Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/85894
Author(s): João Carlos Dias Correia Pinto
Title: Performance Management Analytics for the Automotive Industry: An Empirical Study
Issue Date: 2016-07-19
Description: The automotive industry is permanently collecting data about the business that is then used to calculate high level Key Performance Indicators (KPI) that are organized on a Performance Management System (PMS) to help on the decision making process. However, these KPIs represent uniquely the past. The business, in order to be proactive "i.e." create the future, must predict how it will be. Using Data Mining, Machine Learning, Statistics and Artificial Intelligence tis project creates and compares predictive models to acquire future values for KPIs.
Subject: Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
TID identifier: 201306883
URI: https://repositorio-aberto.up.pt/handle/10216/85894
Document Type: Dissertação
Rights: openAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Dissertação

Files in This Item:
File Description SizeFormat 
153169.pdfPerformance Management Analytics for the Automotive Industry: An Empirical Study1.57 MBAdobe PDFThumbnail
View/Open


This item is licensed under a Creative Commons License Creative Commons