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Framtidens ERP system - Implementering av affärssystem: Förbättrad passform genom maskininlärning?
2021 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
The future of ERP systems - Implementation of ERP systems : An improved fit through machine learning? (English)
Abstract [sv]

ERP leverantörer arbetar ständigt med innovation för att vara konkurrenskraftiga. För att ständigt hålla sig konkurrenskraftiga måste företag arbeta med utveckling av ERP system, Machine Learning, big data och analytics. Att använda dessa tekniker i en kombination hjälper företag att kunna utveckla automatiserade funktioner för kunder. Denna studie är genomförd utifrån en kvalitativ ansats där vi analyserar två olika företag som levererar ERP system i olika branscher. Fokuset kommer att ligga på fenomenen maskininlärning, analytics och ERP system. I denna studie skapas en förståelse för viktiga begrepp och hur de fungerar tillsammans. Men även hur ERP leverantörer arbetar med ERP system, maskininlärning och analytics för att sedan kunna se om det är möjligt att anpassa systemets passform med hjälp av maskininlärning. Studien visar att det finns hinder som företag måste hantera när de arbetar datadrivet. 

Abstract [en]

ERP suppliers are constantly working to be more innovative and to be more competitive. To be able to constantly stay competitive, companies must keep on working with development of ERP systems, Machine Learning, Big Data and Analytics. To use these techniques in combination with each other helps organisations to keep on developing automated functions for their customers. This study was conducted on the basis of a qualitative approach where we analyze two different organisations that deliver ERP systems to different industries. The focus in this study is applied to the concepts of Machine Learning, Analytics and ERP systems. This study also creates an understanding of these important concepts and how they work together. But also how ERP suppliers work with ERP systems, Machine Learning and analytics to be able to see if it is possible to create a better fit for the system with Machine Learning. This study also shows that there are obstacles that organisations must deal with when they work data-driven. This study is written in Swedish.   

Place, publisher, year, edition, pages
2021.
Keywords [en]
Machine learning, ERP, Trace data, Analytics, Big data
Keywords [sv]
Machine learning, ERP, Trace data, Analytics, Big data
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hb:diva-26766OAI: oai:DiVA.org:hb-26766DiVA, id: diva2:1604545
Subject / course
Informatics
Available from: 2021-10-22 Created: 2021-10-20 Last updated: 2021-10-22Bibliographically approved

Open Access in DiVA

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File information
File name FULLTEXT01.pdfFile size 580 kBChecksum SHA-512
804611fd9331b1f2597eb80b3d008fd4158f84d11e5e6f644396bcf6a9131ce2cb8cda76affd83134d3de7965dbf0691f49d9c61d462f76932f57a4780fe95b8
Type fulltextMimetype application/pdf

Information Systems

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CiteExportLink to record
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Citation style
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