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Technological and Economic Analysis of Logistic Activities in Interior Parts Manufacturing

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Języki publikacji
EN
Abstrakty
EN
Logistics activities play in important role in supporting the production process for a wide range of products in many industries, such as the automotive or electrical engineering industries. In the automotive industry, they are present throughout the entire production cycle. To perform these activities, various technologies as well as economic aspects must be considered and employed. This contribution describes the technology of interior parts manufacturing for the automotive industry and specifies costs connected to the preparation and setting of machines and devices in individual workplaces as well as the cost items linked with their production and subsequent logistic activities.
Twórcy
autor
  • College of Logistics, Palackého 1381/25, 750 02 Přerov, Czech Republic
  • College of Logistics, Palackého 1381/25, 750 02 Přerov, Czech Republic
  • College of Logistics, Palackého 1381/25, 750 02 Přerov, Czech Republic
  • Technical University of Košice, Faculty of Mining, Ecology, Process Control and Geotechnology, Letná 9, 042 00 Košice, Slovak Republic
Bibliografia
  • 1. Souza Santos P.V. Introduction of technologies in favor of efficiency in logistics: a case study in the service sector. Navus-Revista de Gestao e Tecnologia 3, 2019, 55-68.
  • 2. Arif J. and Jawab F. Outsourcing of logistics’ activities: Impact analysis on logistics service performance. In: 2018 International Colloquium on Logistics and Supply Chain Management (Logistiqua) IUM on Logistics and Supply Chain Management (Logistiqua). pp. 88-92, 2018.
  • 3. Poulimenos D., Bechtsis D., Vlachos D. and Iakovu E. A software tool for optimizing intra-logistic activities. In: 28th European Symposium on Computer Aided Process Engineering. pp. 439-444, 2018.
  • 4. Straka M., Malindzakova M., Trebuna P., Rosova A, Pekarcikova M. and Fill M. Application of ExtendSim for improvement of production logistics’ efficiency. International Journal of Simulation Modelling. 16, 2017, 422-434.
  • 5. Fabianova J., Kacmary P. and Janekova J. Operative production planning utilising quantitative forecasting and Monte Carlo simulations. Open Engineering 9, 2019, 613-622.
  • 6. Sopkuliak P., Palencar R., Palencar J., Suroviak E. and Markovic J. Evaluation of uncertainties of ITS90 by Monte Carlo method. In: Cybernetics and Mathematics Applications in Intelligent Systems, CSOC 2017, vol. 2. pp. 46-56, 2017.
  • 7. Schuhmacher J. and Hummel V. Development of a descriptive model for intralogistics as a foundation for an autonomous control method for intralogistics systems. In: 8th CIRP Sponsored Conference on Learning Factories (CLF 2018) - Advanced Engineering Education & Training for Manufacturing Innovation. pp. 225-230, 2018.
  • 8. Cempirek V., Drahotsky I., Prusa P. and Nachtigal P. Factors Influencing Warehouse Construction in Relation to Safe Operation. In: CLC 2015: Carpathian Logistics Congress - conference proceedings. pp. 171-177, 2016.
  • 9. Kablashova I.V., Rodionova V.N., Turovets O.G. and Lutsenko M.S. Quality management of logistics processes in the context of methodology of the TQM system. In: VISION 2020: Sustainable Economic Development, Innovation Management, and Global Growth, vols I-IX, 2017. pp. 100-1007, 2017.
  • 10. Romero Postiguillo J., Del Campo Yague J. M., Santamera Sanchez J. A. Evolution, trends and design parameters for the management of areas of logistic activities in Spain. Revista de La Construccion. 16, 2017, 274-283.
  • 11. Fallas-Valverde P., Quesada H. J. and Madrigal-Sanchez J. Implementation of lean thinking principals to logistic activities: a case study in a wood forest industry. Tecnologia en Marcha 31, 2018, 52-65.
  • 12. Mendes F.B., Silva C. C. and Tammela I. Operational analysis of a logistic process in the petroleum exploration and production chain integrating ishikawa, bowersox and liker tools in problem identification: A case study. Sistemas & Gestao. 13, 2018, 283-294.
  • 13. Kliment J. and Solc M. The process of identification security risks in the automotive industry. In: Informatics, Geoinformatics and Remote Sensing Conference Proceedings, SGEM 2016, vol. I. pp. 475-482, 2016.
  • 14. Knoll D., Reinhart G. and Prueglmeier M. Enabling value stream mapping for internal logistics using multidimensional process mining. Expert Systems With Applications. 124, 2019, 130-142.
  • 15.Hlatka M., Bartuska L. and Lizbetin J. Application of the Vogel approximation method to reduce transport-logistics processes. In: 18th International Scientific Conference-LOGI 2017. pp. 1-8, 2017.
  • 16.Kampf R., Hlatka, M. and Bartuska L. Optimization of production logistics. Advances in Science and Technology Research Journal. 12, 2018, 151-156.
  • 17. Tomkova E., Husakova, N. and Strohmandl Possibilities of economic efficiency solution in road haulage. In: Transport Means 2015. pp. 694-697, 2015.
  • 18. Čujan Z. Logistika výrobních technologií. VSLG Přerov o.p.s., Přerov, Czech republic, 2013.
  • 19. Košturiak J. and Frolik Z. Štíhlý a inovativní podnik. Alfa Publishing, Praha, Česká republika, 2006.
  • 20. Rother M., Shook J., Womack J. and Jones D. Learning to See: Value Stream Mapping to Add Value and Eliminate MUDA. Industrial Engineering and Management Press, Lean Enterprise Institute, Inc., 2009.
  • 21. Quarterman L. and Snyder B. Value Stream & Process Mapping. Productivity Press, Bellingham, USA, 2017.
  • 22. Mašín I. Mapování hodnotového toku ve výrobních procesech. Institut průmyslového inženýrství, Liberec, Czech republic, 2003.
  • 23. https://www.systemonline.cz/rizeni-vyroby/kanban-vyroba-tahem.htm.
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  • 25. https://managementmania.com/cs/value-streammapping.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d4e223ee-63cc-4055-a2fb-eccabbd3701a
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