Data Digitization in Manufacturing Factory Using Palantir Foundry Solution
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| Publicado en: | Processes vol. 12, no. 12 (2024), p. 2816 |
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| Autor Principal: | |
| Outros autores: | , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20240101 |b d20241231 | |
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| 100 | 1 | |a Krajný, Peter |u Institute of Industrial Engineering, Management and Applied Mathematics, Technical University of Kosice, Park Komenského 5, 042 00 Košice, Slovakia; <email>peter.krajny@student.tuke.sk</email> (P.K.); <email>jaroslava.janekova@tuke.sk</email> (J.J.) | |
| 245 | 1 | |a Data Digitization in Manufacturing Factory Using Palantir Foundry Solution | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This research describes an online solution for the collection and processing of production data, which are gathered from manufacturing and assembly processes at automotive companies. The solution describes the process for live monitoring of the production health and then evaluation through reports, with the option to generate reports for up to six months. Since the data are located in multiple sources, it is challenging to monitor them live or generate reports on demand. The solution described in this research outlines applications that simplify users’ tasks and provide immediate insights into the processes and health of production lines. Research will be divided into three applications which are delivered in one package, which is called Cycle Time Deviation (CTD): (i) workshop application for live monitoring; (ii) for evaluating data older than 24 h, the shift report application; and (iii) for comparing and monitoring the impact of process changes on the analysis, the before and after application—the Plant Improvement Tracker (PIT)—will be presented. The aim of the research is to describe the proposed solution that was implemented in a multinational automotive corporation and to outline the benefits gained from the implementation. | |
| 653 | |a Innovations | ||
| 653 | |a Big Data | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Digital transformation | ||
| 653 | |a Production lines | ||
| 653 | |a Business intelligence | ||
| 653 | |a Decision making | ||
| 653 | |a Small & medium sized enterprises-SME | ||
| 653 | |a Data processing | ||
| 653 | |a Impact analysis | ||
| 653 | |a Data analysis | ||
| 653 | |a Cycle time | ||
| 653 | |a Digitization | ||
| 653 | |a Automation | ||
| 653 | |a Manufacturing | ||
| 653 | |a Logistics | ||
| 653 | |a Industry 4.0 | ||
| 653 | |a Human error | ||
| 653 | |a Digital technology | ||
| 700 | 1 | |a Janeková, Jaroslava |u Institute of Industrial Engineering, Management and Applied Mathematics, Technical University of Kosice, Park Komenského 5, 042 00 Košice, Slovakia; <email>peter.krajny@student.tuke.sk</email> (P.K.); <email>jaroslava.janekova@tuke.sk</email> (J.J.) | |
| 700 | 1 | |a Fabianová, Jana |u Institute of Logistics and Transport, Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, Letná 9, 042 00 Košice, Slovakia | |
| 773 | 0 | |t Processes |g vol. 12, no. 12 (2024), p. 2816 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149755312/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3149755312/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3149755312/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |