Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/76701
Title: Using business intelligence and machine learning on ERP data to improve business performance
Authors: Scicluna, Jesmar (2020)
Keywords: Business intelligence
Enterprise resource planning
Machine learning
Issue Date: 2020
Citation: Scicluna, J. (2020). Using business intelligence and machine learning on ERP data to improve business performance (Bachelor's dissertation).
Abstract: Nowadays, companies are becoming rich in data. Therefore, businesses should analyse their data to capture and create value from it. In other words, the need to analyse data is paramount, given that businesses can monitor and improve their performance by turning their data to information, and eventually create predictions through machine learning. This study aimed to showcase this by making use of Business Intelligence (BI) through data visualisation, and by creating predictions through a machine learning model in order to enable companies to improve their business performance by making more informed decisions with the use of these technological tools. This study therefore assessed the usability and value that such technologies can deliver to companies looking to make better use of their data. To this end, a company was engaged to participate in the study by providing its Enterprise Resource Planning (ERP) data related to its sales. Various visuals/dashboards were built, based on stakeholder input within the company and data related to sales and sale opportunities. Moreover, a supervised multi-class classification machine learning model was built, based on the sale opportunities dataset which was obtained from the company’s Customer Relationship Management (CRM) system to predict the class label for each sale opportunity. The visualisations and the machine learning model built were subsequently submitted for evaluation to identify the potential value of such technologies. Various employees, working at different levels within the company, were involved during the evaluation process in order to gather feedback and assess usability of the artefacts developed. As a result of the evaluation process, these employees suggested also extending BI and machine learning to areas other than sales. Given that positive feedback and good usability scores were achieved, visualisation and prediction were classified as effective and important tools to enhance a company’s business performance since these ultimately help a company to improve its decision-making process by having access to information at the right time.
Description: B.Sc. IT (Hons)(Melit.)
URI: https://www.um.edu.mt/library/oar/handle/123456789/76701
Appears in Collections:Dissertations - FacICT - 2020
Dissertations - FacICTCIS - 2020

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