Fuzzy cognitive mapping approach to the assessment of industry 4.0 tendency

被引：0

作者：

Kiraz A. ^{[1
]}

Uygun Ö. ^{[1
]}

Erkan E.F. ^{[1
]}

Canpolat O. ^{[1
]}

机构：

[1] Department of Industrial Engineering, Faculty of Engineering, Sakarya University, Sakarya

来源：

Scientia Iranica | 2020年 / 27卷 / 5 E期

关键词：

Fuzzy cognitive maps; Industry; 4.0; Strategic management;

D O I：

10.24200/SCI.2019.51200.2057

中图分类号：

学科分类号：

摘要：

Proper understanding of the conceptual and practical counterparts of Industry 4.0 is of great importance as global competition has made the technology-based production a necessity. The aim of the present study was to propose a model that would predict the existing and future Industry 4.0 levels for companies. The changes of the concepts were examined and interpreted for three di erent hypothetically developed scenarios. In the rst scenario, an organization that was poorly managed in terms of the development of Industry 4.0 was considered. The Industry 4.0 tendency was calculated at 0.04, reaching a steady state after 12 time periods using the Fuzzy Cognitive Maps (FCMs) algorithm. Moderate and well managed organizations were considered in Scenarios 2 and 3, respectively. The Industry 4.0 tendency reached 0.12 after 15 time periods in Scenario 2 and 0.95 at the end of ve iterations in the third scenario with the concept values indicating well managed situation in the latter case. In addition, strategy and organization, smart operation, and smart factory concepts were found to make the most signi cant contribution to the Industry 4.0 level in the static analysis. © 2020 Sharif University of Technology. All rights reserved.

引用

页码：2635 / 2643

页数：8

共 39 条

[1]

Lasi H., Fettke P., Kemper H.-G., Feld T., Et al., Industry 4.0, Bus. Inf. Syst. Eng, 6, 3, pp. 239-242, (2014)

[2]

Shrouf F., Ordieres J., Miragliotta G., Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm, IEEE International Conference on Industrial Engineering and Engineering Management, pp. 697-701, (2014)

[3]

Lichtblau G., Stich V., Bertenrath R., Blum M., Et al., IMPULS-Industrie 4.0 - Readiness, Impuls-Stift. VDMA, (2015)

[4]

Lanza G., Nyhuis P., Majid A.S., Kuprat T., Et al., Empowerment and implementation strategies for Industry 4.0, Technische Informationsbibliothek, 111, 1-2, pp. 76-79, (2016)

[5]

The Industry 4.0: Digital operations self assessment, (2016)

[6]

The connected enterprise maturity model, (2014)

[7]

Intelligent production for institution, (2015)

[8]

Schumacher A., Erol S., Sihn W., A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises, Procedia CIRP, 52, pp. 161-166, (2016)

[9]

Industry 4.0: Building the digital enterprise, (2016)

[10]

Rowhanimanesh A., Akbarzadeh-T M.R., Perception-based heuristic granular search: Exploiting uncertainty for analysis of certain functions, Sci. Iran, 18, 3, pp. 617-626, (2011)

← 1 2 3 4 →