Testing the Replenishment Model Strategy Using Software Tecnomatix Plant Simulation
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Nowadays, simulation tools are used to create the supply strategy of the company, which enable to create variant solutions in digital form and to find the optimal variant without intervention in real production. In this article, a simulation model of procurement for demand-driven consumption is created using software Tecnomatix Plant Simulation. It deals with the creation of a replenishment strategy simulation model, which is based on the P-Q inventory management model. The fundamental of these models is based either on ordering in a fixed amount, resp. at regular supply intervals. This decision depends on the nature of the product and its demand. Software tool Tecnomatix Plant Simulation allows creating such a model and simulating the behaviour of the system under defined conditions and testing possible variants of the potential solutions.
KeywordsSimulation Model Inventory Replenishment
This article was created by implementation of the grant project VEGA 1/0708/16 Development of a new research methods for simulation, assessment, evaluation and quantification of advanced methods of production, KEGA 030TUKE-4/2017 Implementation of innovative instruments for increasing the quality of higher education in the 5.2.52 Industrial engineering field of study and APVV-17-0258 Digital engineering elements application in innovation and optimization of production flows.
- 1.Edl, M., & Kudrna, J. (2013). Methods of industrial engineering (1st ed.). Plzen, Czech Republic: Smart Motion.Google Scholar
- 2.Lenort, R., Stas, D., & Samolejova, A. (2012). Heuristic algorithm for planning and scheduling of forged pieces heat treatment. Metalurgija, 51, 225–228.Google Scholar
- 3.Dulina, L., Rakyta, M., Sulirova, I., & Šeligova, M. (2017). Improvement of the production system. In Smart City 360°. 2nd EAI international Summit: Revised selected papers. Ghent: EAI.Google Scholar
- 6.Poór, P., Šimon, M., & Karková, M. (2015). CMMS as an effective solution for company maintenance costs reduction. In Production Management and Engineering Sciences (pp. 241–246).Google Scholar
- 8.Laciak, M., & Sofranko, M. (2013). Designing of the technological line in the SCADA system PROMOTIC. In Proceedings of the 2013 14th International Carpathian Control Conference, Rytro, Poland.Google Scholar
- 9.Malindzakova, M., Rosova, A., Baranova, V., & Futo, J. (2015). Modelling of outbursts and ejections occurrences during steel production. Metalurgija, 54, 247–250.Google Scholar
- 10.Saniuk, S., Saniuk, A., Lenort, R., & Samolejova, A. (2014). Formation and planning of virtual production networks in metallurgical clusters. Metalurgija, 53, 725–727.Google Scholar
- 11.Straka, M., Bindzar, P., & Kadukova, A. (2014). Utilization of the multicriteria decision-making methods for the needs of mining industry. Acta Montanistica Slovaca, 19, 199–206.Google Scholar
- 13.Badida, M., Gombar, M., Maslejova, A., Sobotova, L., Kmec, J., & Vagaska, A. (2015). Evaluation of zinc coating quality by statistical methods. Przemysl Chemiczny, 94, 2146–2149.Google Scholar
- 14.Rosova, A., & Malindzakova, M. (2014). Material flow – starting point for recovery of inputs in the production company. In International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, 14th International Multidisciplinary Scientific Geoconference and EXPO, SGEM 2014; Albena; Bulgaria SGEM Vol. 3, no. 5.Google Scholar
- 16.Wicher, P., Staš, D., Karkula, M., Lenort, R., & Besta, P. (2015). A computer simulation-based analysis of supply chains resilience in industrial environment. Metalurgija, 54(4), 703–706.Google Scholar
- 17.Pekarčíková, M., Trebuňa, P., & Markovič, J. (2014). Case study of modelling the logistics chain in production, conference: 6th conference on Modelling of Mechanical and Mechatronic Systems (MMaMS) location: Vysoke Tatry, Slovakia, modelling of mechanical and mechatronic systems book series. Procedia Engineering, 96, 355–361.CrossRefGoogle Scholar
- 19.Gregor, T., Krajčovič, M., & Wiecek, D. (2017). Smart connected logistics. In Procedia Engineering. vol. 192. Transcom 2017 12th International Scientific Conference of Young Scientists on Sustainable, Modern and Safe Transport. High Tatras, Grand Hotel Bellevue, Slovakia (pp. 265–270).Google Scholar
- 20.Trebuňa, P., Fiľo, M., & Pekarčíková, M. (2013). Supply and distribution logistics (1st ed.). Ostrava: Amos.Google Scholar
- 22.Siemens. Process Simulate: Manufacturing process verification in powerful 3D environment. Retrieved 11 April, 2019, from https://www.plm.automation.siemens.com/en/products/tecnomatix/manufacturing-simulation/robotics/process-simulate.shtml#lightview%26url=/en_us/Images/7457_tcm1023-80351.pdf%26title=ProcessSimulate%26description=Process Simulate Fact Sheet%26docType=pdf. (2018).
- 23.Siemens. Tecnomatix. Retrieved 16 April, 2019, from http://www.sova.sk/sk/produkty/tecnomatix. (2017).
- 24.SE Research Consortium, Retrieved 20 April, 2019, from https://www.nasa.gov/consortium/DiscreteEventSimulation