Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process: from two different forms of evaluation information and matching perspective

doi:10.23919/JSEE.2023.000099

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (4): 939-954.doi: 10.23919/JSEE.2023.000099

• SYSTEMS ENGINEERING • Previous Articles

Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process: from two different forms of evaluation information and matching perspective

Xin HUANG¹^,²(), Xiaoyan QI³^,*(), Hongzhuan CHEN¹, Xiang CAI⁴()

¹ College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
² Management School, Anhui University, Hefei 230601, China
³ Nursing School, Anhui Medical University, Hefei 230601, China
⁴ Business School, Guilin University of Electronics and Technology, Guilin 541004, China

Received:2021-09-03 Accepted:2023-07-19 Online:2023-08-18 Published:2023-08-28
Contact: Xiaoyan QI E-mail:hxmic@163.com;ellaea2134392@163.com;gdcaixiang@guet.edu.cn
About author:
HUANG Xin was born in 1992. He received his B.S. degree from Anhui University of Traditional Medicine, M.S. degree in management science and engineering from Guilin University of Electronics and Technology, and Ph.D. degree from Nanjing University of Aeronautics and Astronautics. He is an assistant professor in Anhui University. His research interests include supply chain coordination, complex equipment development management, and decision analysis. E-mail: hxmic@163.com

QI Xiaoyan was born in 1994. She received her B.S. degree from Anhui University of Traditional Medicine, Master’s degree in Far Eastern University (Philippine), and Ph.D. degree in Nursing from Philippines Women ’s University. Since 2022, she has been an assistant professor in Nursing School, Anhui Medical University. Her research interests include decision analysis and nursing education. E-mail: ellaea2134392@163.com

CHEN Hongzhuan was born in 1977. She received her B.S. and M.Eng. degrees from Shandong University of Science and Technology, and Ph.D. degree from Hohai University. She is a professor in College of Economics and Management, Nanjing University of Aeronautics and Astronautics. Her research interests include decision analysis, complex equipment development management, quality management, and supply chain coordination. E-mail: chz-hhu@163.com

CAI Xiang was born in 1968. He received his Ph.D. degree from China University of Mining and Technology. He is a professor in the Business School, Guilin University of Electronics and Technology. His research interests include decision analysis, technical innovation, and enterprise marketing. E-mail: gdcaixiang@guet.edu.cn

Abstract

Abstract:

With the development of central-private enterprises integration, selecting suitable key suppliers are able to provide core components for smart complex equipment. We consider selecting suitable key suppliers from matching perspective, for it not only satisfies natural development of smart complex equipment, it is also a good implementation of equipment project in central-private enterprises integration context. In in this paper, we carry out two parts of research, one is evaluation attributes based on comprehensive analysis, and the other is matching process between key suppliers and core components based on the matching attribute. In practical analysis process, we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows. In the analysis process, we adopt entropy-maximum deviation method (MDM)-decision-making trial and evaluation laboratory (DEMATEL)-technique for order preference by similarity to an ideal solution (TOPSIS) to obtain a comprehensive analysis. The entropy-MDM is applied to get weight value, DEMATEL is utilized to obtain internal relations, and TOPSIS is adopted to get ideal evaluated solution. We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean. Moreover, based on the aforementioned attributes and generalized power Heronian mean operator, we aggregate preference information to acquire relevant satisfaction degree, then combine the constructed matching model to get suitable key supplier. Through comprehensive analysis of selecting suitable suppliers, we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment. Through analysis for relevant factors, we find that leading role and service level are also significant for the smart complex equipment development process.

Key words: smart complex equipment, key supplier, entropy- maximum deviation method (MDM), decision-making trial and evaluation laboratory (DEMATEL), technique for order preference by similarity to an ideal solution (TOPSIS), heronian mean operator, central-private enterprises collaborative development

Xin HUANG, Xiaoyan QI, Hongzhuan CHEN, Xiang CAI. Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process: from two different forms of evaluation information and matching perspective[J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 939-954.

Figures/Tables 11

Fig 1

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

References 66

1	CHENG Y B, CHEN H Z, ZHUANG X S, et al Implementation strategy of supplier participating in aviation complex equipment collaborative development. System Engineering-Theory & Practice, 2017, 37 (6): 1568- 1580.
2	WANG H, FANG Z G, WANG D A, et al An integrated fuzzy QFD and grey decision-making approach for supply chain collaborative design of large complex products. Computers & Industrial Engineering, 2020, 140, 1016212.
3	DU B G, TIAN T, GUO J, et al Energy-cost-aware resource-constrained project scheduling for complex equipment system with activity splitting and combing. Experts Systems with Applications, 2021, 173, 114754. doi: 10.1016/j.eswa.2021.114754
4	LI R M, YANG N D, ZHANG Y L, et al Risk propagation and mitigation of design change for complex product development (CPD) projects based on multilayer network theory. Computers & Industrial Engineering, 2020, 142, 106370.
5	LI R M, YANG N D, ZHANG Y L, et al Impacts of module-module aligned patterns on risk cascading propagation in complex product development (CPD) interdependent networks. Physica A: Statistical Mechanics and its Applications, 2021, 564, 125531. doi: 10.1016/j.physa.2020.125531
6	HUANG X, CHEN H Z, MA P, et al A two-sided matching model for complex equipment production of central-private enterprises merging platform with reference effects. Soft Computing, 2021, 25, 10399- 10421. doi: 10.1007/s00500-021-05731-3
7	WANG Q, WEI K K, ZHANG Y, et al Data envelopment analysis method based on a common set of normalized weights using barging game thought. Computers & Industrial Engineering, 2021, 154, 107047.
8	YUE Q, ZHAG L L Two-sided matching for hesitant fuzzy numbers in smart intelligent technique transfer. Mechanical Systems and Signal Processing, 2020, 139, 106643. doi: 10.1016/j.ymssp.2020.106643
9	GALE G, SHAPELY L S College admissions and the stability of marriage. The American Mathematical Monthly, 1962, 69 (1): 9- 15.
10	CHEN J, SONG K J Two-sided matching in the loan market. International Journal of Industrial Organization, 2013, 31 (2): 145- 152. doi: 10.1016/j.ijindorg.2012.12.002
11	LI B D, YANG Y, SU J F, et al Two-sided matching model for complex product manufacturing tasks based on dual hesitant fuzzy preference information. Knowledge-Based Systems, 2019, 186, 104989. doi: 10.1016/j.knosys.2019.104989
12	WANG Z H, LI Y Y, GU F, et al Two-sided matching and strategic selection on freight resource sharing platforms. Physica A: Statistical Mechanics and its Applications, 2020, 559, 125014. doi: 10.1016/j.physa.2020.125014
13	CHAKRABORTY A, CITANNA A, OSTROVSKY M Two-sided matching with independent values. Journal of Economic Theory, 2010, 145 (1): 85- 105. doi: 10.1016/j.jet.2009.07.004
14	SOTOMAYOR M. Competition and cooperation in a two-sided matching market with replication. Journal of Economic Theory, 2019, 183: 1030−1056.
15	CHEN H Z, HUANG X, WANG W M Stackelberg optimization for spare part ’s collaborative innovation of complex products considering cost sharing. Chinese Journal of Management Science, 2020, 30 (7): 69- 76.
16	ZHOU J H, ZHU J J, WANG H H Relational contract coordination to the “main manufacturer supplier” mode of initial period of large passenger aircraft. Control & Decision, 2020, 35 (9): 2225- 2235.
17	PONGCHAROEN P, HIVKS C, BRAIDEN P M The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure. European Journal of Operational Research, 2004, 152 (1): 215- 225. doi: 10.1016/S0377-2217(02)00645-8
18	MOLL J V, JACOBS J, KUSRERS R, et al Defect detection-oriented lifecycle modeling in complex product development. Information and Software Technology, 2004, 46 (10): 665- 675. doi: 10.1016/j.infsof.2003.12.001
19	PRASAD V, DEGRANDE H, HOFF S, et al Lived experience of medical veterans transitioning from military service to civilian nursing education. Journal of Professional Nursing, 2020, 36 (5): 443- 447. doi: 10.1016/j.profnurs.2020.03.009
20	KUNDSON M D M, FACS, ERIC C A, et al Military-civilian partnerships in training, sustaining, recruitment, retention, and readiness: proceedings from an exploratory first-steps meeting. Journal of the American College of Surgeons, 2018, 227 (2): 284- 292. doi: 10.1016/j.jamcollsurg.2018.04.030
21	COWAN R, DORAY D F Quandaries in the economies of dual technologies and spillovers from military to civilian research and development. Research Policy, 1995, 24 (6): 851- 858. doi: 10.1016/0048-7333(94)00802-7
22	NIE M Y Space privatization in China’s national strategy of central-private enterprises integration: an appraisal of critical legal challenges. Research Policy, 2020, 52, 101372.
23	YU D J, XU Z S Intuitionistic fuzzy two-sided matching model and its application to personnel-position matching problems. Journal of Operational Research Society, 2020, 71 (2): 312- 321. doi: 10.1080/01605682.2018.1546662
24	KORKMAZ I, GOKCEN H, CETINYOKUS T An analytic hierarchy process and two-sided matching based decision support system for military personnel assignment. Information Sciences, 2008, 178 (14): 2915- 2927. doi: 10.1016/j.ins.2008.03.005
25	CHEN X, LI Z W, FAN Z P, et al Matching suppliers and demanders in knowledge: a method based on fuzzy axiomatic design. Information Sciences, 2016, 346/347, 130- 145. doi: 10.1016/j.ins.2016.01.096
26	QIU Y, GU D X, ZHANG H M, et al. Two-stage matching decision-making in medical service supply chain. International Journal of Logistics Research and Applications, 2022, 25(4/5): 623−638.
27	LIANG D C, HE X, XU Z S Multi-attribute dynamic two-sided matching method of talent sharing in incomplete preference ordinal environment. Applied Soft Computing, 2020, 93, 106427. doi: 10.1016/j.asoc.2020.106427
28	CHEN S Q, WANG Y M, SHI H L, et al A decision-making method for uncertain matching between volunteer teams and rescue tasks. International Journal of Disaster Risk Reduction, 2021, 58, 1- 18.
29	ROTH A E Common and conflicting interests in two-sided matching market. European Journal Review, 1985, 27 (1): 75- 96.
30	KLUMMP T. Two-sided matching with spatially differentiated agents. Journal of Mathematical Economics, 2009, 45(5/6): 376−390.
31	ALPERN S, KATRANTZI J Equilibria of two-sided matching games with common preferences. European Journal of Operational Research, 2009, 196 (3): 1214- 1222. doi: 10.1016/j.ejor.2008.05.012
32	HU G J, LI J T, TANG R The revealed preference theory of stable matchings with one-sided preferences. Games and Economic Behavior, 2020, 124, 305- 318. doi: 10.1016/j.geb.2020.08.015
33	LIU L, ZHANG Z S, WANG Z Two-sided matching and game on investing in carbon emission reduction under a cap-and-trade system. Journal of Cleaner Production, 2021, 282, 124436. doi: 10.1016/j.jclepro.2020.124436
34	LI Y Y, LIU Y Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users. Transportation Research Part E: Logistics and Transportation Review, 2021, 150 (C): 1- 20. doi: 10.1016/j.tre.2021.102329
35	XUE X S, ZHANG J Matching large-scale biomedical ontologies with central concept based partitioning algorithm and adaptive compact evolutionary algorithm. Applied Soft Computing, 2021, 106, 107343. doi: 10.1016/j.asoc.2021.107343
36	XU L, SHI J, CHEN J H Platform encroachment with price matching: introducing a self-constructing online platform into the sea-cargo market. Computers & Industrial Engineering, 2021, 156, 107266.
37	ZHAO D F, LI C B, WANG Q Q, et al Comprehensive evaluation of national electric power development based on cloud model and entropy method and TOPSIS: a case study in 11 countries. Journal of Cleaner Production, 2021, 277, 123190.
38	XU W X, GUO C, GUO S S, et al A novel quality comprehensive evaluation method based on product gene for solving manufacturing quality tracking problem of large equipment. Computers & Industrial Engineering, 2021, 152, 107032.
39	HUANG W D, WU M, CHEN L F, et al Multi-objective drilling trajectory optimization using decomposition method with minimum fuzzy entropy-based comprehensive evaluation. Applied Soft Computing, 2021, 107, 107392. doi: 10.1016/j.asoc.2021.107392
40	GUO T K, TANG S J, SUN J, et al A coupled thermal-hydraulic-mechanical modeling and evaluation of extraction in the enhanced geothermal system based on analytic hierarchy process and fuzzy comprehensive evaluation. Applied Energy, 2020, 258, 113981. doi: 10.1016/j.apenergy.2019.113981
41	SHAO W, NIE Y H, LIANG F Y, et al A novel comprehensive evaluation method for the corrosion initiation life of RC hollow plies in chloride environments. Construction and Building Materials, 2020, 249, 118801. doi: 10.1016/j.conbuildmat.2020.118801
42	WU Q, WU P, ZHOU L G, et al Some new Hamacher aggregation operators under single-valued neutrosophic 2-tuple linguistics environment and its applications to multi-attribute group decision making. Computers & Industrial Engineering, 2018, 116, 144- 162.
43	WANG H W, ZHAO B. Generalized ordinal sums of aggregation operators on bounded lattices. Information Sciences, 2020, 532: 139−154.
44	LIU P D, LIU Z M, ZHANG X Some intuitionistic uncertain Heronian mean operators and their application to group decision making. Applied Mathematics and Computation, 2014, 230, 570- 586. doi: 10.1016/j.amc.2013.12.133
45	EFREMIDZE L, STANLEY D J, KOWNAKTZKI C Entropy trading strategies reveal inefficiencies in Japanese stock market. International Review of Economics & Finance, 2021, 75, 464- 477.
46	FENG J H, GONG Z R Integrated linguistic entropy weight method and multi-objective programming model for supplier selection and order allocation in a circular economy: a case study. Journal of Cleaner Production, 2020, 277, 122597. doi: 10.1016/j.jclepro.2020.122597
47	HOU X R, LYU T, XU J, et al Energy sustainability evaluation of 30 provinces in China using an improved entropy weight-cloud model. Ecological Indicators, 2021, 126, 107657. doi: 10.1016/j.ecolind.2021.107657
48	LI L X, LI Y N, YE F, et al Carbon dioxide emissions quotas allocation in the Pearl River Delta region: evidence from the maximum deviation method. Journal of Cleaner Production, 2018, 177 (10): 207- 217.
49	FAROOQUE M, JAIN V, ZHANG A, et al. Fuzzy DEMATEL analysis of barriers to blockchain-based life cycle assessment in China. Computers & Industrial Engineering, 2020, 147: 106684.
50	KILIC H S, DEMIRCI A E, DELEN D An integrated decision analysis methodology based on IF-DEMATEL and IF-ELECTRE for personnel selection. Decision Support System, 2020, 137, 113360. doi: 10.1016/j.dss.2020.113360
51	DU Y W, LI X X Hierarchical DEMATEL method for complex systems. Experts System with Applications, 2021, 167, 113871. doi: 10.1016/j.eswa.2020.113871
52	CHEN Y F, RAN Y, HUANG G Q, et al A new integrated MCDM approach for improving QFD based on DEMATEL, and extended MULTIMOORA under uncertainty environment. Applied Soft Computing, 2021, 105, 107222. doi: 10.1016/j.asoc.2021.107222
53	MONTANARI R, MICALE R, E. BOTTAN E, et al Evaluation of routing policies using an interval-valued TOPSIS approach for the allocation rules. Computers & Industrial Engineering, 2021, 156, 107256.
54	LIU P D, LIU J L, MERIGO J M Partitioned Heronian means based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision-making. Applied Soft Computing, 2018, 62, 395- 422. doi: 10.1016/j.asoc.2017.10.017
55	SONG F, ZHOU Y T, WANG Y, et al Smart collaborative distribution for privacy enhancement in moving target defense. Information Sciences, 2019, 479, 593- 606. doi: 10.1016/j.ins.2018.06.002
56	LU Y Q, AGHAR M R Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing. Journal of Manufacturing Systems, 2020, 55, 348- 359. doi: 10.1016/j.jmsy.2020.05.001
57	ZHANG G, CHEN C H, ZHANG P, et al An integrated framework for active discovery and optimal allocation of smart manufacturing services. Journal of Cleaner Production, 2020, 273, 123144. doi: 10.1016/j.jclepro.2020.123144
58	CHANG F T, ZHOU G H, ZHANG C, et al A maintenance decision-making oriented collaborative cross-organization knowledge sharing blockchain network for complex multi-component systems. Journal of Cleaner Production, 2021, 282, 124541. doi: 10.1016/j.jclepro.2020.124541
59	ZHENG Y J, WANG Y, LING H F, et al Integrated civilian-military pre-positioning of emergency supplies: a multiobjective optimization approach. Applied Soft Computing, 2017, 58, 732- 741. doi: 10.1016/j.asoc.2017.05.016
60	FONG D K H, FU H Z, LI Z L Efficiency in shortage reduction when using a more expensive common component. Computers & Operations Research, 2014, 31 (1): 123- 138.
61	LI H L Multivariate time series clustering based on common principal component analysis. Neurocomputing, 2019, 349, 239- 247. doi: 10.1016/j.neucom.2019.03.060
62	MORGAN N R, DAVIS K D, RICHARDSON C, et al Common components analysis: an adapted approach for evaluating programs. Evaluation and Programming Plan, 2018, 67, 1- 9. doi: 10.1016/j.evalprogplan.2017.10.009
63	QU Y, FERNANDO D, GATTAS J M Experimental investigation of a novel concrete-timber floor panel system with digitally fabricated FRP-timber hollow core component. Construction and Building Materials, 2019, 227, 116667. doi: 10.1016/j.conbuildmat.2019.08.048
64	LIN R H, CHUANG C L, LIOU J J H, et al An integrated method for finding key suppliers in SCM. Experts Systems with Applications, 2009, 36 (3): 6461- 6465. doi: 10.1016/j.eswa.2008.07.078
65	MIOCEVIC D, KARANOVIV B C The mediating role of key suppliers relationship management practices on supply chain orientation−the organizational buying effectiveness link. Industrial Marketing Management, 2012, 41 (1): 115- 124. doi: 10.1016/j.indmarman.2011.11.015
66	MURPHY J, SMITH S Chefs and suppliers: an exploratory look at supply chain issues in an upscale restaurant alliance. International Journal of Hospitality Management, 2009, 28 (2): 212- 220. doi: 10.1016/j.ijhm.2008.07.003

Evaluation dimension	Indicator
Collaborative development process for smart complex equipment	Protection of core technology
	Availability of technology and equipment
	High integrity in coordination
	Large investment and high knowledge-intensive
central-private enterprises integration	Legal normalization
	Responding efficiency
	Interaction between two sides
Requirements for core components	Manufacturing cost
	Manufacturing cycle
	Quality requirement
	Special performance
Basic characteristics of key suppliers	Mutual adaptability
	Leading effect
	Service capacity

Evaluated influencing factor	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5
Protection of core technology	0.92	0.87	0.95	0.89	0.91
Availability of technology and equipment	0.73	0.69	0.81	0.71	0.70
High integrity in coordination	0.82	0.95	0.86	0.85	0.88
Large investment and high knowledge-intensive	0.73	0.76	0.79	0.82	0.80
Legal normalization	0.70	0.73	0.76	0.81	0.75
Responding efficiency	0.82	0.85	0.81	0.79	0.82
Interaction of two sides	0.56	0.70	0.63	0.65	0.73
Manufacturing cost	0.35	0.42	0.31	0.37	0.40
Manufacturing cycle	0.50	0.43	0.39	0.45	0.47
Quality requirement	0.85	0.92	0.83	0.86	0.89
Special performance	0.34	0.36	0.31	0.33	0.30
Mutual adaptability	0.85	0.87	0.82	0.89	0.92
Leading effect	0.75	0.78	0.80	0.79	0.83
Service capacity	0.63	0.72	0.68	0.67	0.65

Evaluated influencing factor	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5
Availability of technology and equipment	0.73	0.69	0.81	0.71	0.70
High integrity in coordination	0.82	0.95	0.86	0.85	0.88
Large investment and high knowledge-intensive	0.73	0.76	0.79	0.82	0.80
Interaction of two sides	0.56	0.70	0.63	0.65	0.73
Manufacturing cost	0.35	0.42	0.31	0.37	0.40
Manufacturing cycle	0.50	0.43	0.39	0.45	0.47
Quality requirement	0.85	0.92	0.83	0.86	0.89
Special performance	0.34	0.36	0.31	0.33	0.30
Mutual adaptability	0.85	0.87	0.82	0.89	0.92
Service capacity	0.63	0.72	0.68	0.67	0.65

Evaluated influencing factor	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5
Availability of technology and equipment	[0.70,0.72]	[0.65,0.75]	[0.75,0.82]	[0.73,0.78]	[0.82,0.86]
High integrity in coordination	[0.84,0.87]	[0.86,0.89]	[0.78,0.85]	[0.89,0.92]	[0.76,0.84]
Large investment and high knowledge-intensive	[0.67,0.75]	[0.75,0.81]	[0.73,0.80]	[0.81,0.86]	[0.76,0.82]
Interaction of two sides	[0.50,057]	[0.69,0.74]	[0.65,0.69]	[0.66,0.70]	[0.71,0.77]
Manufacturing cost	[0.36,0.39]	[0.41,0.45]	[00.33,0.38]	[0.36,0.40]	[0.38,0.41]
Manufacturing cycle	[0.46,0.51]	[0.42,0.48]	[0.40,0.45]	[0.42,0.46]	[0.46,0.50]
Quality requirement	[0.82,0.87]	[0.90,0.93]	[0.82,0.86]	[0.84,0.87]	[0.90,0.92]
Special performance	[0.32,0.35]	[0.37,0.39]	[0.30,0.36]	[0.32,0.35]	[0.32,0.37]
Mutual adaptability	[0.83,0.88]	[0.88,0.90]	[0.83,0.85]	[0.87,0.91]	[0.91,0.94]
Service capacity	[0.61,0.65]	[0.71,0.75]	[0.65,0.69]	[0.69,0.73]	[0.64,0.70]

Evaluated influencing factor	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5
Availability of technology and equipment	0.720	0.725	0.798	0.732	0.768
High integrity in coordination	0.837	0.908	0.837	0.877	0.840
Large investment and high knowledge-intensive	0.720	0.803	0.778	0.828	0.795
Interaction of two sides	0.547	0.694	0.650	0.665	0.735
Manufacturing cost	0.362	0.403	0.332	0.375	0.398
Manufacturing cycle	0.493	0.440	0.407	0.445	0.475
Quality requirement	0.847	0.886	0.835	0.858	0.900
Special performance	0.338	0.352	0.320	0.333	0.322
Mutual adaptability	0.853	0.885	0.830	0.890	0.923
Service capacity	0.630	0.718	0.675	0.690	0.660

Selecting suitable key supplier for core components during smart complex equipment central-private enterprises collaborative development process: from two different forms of evaluation information and matching perspective

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 11

References 66

Related Articles 0

Recommended Articles

Metrics

Comments

Key supplier to core component	Large investment and knowledge-intensive	Interaction between two sides	Manufacturing cost	Manufacturing cycle
$ {S_1} \to {C_1} $	[0.72,0.85]	[0.62,0.66]	[0.37,0.42]	[0.45,0.48]
$ {S_1} \to {C_2} $	[0.67,0.69]	[0.61,0.68]	[0.33,0.39]	[0.42,0.53]
$ {S_1} \to {C_3} $	[0.69,0.74]	[0.62,0.68]	[0.37,0.44]	[0.40,0.48]
$ {S_2} \to {C_1} $	[0.72,0.76]	[0.75,0.82]	[0.37,0.41]	[0.46,0.51]
$ {S_2} \to {C_2} $	[0.73,0.77]	[0.73,0.80]	[0.39,0.42]	[0.43,0.52]
$ {S_2} \to {C_3} $	[0.76,0.81]	[0.72,0.81]	[0.37,0.45]	[0.40,0.54]
$ {S_3} \to {C_1} $	[0.67,0.79]	[0.81,0.84]	[0.39,0.40]	[0.38,0.45]
$ {S_3} \to {C_2} $	[0.66,0.69]	[0.80,0.87]	[0.39,0.42]	[0.41.0.53]
$ {S_3} \to {C_3} $	[0.68,0.72]	[0.81,0.89]	[0.41,0.46]	[0.42,0.50]
$ {S_4} \to {C_1} $	[0.72,0.77]	[0.82,0.90]	[0.43,0.48]	[0.40,0.51]
$ {S_4} \to {C_2} $	[0.73,0.79]	[0.83,0.92]	[0.44,0.49]	[0.43,0.50]
$ {S_4} \to {C_3} $	[0.78,0.83]	[0.81,0.91]	[0.42,0.48]	[0.45,0.51]

Core component to key supplier	Availability of technology and equipment	High integrity in coordination	Special performance	Mutual adaptability	Service capacity
$ {C_1} \to {S_1} $	[0.65,0.71]	[0.73,0.84]	[0.42,0.47]	[0.61,0.70]	[0.81,0.87]
$ {C_1} \to {S_2} $	[0.63,0.78]	[0.76,0.82]	[0.45,0.49]	[0.63,0.75]	[0.84,0.92]
$ {C_1} \to {S_3} $	[0.71,0.76]	[0.75,0.83]	[0.47,0.51]	[0.67,0.71]	[0.82,0.89]
$ {C_1} \to {S_4} $	[0.73,0.81]	[0.72,0.81]	[0.41,0.49]	[0.62,0.84]	[0.80,0.93]
$ {C_2} \to {S_1} $	[0.72,0.83]	[0.70,0.84]	[0.40,0.52]	[0.61,0.85]	[0.78,0.84]
$ {C_2} \to {S_2} $	[0.73,0.77]	[0.71,0.78]	[0.42,0.55]	[0.63,0.82]	[0.71,0.83]
$ {C_2} \to {S_3} $	[0.63,0.84]	[0.80,0.86]	[0.43,0.52]	[0.67,0.81]	[0.75,0.81]
$ {C_2} \to {S_4} $	[0.67,0.73]	[0.76,0.83]	[0.45,0.54]	[0.72,0.75]	[0.71,0.77]
$ {C_3} \to {S_1} $	[0.53,0.59]	[0.72,0.86]	[0.47,0.52]	[0.71,0.78]	[0.70,0.82]
$ {C_3} \to {S_2} $	[0.54,0.62]	[0.71,0.82]	[0.45,0.53]	[0.65,0.72]	[0.74,0.81]
$ {C_3} \to {S_3} $	[0.62,0.71]	[0.74,0.85]	[0.47,0.54]	[0.73,0.82]	[0.65,0.75]
$ {C_3} \to {S_4} $	[0.57,0.75]	[0.75,0.84]	[0.43,0.49]	[0.73,0.86]	[0.76,0.81]

Key supplier to core component	Satisfaction degree
$ {S_1} \to {C_1} $	0.564
$ {S_1} \to {C_2} $	0.535
$ {S_1} \to {C_3} $	0.547
$ {S_2} \to {C_1} $	0.592
$ {S_2} \to {C_2} $	0.592
$ {S_2} \to {C_3} $	0.600
$ {S_3} \to {C_1} $	0.581
$ {S_3} \to {C_2} $	0.588
$ {S_3} \to {C_3} $	0.602
$ {S_4} \to {C_1} $	0.620
$ {S_4} \to {C_2} $	0.633
$ {S_4} \to {C_3} $	0.640

Supplier	Core component 1	Core component 2	Core component 3
Key supplier 1	0	0	0
Key supplier 2	1	0	0
Key supplier 3	0	1	0
Key supplier 4	0	0	1