eesti teaduste
akadeemia kirjastus
SINCE 1952
Proceeding cover
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

Production monitoring system development and modification; pp. 567–580

Full article in PDF format | doi: 10.3176/proc.2015.4S.04

Aleksei Snatkin, Tanel Eiskop, Kristo Karjust, Jüri Majak


Main attention of this paper is paid to the development of a simple, but efficient concept of a real time production monitoring system. The goal is to offer an effective concept, which will help to provide an accurate overview of the shop floor activities by diverse information appearance and improve asset management, machinery utilization, and production process stability. The subtask considered includes description of the design of a visual module for the proposed production monitoring system for a certain type of micro, small and medium sized enterprises.



  1. Jardine, A., Lin, D., and Banjevic, D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process., 2006, 20, 1483–1510.

  2. Cowling, P. and Johansson, M. Using real time informa­tion for effective dynamic scheduling. Eur. J. Oper. Res., 2002, 139, 230–244.

  3. Gu, Y., Lo, A., and Niemegeers, I. A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials, 2009, 11(1), 13–32.

  4. Lanza, G., Stoll, J., Stricker, N., Peters, S., and Lorenz, C. Measuring global production effectiveness. Procedia CIRP, 2013, 7, 31–36.

  5. Saenz de Ugarte, B., Ariba, A., and Pellerin, R. Manu­facturing execution system – a literature review. Prod. Plan. Control, 2009, 20, 525–539.

  6. Meyer, H., Fuchs, F., and Thiel, K. Manufacturing Execu­tion Systems: Optimal Design, Planning, and Deploy­ment. McGraw-Hill, New York, 2009.

  7. Bo, L., Zhenghang, C., and Ying, C. Research on recon­figurable manufacturing execution system. In Proc. 2004 International Conference on Intelligent Mecha­tronics and Automation. Piscataway, N.J., 2004, 157–161.

  8. Ben Khedher, A., Henry, S., and Bouras, A. Integration between MES and product lifecycle management. In Proc. 2011 IEEE 16th Conference on Emerging Technologies & Factory Autom. (Mammeri, Z., ed.). Toulouse, 2011, 1–8.

  9. Snatkin, A., Karjust, K., Majak, J., Aruväli, T., and Eiskop, T. Real time production monitoring system in SME. Estonian J. Eng., 2013, 19, 62–75.

10. Eiskop, T., Snatkin, A., Kõrgesaar, K., and Søren, J. Develop­ment and application of a holistic production monitoring system. In Proc. 9th International Conference of DAAAM Baltic Industrial Engineering (Otto, T., ed.). Tallinn University of Technology, Tallinn, Estonia, 2014, 85–91.

11. Snatkin, A., Eiskop, T., and Kõrgesaar, K. Production monitoring system concept development. In Proc. 9th International Conference of DAAAM Baltic Industrial Engineering (Otto, T., ed.). Tallinn University of Technology, Tallinn, Estonia, 2014, 198–203.

12. Hashem, I. A. T., Yaqoob, I., Anuar, I. N. B., Mokhtar, S., Gani, A., and Khan, S. U. The rise of “big data” on cloud computing: Review and open research issues. Inform. Syst., 2015, 47, 98–115.

13. Heron, M., Hanson, V. L., and Ricketts, I. Open source and accessibility: advantages and limitation. J. Interact. Sci., 2013, 1, 1–10.

14. Wasko, M. M. and Faraj, S. “It is what one does”: why people participate and help others in electronic communities of practice. J. Strategic Informat. Syst., 2000, 9, 155–173.

15. Oakland, S. J. Statistical Process Control (6th ed.). Butter­worth-Heinemann, Oxford, 2008.

16. Hines, J. W. and Usynin, A. Current computational trends in equipment prognostics. Int. J. Comput. Intell. Syst., 2008, 1, 94–102.

17. Camci, F., Eker, O. F., and Jennions, I. K. Major challenges in prognostics: study on benchmarking prognostics datasets, In Proc. First European Con­ference of the Prognostics and Health Management Society 2012 (Bregon, A. and Saxena, A., eds). PHMS, Dresden, Germany, 2012, 148–155.

18. Zhang, Q., Hua, C., and Xu, G. A mixture Weibull proportional hazard model for mechanical system failure prediction utilising lifetime and monitoring data. Mech. Syst. Signal Process., 2014, 43, 103–112.

19. Khemapech, I. Feasibility study of direct communication in wireless sensor networks. Procedia Computer Sci., 2013, 21, 424–429.

20. Kateeb, A. Next-generation wireless sensor networks infrastructure development for monitoring applica­tions. Procedia Computer Sci., 2011, 5, 749–753.

21. Hick, W. E. On the rate of gain of information. Quart. J. Exp. Psychol., 1952, 4, 11–36.

22. Hyman, R. Stimulus information as a determinant of reaction time. J. Exp. Psychol., 1953, 45, 188–196.

23. Fitts, P. M. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol., 1954, 49, 389–391.

24. MacKenzie, I. S. Fitts’ law as a research and design tool in human–computer interaction. Human–Computer Inter­action, 1992, 7, 91–139.

25. Shneiderman, B. and Plaisant, C. Designing the User Interface: Strategies for Effective Human–Computer Interaction (4th ed.). Addison-Wesley, Boston, 2004.

26. Ullman, L. PHP and MySQL for Dynamic Web Sites: Visual QuickPro Guide (4th ed.). Peachipt Press, 2011.


Back to Issue