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.
http://dx.doi.org/10.1016/j.ymssp.2005.09.012
2. Cowling, P. and Johansson, M. Using real time information for effective dynamic scheduling. Eur. J. Oper. Res., 2002, 139, 230–244.
http://dx.doi.org/10.1016/S0377-2217(01)00355-1
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.
http://dx.doi.org/10.1109/SURV.2009.090103
4. Lanza, G., Stoll, J., Stricker, N., Peters, S., and Lorenz, C. Measuring global production effectiveness. Procedia CIRP, 2013, 7, 31–36.
http://dx.doi.org/10.1016/j.procir.2013.05.006
5. Saenz de Ugarte, B., Ariba, A., and Pellerin, R. Manufacturing execution system – a literature review. Prod. Plan. Control, 2009, 20, 525–539.
http://dx.doi.org/10.1080/09537280902938613
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.
http://dx.doi.org/10.1109/etfa.2011.6058993
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.
http://dx.doi.org/10.3176/eng.2013.1.06
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.
http://dx.doi.org/10.1016/j.is.2014.07.006
13. Heron, M., Hanson, V. L., and Ricketts, I. Open source and accessibility: advantages and limitation. J. Interact. Sci., 2013, 1, 1–10.
http://dx.doi.org/10.1186/2194-0827-1-2
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.
http://dx.doi.org/10.1016/S0963-8687(00)00045-7
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.
http://dx.doi.org/10.2991/ijcis.2008.1.1.7
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.
http://dx.doi.org/10.1016/j.ymssp.2013.10.013
19. Khemapech, I. Feasibility study of direct communication in wireless sensor networks. Procedia Computer Sci., 2013, 21, 424–429.
http://dx.doi.org/10.1016/j.procs.2013.09.056
20. Kateeb, A. Next-generation wireless sensor networks infrastructure development for monitoring applications. Procedia Computer Sci., 2011, 5, 749–753.
http://dx.doi.org/10.1016/j.procs.2011.07.099
21. Hick, W. E. On the rate of gain of information. Quart. J. Exp. Psychol., 1952, 4, 11–36.
http://dx.doi.org/10.1080/17470215208416600
22. Hyman, R. Stimulus information as a determinant of reaction time. J. Exp. Psychol., 1953, 45, 188–196.
http://dx.doi.org/10.1037/h0056940
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.
http://dx.doi.org/10.1037/h0055392
24. MacKenzie, I. S. Fitts’ law as a research and design tool in human–computer interaction. Human–Computer Interaction, 1992, 7, 91–139.
http://dx.doi.org/10.1207/s15327051hci0701_3
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.
