Data Analysis of Readiness Programs of Machine-Building Enterprises

Advances in Design, Simulation and Manufacturing III: Proceedings of the 3rd International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2020. Vol.1: Manufacturing and Materials Engineering 2020
Bohdan Haidabrus, S. Protsenko, P. Rosenberger, Jānis Grabis

One of the important aspects of providing the high level of the enterprises IT-readiness at machine-building enterprises is using the data science approach. By using data analysis, we mean the ability of the enterprises’ experts to increase using data by the most effective appliance of modern data science algorithms. In our research, the analysis has been carried out and the proposed approach can be used in real practice to evaluate the implementation of program projects to boost IT readiness. For example, at the initial stage, when the project is just starting and we do not know the real values of features, we can assign to modifications the average value for each, and then when the project arrives the real value of modification, we can calculate the target and track the dynamics of the assessment, the quality of the program projects to boost IT-readiness of machine-building enterprises. Thus, an important question is the compliance of the enterprise to the necessary level of IT-readiness which is directly connected with data analysis.

Keywords
Data science, IT-readiness, Machine learning, Program management, Project management
DOI
10.1007/978-3-030-50794-7_13
Hyperlink
https://link.springer.com/chapter/10.1007%2F978-3-030-50794-7_13

Haidabrus, B., Protsenko, S., Rosenberger, P., Grabis, J. Data Analysis of Readiness Programs of Machine-Building Enterprises. In: Advances in Design, Simulation and Manufacturing III: Proceedings of the 3rd International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2020. Vol.1: Manufacturing and Materials Engineering, Ukraine, Kharkiv, 9-12 June, 2020. Singapore: Springer Nature Switzerland AG, 2020, pp.128-136. ISBN 978-3-030-50793-0. e-ISBN 978-3-030-50794-7. ISSN 2195-4356. e-ISSN 2195-4364. Available from: doi:10.1007/978-3-030-50794-7_13

Publication language
English (en)