Evaluation of the Workplace in Order to Reduce Waste in Material Flow

  • Daniela OnofrejovaEmail author
  • Jaroslava Kadarova
  • Dusan Simsik
  • Jaroslava Janekova
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Value flow management, quality in the logistics chain without unnecessary waste, optimization of transport, and standardization of logistics processes are considered as significant characteristics of lean logistics. This article focuses on the analysis and evaluation of existing material flow at the manufacturing operation. The assessment is based on comparing the total distance traveled during the transport of material for certain time consumption. And due to financial indicators, the costs necessary for the operation of the production workplace are monitored. In the efficient operation of the production system, efforts are made to minimize traffic routes, thereby reducing transport costs as well as the need for means of transport.


Value flow management Lean logistics Material flow efficiency Material flow calculation Industry 4.0 



This work has been supported by the Slovak Grant Agency KEGA 026TUKE-4/2017 Implementation of innovative educational approaches and tools to enhance the development of the core competencies graduate study program Industrial Engineering.


  1. 1.
    Kosturiak, J., & Frolik, Z. (2006). Lean and innovative enterprise (1st ed.). Praha: Alfa Publishing, s.r.o.. (in Czech).Google Scholar
  2. 2.
    Womack, J. P., & Jones, D. T. (2003). Lean thinking banish waste and create wealth in your corporation (2nd ed.). New York: Free Press, Simon & Schuster, Inc..Google Scholar
  3. 3.
    Liker, J. K. (2004). The Toyota way. 14 management principles from the world greatest manufacturer (1st ed.). New York: McGraw-Hill.Google Scholar
  4. 4.
    Bauerhansl, T., Hompel, M., & Vogel-Heuser, B. (2014). Industrie 4.0 in Produktion, Automatisierung und Logistik (1st ed.). Springer Vieweg, Springer Fachmedien Wiesbaden, Heidelberg.Google Scholar
  5. 5.
    Onofrejova, D., & Janekova, J. (2016). Design concept of digital production system. Transfer of Innovations, 20(33), 169–173.Google Scholar
  6. 6. What is the smart factory and its impact on manufacturing, Accessed 02 Mar 2018.
  7. 7.
    Fei, T., Jiangfeng, C. H., Qinglin, Q., Meng, Z., He, Z., & Fangyuan, S. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576.Google Scholar
  8. 8.
    Simsik, D., et al. (2017). Identification, modelling and simulation of systems (1st ed.). Kosice: Edition of educational literature. (in Slovak).Google Scholar
  9. 9.
    Onofrejova, D., Onofrej, P., & Simsik, D. (2014). Model of production environment controlled with intelligent systems. In Procedia engineering: Modelling of mechanical and mechatronic systems MMaMS 2014: 25th–27th November 2014, High Tatras, Slovakia. No. 96 (pp. 330–337).Google Scholar
  10. 10.
    Onofrejova, D., & Simsik, D. (2019). Change in concept from conventional to digital factory of the future. MM Science Journal, 12(5), 3453--3457.Google Scholar
  11. 11.
    Synergycad: Feige filling: Autodesk factory design suit increases competitive edge, Accessed 05 Apr 2018.
  12. 12.
    Panda, A., Jurko, J., & Pandova, I. (2016). Monitoring and evaluation of production processes an analysis of the automotive industry (1st ed.). Switzerland: Springer International Publishing.Google Scholar
  13. 13.
    Straka, M., et al. (2017). Application of EXTENDSIM for improvement of production logistics’ efficiency. International Journal of Simulation Modelling, 16(3), 422–434.CrossRefGoogle Scholar
  14. 14.
    Onofrejova, D., & Janekova, J. (2017). Critical drivers that influence an operation performance. Studia i Materialy, 6(37), 48–52.Google Scholar
  15. 15.
    Fabianova, J., Janekova, J., & Onofrejova, D. (2017). Cost analysis of poor quality using a software simulation. Amfiteatru Economic, 19(44), 181–196.Google Scholar
  16. 16.
    Janekova, J., et al. (2018). Product mix optimization based on Monte Carlo simulation: A case study. International Journal of Simulation Modelling, 17(2), 295–307.CrossRefGoogle Scholar
  17. 17.
    Trebuna, P., et al. (2017). Economic evaluation of investment project in the area of sheet metal processing. Metalurgija, 2(56), 245–248.Google Scholar
  18. 18.
    Vagas, M., et al. (2018). Safety as necessary aspect of automated systems. In ICETA 2018: Proceedings: 16th IEEE International Conference on Emerging eLearning Technologies and Applications (pp. 617–622). New Jersey: Institute of Electrical and Electronics Engineers.CrossRefGoogle Scholar
  19. 19.
    Vagas, M., Simsik, D., & Onofrejova, D. (2019). Factors for successfully implementation of automated solutions based on industry 4.0. In ARTEP 2019 Automatization and control in theory and praxis: 13. Annual conference of researchers from universities, educational high schools and companies (pp. 1–8). Kosice: Technical University of Kosice.Google Scholar
  20. 20.
    Pachnikova, L., Janos, R., & Sidlovska, L. (2013). Manufacturing systems suitable for globalized market. Applied Mechanics and Materials., 282, 230–234.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Daniela Onofrejova
    • 1
    Email author
  • Jaroslava Kadarova
    • 1
  • Dusan Simsik
    • 1
  • Jaroslava Janekova
    • 1
  1. 1.Faculty of Mechanical EngineeringTechnical University of KosiceKosiceSlovakia

Personalised recommendations