COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY

Wojciech  DANILCZUK; Arkadiusz GOLA

doi:10.23743/acs-2020-20

COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY

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Published: Sep 30, 2020

DOI: https://doi.org/10.23743/acs-2020-20

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DOI

https://doi.org/10.23743/acs-2020-20

Authors

Wojciech DANILCZUK

danilczuk.wojciech@gmail.com

Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation, Nadbystrzycka 36, 20-618 Lublin

Arkadiusz GOLA

a.gola@pollub.pl

Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Computerisation and Robotisation, Nadbystrzycka 36, 20-618 Lublin

Abstract

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.

Keywords:

Decision Support Systems, material requirements planning, ERP, business intelligence

References

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

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 DOI: https://doi.org/10.1177/0972150919849751

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 DOI: https://doi.org/10.1080/0951192X.2016.1145739

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 DOI: https://doi.org/10.1108/JEIM-06-2019-0148

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

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

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 DOI: https://doi.org/10.1109/MITP.2018.2876931

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 DOI: https://doi.org/10.1007/s10586-018-2883-7

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 DOI: https://doi.org/10.4018/JDM.2020070102

Gola, A. (2014). Economic aspects of manufacturing systems design. Actual Problems of Economics, 156(6), 205–212.

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/naukai-technika-spoleczenstwo-informacyjne/spoleczenstwo-informacyjne/wykorzystanietechnologii-informacyjno-komunikacyjnych-w-jednostkach-administracjipublicznej-przedsiebiorstwach-i-gospodarstwach-domowych-w-2019-roku,3,18.html

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 DOI: https://doi.org/10.3390/su11236785

Januszewski, A. (2008). Funkcjonalność informatycznych systemów zarządzania: Tom 1 Zintegrowane systemy transakcyjne. Wydawnictwo Naukowe PWN.

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 DOI: https://doi.org/10.1007/s11192-015-1539-0

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 DOI: https://doi.org/10.1109/CCIE.2010.33

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/fman2013-0010 DOI: https://doi.org/10.2478/fman-2013-0010

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 DOI: https://doi.org/10.1145/3370912

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 DOI: https://doi.org/10.1108/EJIM-04-2019-0092

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 DOI: https://doi.org/10.15439/2018F83

Sobaszek, Ł., Gola, A., & Świć, A. (2020). Time-based machine failure prediction in multimachine manufacturing systems. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 22(1), 52–62. http://doi.org/10.17531/ein.2020.1.7 DOI: https://doi.org/10.17531/ein.2020.1.7

Świć, A., & Gola, A. (2013). Economic analysis of casing parts production in a flexible manufacturing system. Actual Problems of Economics, 141(3), 526–533.

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 DOI: https://doi.org/10.1016/j.cirp.2015.04.121

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 DOI: https://doi.org/10.1080/17517575.2019.1678072

Waters, D. (1996). Operations Mangement: Producing Goods and Services. Addison Wesley Longman Limited.

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/IMDS12-2019-0659 DOI: https://doi.org/10.1108/IMDS-12-2019-0659

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.

Article Details

DANILCZUK, W. ., & GOLA, A. (2020). COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY. Applied Computer Science, 16(3), 42–55. https://doi.org/10.23743/acs-2020-20

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