Self-Service Business Intelligence

• Autor(a) principal: Eduardo Cardoso de Abreu
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/89927
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

The production management systems in industrial companies produce large amounts of data, but it is not always easy to take the necessary indicators for decision making. Solving this issue is the purpose of analytical processing tools (Business Intelligence), that extract relevant information from the operating systems and produce the reports requested by the managers, but frequently using the help of experts. The need to have an IT department as an intermediary generates delays, reducing the company's decision-making capacity, so there was a need to create tools of Self-Service Business Intelligence (SSBI). These allow employees without IT knowledge to interrogate the database, obtaining the information they desire immediately, in addition to being able to better explore the information stored. Critical Manufacturing (CMF), the company where this dissertation was made, is a software development company with general BI and SSBI solutions for its own production management system (MES) cmNAVIGO. However, there are many companies without the funds to purchase these solutions, due to the considerable costs in licensing and infrastructure, but nevertheless wish to obtain some of their benefits. The aim of this work is to integrate one SSBI prototype tool into cmNAVIGO, allowing non-experts to interrogate the data and produce dashboards through a graphical interface. To do this, some tools SSBI and dashboard builders were selected and evaluated on their approaches, features, advantages and disadvantages, to determine the best path for the project. With the tool chosen, different approaches to the implementation of a simplified data model were studied, allowing the end user to interact with the data he considers most relevant.