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Tytuł artykułu

Basics of the management system for transformations of production planning and control processes

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Języki publikacji
EN
Abstrakty
EN
Intensive development of technologies, in particular of the spinotronics, nanotechnology, robotics, and ICT, shapes the new generation production systems. Their distinctive feature is the flexibility associated with production intelligence. It is required from all processes, including the production planning and control processes (PPCP). The article is of conceptual character. It was prepared based on literature research. It encompasses issues related to Next Generation Manufacturing Systems, including Intelligent Manufacturing Systems in particular, covering solutions for PPCP. The article was also built upon the results of research on the level of automation of Polish manufacturing enterprises. The research results in the development of general assumptions of an informatic management system for the transformation of PPCP for technologically advanced and organizational production systems. The system has a modular structure resulting from its functions, which include identifying the need for transformation, its goals and vision, planning, design, implementation and evaluation of transformation. It takes the technical, organizational, socio-psychological and economic aspects of transformation into account. Choosing the right solutions for PPCP purposes allows for flexible adaptation to the requirements and needs of the environment. Its essential part is the knowledge database, thanks to which it is possible to shape the system’s intelligence.
Rocznik
Strony
317--328
Opis fizyczny
Bibliogr. 19 poz., tab.
Twórcy
  • Faculty of Management and Economics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
Bibliografia
  • 1. Badania stopnia automatyzacji firm w Polsce. Astor (2016), [accesed: 15.04.2018].
  • 2. Badurek J. (2015), Przedsiębiorstwo informacyjne, Systemy produkcyjne nowej generacji, Wydawnictwo Politechniki Gdańskiej, Gdańsk.
  • 3. Barbosa J., Leitao P., Adam E. & Trenstesaux D. (2015), Dynamic self-organization in holonic multi-agent manufacturing systems: the ADACOR evolution, Computer in Industry, Vol. 66, pp. 99-111.
  • 4. Chan F.T.S. (2002), Design of material handling equipment selection system: an integration of expert system with analytic hierarchy process approach, Integrated Manufacturing System, Vol. 13, No. 1, pp. 58-68.
  • 5. Filo G. (2011), Zastosowanie algorytmów mrówkowych w rozwiązywaniu problemu szeregowania zadań, Mechanika, Wydawnictwo Politechniki Krakowskiej, pp. 135-140.
  • 6. Goldratt E.M. & Cox J. (2000), Cel. Doskonałość w produkcji, Werbel, Warszawa.
  • 7. Gudanowska A.E. (2017), Transformation towards Industry 4.0 - identification of research trends and aspect of necessary competences in the light of selected publications, Research in Logistics & Production, Vol. 7, No. 5, pp. 431-441.
  • 8. Ibrahimov M., Mohais A., Schellenberg S. & Michalewicz Z. (2012), Evolutionary approaches for supply chain optimisation. Part II: multi-silo supply chains, International Journal of Intelligent Computing and Cybernetics, Vol. 5, Iss 4, pp. 473-499.
  • 9. IMS (2005), Intelligent Manufacturing System. Impact Report (Full Report), History and achievements of Phase I. Gaithersburg: IMS, U.S. Department of Commerce, National Institute of Standards and Technology.
  • 10. Kalayci C.B. & Gupta S.M. (2013), Ant colony optimization for sequence-dependent disassembly line balancing problem, Journal of Manufacturing Technology Management, Vol. 24, Iss. 3, pp. 413-427.
  • 11. Koren Y., Wang W. & Gu X. (2017), Value creation through design for scalability of reconfigurable manufacturing systems, International Journal of Production Research, Vol. 55, Iss. 5, pp. 1227-1242.
  • 12. Łopatowska J. (2012), Identification of advanced manufacturing systems change factors - methodical aspects, [in:] Ł. Hadaś (ed.), Production management - contemporary approaches - selected aspects, Publishing House of Poznan University of Technology, Poznań, pp. 7-21.
  • 13. Metaxiotis K. & Psarras J. (2003), Expert systems in business: applications and future directions for the operations research, International Management & Data Systems, Vol. 103, Iss. 5, pp. 361-368.
  • 14. Szeliga M. (2017), Data Science i uczenie maszynowe, Wydawnictwo Naukowe PWN, Warszawa.
  • 15. Wang L. & Shih H. (2011), An advanced overlapping production planning model in manufacturing supply chain, Journal of Manufacturing Technology Management Vol. 22, No. 7, pp. 870-890.
  • 16. Zawadzka L., Badurek J. & Łopatowska J. (2012a), Inteligentne systemy produkcyjne. Algorytmy, koncepcje, zastosowania, Wydawnictwo Politechniki Gdańskiej, Gdańsk.
  • 17. Zawadzka L., Badurek J. & Łopatowska J. (2012b), Systemy produkcyjne nowej generacji. Modele interdyscyplinarne, Wydawnictwo Politechniki Gdańskiej, Gdańsk.
  • 18. Zheng P., Wang H., Sang Z., Zhong R.Y., Liu Y., Liu Ch., Mubarok K., Yu S. & Xu X. (2018), Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives, Frontiers of Mechanical Engineering, Vol. 13, Iss. 2, pp. 137-150.
  • 19. Zhong R.Y., Xu X., Klotz E. & Newman S.T. (2017), Intelligent Manufacturing in the Context of Industry 4.0: A Review, Engineering, 3, pp. 616-630.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b1e5c27b-c570-4401-a1bb-0ca427af2e50
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