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 |
| DOI: | 10.34626/cdwq-pa42 |
| TID identifier: | 201306883 |
| URI: | https://hdl.handle.net/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 | Size | Format | |
|---|---|---|---|---|
| 153169.pdf | Performance Management Analytics for the Automotive Industry: An Empirical Study | 1.57 MB | Adobe PDF |  View/Open |
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