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Computer-aided material demand planning using ERP systems and business intelligence technology

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in small and medium enterprises.
Rocznik
Strony
42--55
Opis fizyczny
Bibliogr. 26 poz., fig.
Twórcy
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation, Nadbystrzycka 36, 20-618 Lublin, Poland
  • Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation, Nadbystrzycka 36, 20-618 Lublin, Poland
Bibliografia
  • [1] Alsoub, R.K., Alrawashdeh, T.A., & Althunibat, A. (2018). User acceptance for Enterprise Resource Planning Software Systems. International Journal of Innovative Computing Information and Control, 14(1), 297–307. http://doi.org/10.24507/ ijicic.14.01.297
  • [2] Aremu, A.Y., Shahzad, A., & Hassan, S. (2019). The Empirical Evidence of Enterprise Resource Planning System Adoption and Implementation on Firm’s Performance Among Medium-sized Enterprises. Global Business Review, UNSP 0972150919849751. http://doi.org/10.1177/0972150919849751
  • [3] Bocewicz, G., Nielsen, I., & Banaszak, Z. (2016). Production Flows Scheduling Subject to Fuzzy Processing Time Constraints. International Journal of Computer Integrated Manufacturing, 29(10), 1105–1127. http://doi.org/10.1080/0951192X.2016.1145739
  • [4] Chang, Y.W. (2020). What drives organizations to switch to cloud ERP systems? The impacts of enablers and inhibitors. Journal of Enterprise Information Management, 33(3), 600–626. http://doi.org/10.1108/JEIM-06-2019-0148
  • [5] Cieśla, B., & Gunia, G. (2019). Development of integrated management information systems in the context of Industry 4.0. Applied Computer Science, 15(4), 37–48. http://doi.org/10.23743/acs-2019-28
  • [6] Danilczuk, W. (2019). Analiza danych produkcyjnych na podstawie transakcji w systemie ERP z wykorzystaniem technologii Business Intelligence. Autobusy – Technika, Eksploatacja, Systemy transportowe, 232(7/8), 62–65. http://doi.org/10.24136/ attest.2019.192
  • [7] De Oliveira, A., & De Almeida, J.R. (2019). Business Intelligence Application for Multidimensional Analysis Risks in Complex Projects. IT Professional, 21(6), 33–39. http://doi.org/10.1109/MITP.2018.2876931
  • [8] Djiroun, R., Boukhalfa, K., & Alimazighi, Z. (2019). Designing data cubes in OLAP systems: a decision makers’ requirements-based approach. Cluster Computing – The Journal of Networks Software Tools and Applications, 22(3), 783–803. http://doi.org/10.1007/s10586-018-2883-7
  • [9] George, A., Schmitz, K., & Storey, V.C. (2020). A Framework for Building Mature Business Intelligence and Analytics in Organizations. Journal of Database Management, 31(3), 14-39. http://doi.org/10.4018/JDM.2020070102
  • [10] Gola, A. (2014). Economic aspects of manufacturing systems design. Actual Problems of Economics, 156(6), 205–212.
  • [11] GUS (2020, August 7). Wykorzystanie technologii informacyjno-komunikacyjnych w jednostkach administracji publicznej, przedsiębiorstwach i gospodarstwach domowych w 2019 roku. Retrieved from https://stat.gov.pl/obszary-tematyczne/nauka-i-technika-spoleczenstwo-informacyjne/spoleczenstwo-informacyjne/wykorzystanie-technologii-informacyjno-komunikacyjnych-w-jednostkach-administracji-publicznej-przedsiebiorstwach-i-gospodarstwach-domowych-w-2019-roku,3,18.html
  • [12] Huang, S.Y., Chiu, A.A., Chao, P.C., & Arniati, A. (2019). Critical Success Factors in Implementing Enterprise Resource Planning Systems for Sustainable Corporations. Sustainability, 11(23), 6785. http://doi.org/10.3390/su11236785
  • [13] Januszewski, A. (2008). Funkcjonalność informatycznych systemów zarządzania: Tom 1 Zintegrowane systemy transakcyjne. Wydawnictwo Naukowe PWN.
  • [14] Loudcher, S., Jakawat, W., Soriano Morales, E.P., & Favre, C. (2015). Combining OLAP and information networks for bibliographic data analysis: a survey. Scientometrics, 103, 471–487. http://doi.org/10.1007/s11192-015-1539-0
  • [15] Meilin, W., Xiangwei, Z., & Qingyun, D. (2010). An Integration Methodology Based on SOA to Enable Real-Time Closed-Loop MRP between MES and ERP. 2010 International Conference on Computing, Control and Industrial Engineering, 1, 101–105. http://doi.org/10.1109/CCIE.2010.33
  • [16] Patalas-Maliszewska, J. (2012). Assessing the Impact of ERP Implementation in the Small Enterprises. Foundations of Management, 4(2), 51–62. http://doi.org/10.2478/fman-2013-0010
  • [17] Queiroz-Sousa, P.O., & Salgado, A.C. (2020). A review on OLAP Technologies Applied to Information Networks. ACM Transactions on Knowledge Discovery from Data, 14(1), 8. http://doi.org/10.1145/3370912
  • [18] Rodriguez, R., Molina-Castillo, F.J., & Svensson, G. (2020). Enterprise resource planning and business model innovation: process, evolution and outcome. European Journal of Innovation Management, 23(4), 728–752. http://doi.org/10.1108/IJIM-04-2019-0092
  • [19] Sobaszek, Ł., Gola, A., & Kozłowski, E. (2018). Job-shop scheduling with machine breakdown prediction under completion time constraint. Annals of Computer Science and Information Systems, 15, 437–440. http://doi.org/10.15439/2018F83
  • [20] Sobaszek, Ł., Gola, A., & Świć, A. (2020). Time-based machine failure prediction in multi-machine manufacturing systems. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 22(1), 52–62. http://doi.org/10.17531/ein.2020.1.7
  • [21] Świć, A., & Gola, A. (2013). Economic analysis of casing parts production in a flexible manufacturing system. Actual Problems of Economics, 141(3), 526–533.
  • [22] Terkaj, W., Tolio, T., & Urgo, M. (2015). A virtual factory approach for in situ simulation to support production and maintenance planning. CIRP Annals Manufacturing Technology, 64(1), 451–454. http://doi.org/10.1016/j.cirp.2015.04.121
  • [23] Vargas, M.A., & Comuzzi, M. (2020). A multi-dimensional model of Enterprise Resource Planning critical successes factors. Enterprise Information Systems, 14(1), 38–57. http://doi.org/10.1080/17517575.2019.1678072
  • [24] Waters, D. (1996). Operations Mangement: Producing Goods and Services. Addison Wesley Longman Limited.
  • [25] Yiu, L.M.D., Yeung, A.C.L., & Jong, A.P.L. (2020). Business intelligence systems and operational capability: an empirical analysis of high-tech sectors. Industrial Management & Data Systems, 120(6), 1195–1215. http://doi.org/10.1108/IMDS-12-2019-0659
  • [26] Zwolińska, B., Grzybowska, K., & Kubica, Ł. (2017). Shaping production change variability in relation to the utilized technology. 24th International Conference on Production Research, ICPR 2017, 155812, 51–56.
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-620c56a3-dd3e-4f1c-a75a-5f02843e8b62
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