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The role of big data in Industry 4.0 in mining industry in Serbia

100%

Tylečková E. , Noskievičová D.

System Safety : Human - Technical Facility - Environment

2020

tom Vol. 2, nr 1

166--173

The current age characterized by unstoppable progress and rapid development of new technologies and methods such as the Internet of Things, machine learning and artificial intelligence, brings new requirements for enterprise information systems. Information systems ought to be a consistent set of elements that provide a basis for information that could be used in context to obtain knowledge. To generate valid knowledge, information must be based on objective and actual data. Furthermore, due to Industry 4.0 trends such as digitalization and online process monitoring, the amount of data produced is constantly increasing – in this context the term Big Data is used. The aim of this article is to point out the role of Big Data within Industry 4.0. Nevertheless, Big Data could be used in a much wider range of business areas, not just in industry. The term Big Data encompasses issues related to the exponentially growing volume of produced data, their variety and velocity of their origin. These characteristics of Big Data are also associated with possible processing problems. The article also focuses on the issue of ensuring and monitoring the quality of data. Reliable information cannot be inferred from poor quality data and the knowledge gained from such information is inaccurate. The expected results do not appear in such a case and the ultimate consequence may be a loss of confidence in the information system used. On the contrary, it could be assumed that the acquisition, storage and use of Big Data in the future will become a key factor to maintaining competitiveness, business growth and further innovations. Thus, the organizations that will systematically use Big Data in their decision-making process and planning strategies will have a competitive advantage.

Proposal of methodology for practical application of nonparametric control charts

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Noskievičová D. , Smajdorová T.

tom Vol. 1, Iss. 1

464--471

This paper deals with the methodology for practical application of nonparametric control charts. This topic is very important for two reasons: firstly nonparametric control charts are very effective instruments for the realization of the statistical process monitoring phase I due to their robustness against various deviations from the data assumptions that must be met when applying model-based control charts. Secondly nonparametric control charts have very weak SW support and also they are not taught in the frame of training courses not even of the university study programmes. For that reason the practitioners do not know them and do not use them. The paper offers the proposal how to practically apply these control charts which is based on the complex simulation study of various nonparametric control charts performance when various data assumptions have not been met. The study has covered these nonparametric control charts: Shewhart sign control chart, nonparametric EWMA and nonparametric CUSUM control charts, nonparametric progressive mean control chart, control chart based on Mood statistics and robust median absolute deviation control chart. All charts have been studied in condition of not normally distributed data, autocorrelated data and data with nonconstant distribution parameters. The simulations were realized for statistically stable (IC – in control) and also statistically unstable (OC – out of control) processes. For the evaluation of the control charts performance median run length, 0.05-quantile, and 0.95-quantile were used.