Evaluation of Industrial Internet maturity in the cement industry

doi:10.12267/j.issn.2096-5931.2022.04.013

Abstract

Abstract:

In order to clarify the cement industry Industrial Internet development degree and future development direction, this paper propoese a three-level indicator system for the Industrial Internet maturity evaluation of the cement industry, based on industry status analysis, literature and policy analysis, survey questionnaires, expert interviews, etc. It propoese a method for evaluating the maturity of the Industrial Internet of cement enterprises based on entropy weight method and expert scoring method. An empirical study is carried out with 9 representative cement companies as the research sample. The results shows that the maturity of the Industrial Internet of large cement companies is generally uneven. Among the individual capabilities, the level of interconnection and interoperability is the highest, the data analysis and utilization are the second, and the comprehensive integration is the weakest.

Key words: Industrial Internet of cement industry, maturity, entropy method, evaluation

CLC Number:

YANG Heng, GAO Xinbo, LIU Liu, TANG Rui. Evaluation of Industrial Internet maturity in the cement industry[J]. Information and Communications Technology and Policy, 2022, 48(4): 76-84.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2022.04.013

http://ictp.caict.ac.cn/EN/Y2022/V48/I4/76

Figures/Tables 10

References 20

[1]	中国电信集团有限公司, 中国水泥协会, 中国信息通信研究院技术与标准研究所, 等. “5G +工业互联网”水泥行业白皮书[R]. 2020.
[2]	SCHUH G, ANDERL R, GAUSEMEIER J, et al. Industrie 4.0 maturity index: die digitale transformation von unternehmen gestalten[M]. Herbert Utz Verlag, 2017.
[3]	于秀明, 王程安. 中德智能制造成熟度模型对比[J]. 信息技术与标准化, 2016(7):55-58+70.
[4]	LEYH C, BLEY K, SCHÄFFER T, et al. SIMMI 4.0-a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0[C]// 2016 Federated Conference on Computer Science and Information Systems (Fedcsis). IEEE, 2016: 1297-1302.
[5]	LEE B, BENJAMIN D. Defining and assessing industry 4.0 maturity levels - case of the defence sector[J]. Production Planning & Control, 2018, 29(12):1030-1043.
[6]	RAFAEL L D, JAIONE G E, CRISTINA L, et al. An Industry 4.0 maturity model for machine tool companies[J]. Technological Forecasting and Social Change, 2020, 159:120203. doi: 10.1016/j.techfore.2020.120203 URL
[7]	CANETTA L, BARNI A, MONTINI E. Development of a digitalization maturity model for the manufacturing sector[C]// 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE, 2018:1-7.
[8]	WEBER C M, KÖNIGSBERGER J, KASSNER L, et al. M2DDM-a maturity model for data-driven manufacturing[J]. Procedia Cirp, 2017, 63:173-178. doi: 10.1016/j.procir.2017.03.309 URL
[9]	SCHUMACHER A, NEMETH T, SIHN W. Roadmapping towards industrial digitalization based on an Industry 4.0 maturity model for manufacturing enterprises[J]. Procedia Cirp, 2019,409-414.
[10]	SCHUMACHER A, EROL S, SIHN W. A maturity model for a ssessing Industry 4.0 readiness and maturity of manufacturing enterprises[J]. ScienceDirect, 2016:161-166.
[11]	EROL S, SCHUMACHER A, SIHN W. Strategic guidance towards Industry 4.0 - a three-stage process model[C]// International Conference on Competitive Manufacturing. 2016, 9(1):495-501.
[12]	SIMETINGER F, ZHANG Z. Deriving secondary traits of industry 4.0: a comparative analysis of significant maturity models[J]. Systems Research and Behavioral Science, 2020, 37(4): 663-678. doi: 10.1002/sres.2708 URL
[13]	SARSHAR M, FINNEMORE M, Haigh R, et al. SPICE: is a capability maturity model applicable in the construction industry[C]// International Conference on Durability of Building Materials and Components. 1999, 30:2836-2843.
[14]	JODLBAUER H, SCHAGERL M. Reifegradmodell industrie 4.0-ein vorgehensmodell zur identifikation von industrie 4.0 potentialen[J]. Informatik, 2016.
[15]	WEBER C, KÖNIGSBERGER J, KASSNER L, et al. M2DDM-a maturity model for data-driven manufacturing[J]. Procedia CIRP, 2017, 63:173-178. doi: 10.1016/j.procir.2017.03.309 URL
[16]	GEISSBAUER DR, VEDSO J, SCHRAUF S. Industry 4.0: build- ing the digital enterprise[R]. PWC, 2016.
[17]	USTUNDAG A, CEVIKCAN E. Industry 4.0: managing the digital transformation[M]. Springer, 2017.
[18]	战德臣, 程臻, 赵曦滨, 等. 制造服务及其成熟度模型[J]. 计算机集成制造系统, 2012, 18(7):1584-1594.
[19]	江源. 水泥行业两化融合发展现状及工作建议[C]. 中国建材产业转型升级创新发展研究论文集, 2013:199-204.
[20]	建筑材料工业信息中心. 建材行业智能制造水平提升路径及措施研究[EB/OL]. [2021-11-19]. .

层级	L1	L2	L3	L4	L5	L6
4	√			√	√	√
5		√	√	√	√	√
6	√	√	√	√	√	√

层级	L1	L2	L3	L4	L5	L6
4	√			√	√	√
5		√	√	√	√	√
6	√	√	√	√	√	√

层级	L1	L2	L3	L4	L5	L6
	未规划	规划级	规范级	集成级	优化级	引领级
5		√	√	√	√	√
6	√	√	√	√	√	√

层级	L1	L2	L3	L4	L5	L6
	未规划	规划级	规范级	集成级	优化级	引领级
5		√	√	√	√	√
6	√	√	√	√	√	√

一级指标	主观权重 (a_g)	客观权重 (b_g)	综合权重 (w_g)	二级指标
互联互通	0.35	0.37	0.36	智能设备联网	0.40	0.38	0.39
互联互通	0.35	0.37	0.36	信息网络设施	0.60	0.62	0.61
综合集成	0.33	0.30	0.31	纵向集成	0.50	0.34	0.41
				端到端集成	0.18	0.39	0.26
				横向集成	0.32	0.27	0.29
数据分析利用	0.32	0.33	0.32	供应链优化	0.24	0.19	0.21
				生产智能管理	0.32	0.20	0.25
				能耗与安全管理优化	0.16	0.19	0.17
				运营智能决策	0.20	0.19	0.19
				服务化延伸	0.10	0.28	0.17