997, 8(2): 263–275
https://doi.org/10.1016/S1047-8310(97)90006-7
7
Dai J B, Lee N K. Economic feasibility analysis of flexible material handling systems: A case study in the apparel industry. International Journal of Production Economics, 2012, 136(1): 28–36
https://doi.org/10.1016/j.ijpe.2011.09.006
8
Li J F, Long Y, Liu Q P. Advanced manufacturing technology, quality of product and level of performance: Empirical evidence from Chongqing. In: Proceedings of International Conference on Services Systems and Services Management. IEEE, 2005, 486– 490
9
Black S E, Lynch L M. Human-capital investments and productivity. American Economic Review, 1996, 86(2): 263–267
10
Hoffman J M, Mehra S. Management leadership and productivity improvement programs. International Journal of Applied Quality Management, 1999, 2(2): 221–232
https://doi.org/10.1016/S1096-4738(99)80091-8
11
Millea M, Fuess S M Jr. Does pay affect productivity or react to it?: Examination of U.S. manufacturing. The Quarterly Review of Economics and Finance, 2005, 45(4–5): 796–807
https://doi.org/10.1016/j.qref.2004.06.004
12
Kazerooni A, Chan F, Abhary K. A fuzzy integrated decision-making support system for scheduling of FMS using simulation. Computer Integrated Manufacturing Systems, 1997, 10(1): 27–34
https://doi.org/10.1016/S0951-5240(96)00012-2
13
Oke A. A framework for analysing manufacturing flexibility. International Journal of Operations & Production Management, 2005, 25(10): 973–996
https://doi.org/10.1108/01443570510619482
14
Buyurgan N, Saygin C, Kilic S E. Tool allocation in flexible manufacturing systems with tool alternatives. Robotics and Computer-integrated Manufacturing, 2004, 20(4): 341–349
https://doi.org/10.1016/j.rcim.2004.01.001
15
?zbayrak M, Bell R. A knowledge-based decision support system for the management of parts and tools in FMS. Decision Support Systems, 2003, 35(4): 487–515
https://doi.org/10.1016/S0167-9236(02)00128-8
16
Anderson E W, Fornell C, Rust R T. Customer satisfaction, productivity, and profitability: Differences between goods and services. Marketing Science, 1997, 16(2): 129–145
https://doi.org/10.1287/mksc.16.2.129
17
L?thgren M, Tambour M. Productivity and customer satisfaction in Swedish pharmacies: A DEA network model. European Journal of Operational Research, 1999, 115(3): 449–458
https://doi.org/10.1016/S0377-2217(98)00177-5
18
Sharma R K, Kumar D, Kumar P. Manufacturing excellence through TPM implementation: A practical analysis. Industrial Management & Data Systems, 2006, 106(2): 256–280
https://doi.org/10.1108/02635570610649899
19
Youssef M A, Al-Ahmady B. Quality management practices in a Flexible Manufacturing Systems (FMS) environment. Total Quality Management, 2002, 13(6): 877–890
https://doi.org/10.1080/0954412022000010217
20
Singh B, Narain R, Yadav R C. Identifying critical barriers in the growth of Indian Micro, Small and Medium Enterprises (MSMEs). International Journal of Business Competition and Growth, 2012, 2(1): 84–105
https://doi.org/10.1504/IJBCG.2012.044057
21
Swamidass P M, Waller M A. A classification of approaches to planning and justifying new manufacturing technologies. Journal of Manufacturing Systems, 1990, 9(3): 181–193
https://doi.org/10.1016/0278-6125(90)90050-R
Kashyap A S, Khator S K. Analysis of tool sharing in an FMS: A simulation study. Computers & Industrial Engineering, 1996, 30(1): 137–145
https://doi.org/10.1016/0360-8352(95)00020-8
Barron J M, Berger M C, Black D A. Do workers pay for on-the-job training? Journal of Human Resources, 1999, 34(2): 235–252
https://doi.org/10.2307/146344
26
Yellen J L. Efficiency wage models of unemployment. Information and Macroeconomics, 1984, 74(2): 200–205
27
Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 2000, 11(4): 403–419
https://doi.org/10.1023/A:1008930403506
28
?ztürk A, Kayal?gil S, ?zdemirel N E. Manufacturing lead time estimation using data mining. European Journal of Operational Research, 2006, 173(2): 683–700
https://doi.org/10.1016/j.ejor.2005.03.015
29
D'Souza D E, Williams F P. Toward a taxonomy of manufacturing flexibility dimensions. Journal of Operations Management, 2000, 18(5): 577–593
https://doi.org/10.1016/S0272-6963(00)00036-X
30
Gupta Y P, Somers T M. The measurement of manufacturing flexibility. European Journal of Operational Research, 1992, 60(2): 166–182
https://doi.org/10.1016/0377-2217(92)90091-M
31
Chan F T, Chan H. A comprehensive survey and future trend of simulation study on FMS scheduling. Journal of Intelligent Manufacturing, 2004, 15(1): 87–102
https://doi.org/10.1023/B:JIMS.0000010077.27141.be
32
Das S K. The measurement of flexibility in manufacturing systems. International Journal of Flexible Manufacturing Systems, 1996, 8(1): 67–93
https://doi.org/10.1007/BF00167801
Beamon B M. Performance, reliability, and performability of material handling systems. International Journal of Production Research, 1998, 36(2): 377–393
https://doi.org/10.1080/002075498193796
36
Kulak O. A decision support system for fuzzy multi-attribute selection of material handling equipments. Expert Systems with Applications, 2005, 29(2): 310–319
https://doi.org/10.1016/j.eswa.2005.04.004
Jain V, Raj T. Ranking of flexibility in flexible manufacturing system by using a combined multiple attribute decision making method. Global Journal of Flexible Systems Management, 2013, 14(3): 125–141
https://doi.org/10.1007/s40171-013-0038-5
39
Co H C, Patuwo B E, Hu M Y. The human factor in advanced manufacturing technology adoption: An empirical analysis. International Journal of Operations & Production Management, 1998, 18(1): 87–106
https://doi.org/10.1108/01443579810192925
40
Stecke K E. Planning and scheduling approaches to operate a particular FMS. European Journal of Operational Research, 1992, 61(3): 273–291
https://doi.org/10.1016/0377-2217(92)90357-F
41
Bengtsson J, Olhager J. Valuation of product-mix flexibility using real options. International Journal of Production Economics, 2002, 78(1): 13–28
https://doi.org/10.1016/S0925-5273(01)00143-8
42
Elkins D A, Huang N, Alden J M. Agile manufacturing systems in the automotive industry. International Journal of Production Economics, 2004, 91(3): 201–214
https://doi.org/10.1016/j.ijpe.2003.07.006
43
Silveira G D. A framework for the management of product variety. International Journal of Operations & Production Management, 1998, 18(3): 271–285
https://doi.org/10.1108/01443579810196471
44
ElMaraghy H A. Flexible and reconfigurable manufacturing systems paradigms. International Journal of Flexible Manufacturing Systems, 2005, 17(4): 261–276
https://doi.org/10.1007/s10696-006-9028-7
45
Azevedo S, Carvalho H, Cruz-Machado V. Using interpretive structural modelling to identify and rank performance measures: An application in the automotive supply chain. Baltic Journal of Management, 2013, 8(2): 208–230
https://doi.org/10.1108/17465261311310027
46
Govindan K, Palaniappan M, Zhu Q, Kannan D. Analysis of third party reverse logistics provider using interpretive structural modeling. International Journal of Production Economics, 2012, 140(1): 204–211
https://doi.org/10.1016/j.ijpe.2012.01.043
47
Raj T, Attri R, Jain V. Modelling the factors affecting flexibility in FMS. International Journal of Industrial and Systems Engineering, 2012, 11(4): 350–374
https://doi.org/10.1504/IJISE.2012.047542
48
Singh R K, Garg S K, Deshmukh S G, Kumar M. Modelling of critical success factors for implementation of AMTs. Journal of Modelling in Management, 2007, 2(3): 232–250
https://doi.org/10.1108/17465660710834444
49
Jain V, Raj T. Evaluating the variables affecting flexibility in FMS by exploratory and confirmatory factor analysis. Global Journal of Flexible Systems Management, 2013, 14(4): 181–193
https://doi.org/10.1007/s40171-013-0042-9
50
Ramanathan U, Muyldermans L. Identifying the underlying structure of demand during promotions: A structural equation modelling approach. Expert Systems with Applications, 2011, 38(5): 5544–5552
https://doi.org/10.1016/j.eswa.2010.10.082
51
Su Y, Yang C. A structural equation model for analyzing the impact of ERP on SCM. Expert Systems with Applications, 2010, 37(1): 456–469
https://doi.org/10.1016/j.eswa.2009.05.061
52
Lau A K, Yam R C, Tang E P. Supply chain integration and product modularity: An empirical study of product performance for selected Hong Kong manufacturing industries. International Journal of Operations & Production Management, 2010, 30(1): 20–56
https://doi.org/10.1108/01443571011012361
53
Vázquez-Bustelo D, Avella L, Fernández E. Agility drivers, enablers and outcomes: Empirical test of an integrated agile manufacturing model. International Journal of Operations & Production Management, 2007, 27(12): 1303–1332
https://doi.org/10.1108/01443570710835633
54
Rao R V, Padmanabhan K. Selection, identification and comparison of industrial robots using digraph and matrix methods. Robotics and Computer-integrated Manufacturing, 2006, 22(4): 373–383
https://doi.org/10.1016/j.rcim.2005.08.003
55
Rao R V. A material selection model using graph theory and matrix approach. Materials Science and Engineering A, 2006, 431(1–2): 248–255
https://doi.org/10.1016/j.msea.2006.06.006
56
Grover S, Agrawal V, Khan I. Human resource performance index in TQM environment. International Journal of Management Practice, 2005, 1(2): 131–151
https://doi.org/10.1504/IJMP.2005.007132
57
Grover S, Agrawal V P, Khan I A. A digraph approach to TQM evaluation of an industry. International Journal of Production Research, 2004, 42(19): 4031–4053
https://doi.org/10.1080/00207540410001704032
58
Wilcox J B, Bellenger D N, Rigdon E E. Assessing sample representativeness in industrial surveys. Journal of Business and Industrial Marketing, 1994, 9(2): 51–61
https://doi.org/10.1108/08858629410059834
59
Haleem A, Sushil, Qadri M A, Kumar S. Analysis of critical success factors of world-class manufacturing practices: An application of interpretative structural modelling and interpretative ranking process. Production Planning and Control, 2012, 23(10–11): 722–734
https://doi.org/10.1080/09537287.2011.642134
60
Shah R, Goldstein S M. Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 2006, 24(2): 148–169
https://doi.org/10.1016/j.jom.2005.05.001
61
Rigdon E E. The equal correlation baseline model for comparative fit assessment in structural equation modeling. Structural Equation Modeling, 1998, 5(1): 63–77
https://doi.org/10.1080/10705519809540089
62
J?reskog K G, S?rbom D. LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Chicago: Scientific Software International, 1993
63
J?reskog K G, S?rbom D. PRELIS 2 User’s Reference Guide: A Program for Multivariate Data Screening and Data Summarization: A Preprocessor for LISREL. 3rd ed. Chicago: Scientific Software International, 1996
64
Rao R V. Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods: Volume 2. London: Springer,2013
Sage A. Interpretive Structural Modeling: Methodology for Large-Scale Systems. New York: McGraw-Hill, 1977, 91–164
67
Warfield J N.Developing interconnection matrices in structural modeling. Systems, Man and Cybernetics, IEEE Transactions on, 1974, SMC-4(1): 81–87
68
Nunnally J C, Bernstein I H. Psychometric Theory. 3rd ed. New York: McGraw-Hill, 1994
69
Hair J F, Black W C, Babin B J, Anderson R E. Multivariate Data Analysis. 7th ed. New Jersey: Prentice Hall, 2009.
70
Anderson J C, Gerbing D W. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 1988, 103(3): 411–423
https://doi.org/10.1037/0033-2909.103.3.411
71
Byrne B M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. 2nd ed. London: Routledge, 2009
72
Fornell C, Larcker D F. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 1981, 18(1): 39–50
https://doi.org/10.2307/3151312
73
Carmines E G, Zeller R A. Reliability and validity assessment. London: Sage Publications, Inc., 1979
