Development of an automated industrial dynamics system

Authors

DOI:

https://doi.org/10.15587/2706-5448.2020.217079

Keywords:

industrial dynamics, material flows, financial flows, automated system, activity diagram.

Abstract

The object of research is the process of determining the main indicators of the functioning of a manufacturing enterprise using the method of system dynamics. Any enterprise for the production and sale of products is a complex socio-economic system that is closely related to the external environment through input and output channels. The external environment determines the conditions for the functioning of the enterprise and can be described by a set of a large number of different parameters, the values of which will dynamically change and are fundamentally indeterminate.

Coordination and control over material and financial flows at a manufacturing enterprise is often a separate problem. The interaction of financial resources and material flows, which are selected by the enterprise as the main ones in accordance with market requirements and the specifics of the activity, must be coordinated accordingly to achieve a more efficient operation of the enterprise. Therefore, the task of the presented study is to develop a model of material and financial flows of a production enterprise with its further software implementation. The purpose of the software implementation is to further conduct experiments with the model to determine the main indicators of the production enterprise, depending on changes in the functioning parameters due to the external environment.

All the variety of modeling methods considered in modeling theory can be conditionally divided into two groups: analytical and simulation modeling. To solve the problem of this study, simulation modeling was used, which provides for the construction of a model with characteristics adequate to the original on the basis of a certain information principle.

In the course of the research, a model of material and financial flows of a production enterprise was built. The mathematical model of flows was developed using the system dynamics method by J. Forrester. An automated system was also developed, which is a software implementation of the proposed model.

The automated system of industrial dynamics of a production enterprise developed in the study will significantly increase the efficiency and scientific validity of decisions regarding the management of material and financial resources.

Author Biographies

Ganna Solodovnik, Kharkiv National University of Civil Engineering and Architecture, 40, Sumska str., Kharkiv, Ukraine, 61002

PhD, Associate Professor

Department of Computer Science and Information Technology

Kateryna Kovalenko, Kharkiv National University of Civil Engineering and Architecture, 40, Sumska str., Kharkiv, Ukraine, 61002

Department of Computer Science and Information Technology

References

  1. Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill, 982.
  2. Rassokha, I. M. (2011). Metodolohiia ta orhanizatsiia naukovykh doslidzhen. Kharkiv: KhNAMH, 76.
  3. Miziuk, B. M. (2009). Osnovy stratehichnoho upravlinnia. Lviv: Mahnoliia, 544.
  4. Negulescu, O., Doval, E. (2014). The Quality of Decision Making Process Related to Organizations’ Effectiveness. Procedia Economics and Finance, 15, 858–863. doi: http://doi.org/10.1016/s2212-5671(14)00548-6
  5. Tohidi, H., Jabbari, M. M. (2012). Decision role in management to increase effectiveness of an organization. Procedia – Social and Behavioral Sciences, 31, 825–828. doi: http://doi.org/10.1016/j.sbspro.2011.12.149
  6. Akdere, M. (2011). An analysis of decision-making process in organizations: Implications for quality management and systematic practice. Total Quality Management & Business Excellence, 22 (12), 1317–1330. doi: http://doi.org/10.1080/14783363.2011.625180
  7. Bolle-Reddat, B., Dumora, R. (2016). The Role of Models in Management Decision. Modelling in Life Insurance – A Management Perspective, 7, 130–141. doi: http://doi.org/10.1007/978-3-319-29776-7_11
  8. Winter, S. G., Kaniovski, Y. M., Dosi, G. (2000). Modeling industrial dynamics with innovative entrants. Structural Change and Economic Dynamics, 11 (3), 255–293. doi: http://doi.org/10.1016/s0954-349x(99)00010-7
  9. Li, D., Liu, L., Zhu, W., Rong, G. (2008). Material-flow Modeling Technology and Its Application in Manufacturing Execution Systems of Petrochemical Industry. Chinese Journal of Chemical Engineering, 16 (1), 71–78. doi: http://doi.org/10.1016/s1004-9541(08)60040-3
  10. Lim, G. C. (1991). Estimating portfolio models from financial flow data: An analysis of the demand for liabilities, real assets and financial assets by the household sector. Economic Modelling, 8, 219–224. doi: http://doi.org/10.1016/0264-9993(91)90016-h
  11. Mody, A., Taylor, M. P., Kim, J. Y. (2001). Modelling fundamentals for forecasting capital flows to emerging markets. International Journal of Finance & Economics, 6 (3), 201–216. doi: http://doi.org/10.1002/ijfe.159
  12. Combes, J.-L., Kinda, T., Ouedraogo, R., Plane, P. (2019). Financial flows and economic growth in developing countries. Economic Modelling, 83, 195–209. doi: http://doi.org/10.1016/j.econmod.2019.02.010
  13. Sumets, О. М., Kyzym, M. О., Syromyаtnikov, P. S., Kozureva, O. V., Tsvirko, О. О. (2019). Financial flows in logistics systems of production enterprises. Financial and Credit Activity: Problems of Theory and Practice, 3 (30), 165–175. doi: http://doi.org/10.18371/fcaptp.v3i30.179529
  14. Ladiges, J., Fulber, A., Arroyo, E., Fay, A., Haubeck, C., Lamersdorf, W. (2015). Learning material flow models for manufacturing plants from data traces. 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). doi: http://doi.org/10.1109/indin.2015.7281750
  15. Suprunenko, O. A. (2013). Paradigms of simulation modeling in studying complex parallel systems. Eastern-European Journal of Enterprise Technologies, 5 (4 (65)), 63–67. Available at: http://journals.uran.ua/eejet/article/view/18353
  16. Sheshasaayee, A., Bhargavi, K. (2017). A study of automated decision making systems. Research Inventy: International Journal of Engineering And Science, 7, 28–31.
  17. Solodovnyk, H. V. (2016). Instrumentalni zasoby modeliuvannia sotsialno-ekonomichnykh system. Kharkiv: Rozhko S. H., 122.
  18. Booch, G., Maksimchuk, R. A., Engle, M. W., Young, B. J., Conallen, J., Houston, K. A. (2007). Object-Oriented Analysis and Design with Applications. Addison-Wesley Professional, 720.
  19. Rosenberg, D., Scott, K. (2007). Use Case Driven Object Modeling with UML: A Practical Approach. Addison-Wesley Professional, 188. doi: http://doi.org/10.1007/978-1-4302-0369-8
  20. Ivanilov, O. S. (2009). Ekonomika pidpryiemstva. Kyiv: Tsentr uchbovoi literatury, 728.

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Published

2020-12-30

How to Cite

Solodovnik, G., & Kovalenko, K. (2020). Development of an automated industrial dynamics system. Technology Audit and Production Reserves, 6(2(56), 6–13. https://doi.org/10.15587/2706-5448.2020.217079

Issue

Vol. 6 No. 2(56) (2020): Information and control systems

Section

Information Technologies: Original Research

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Copyright (c) 2020 Ganna Solodovnik, Kateryna Kovalenko

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