Machine learning-based prediction of missing parts for assembly
Dissertation, Universität Siegen, 2024
Gespeichert in:
| 1. Verfasser: | |
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Wiesbaden, Heidelberg
Springer Vieweg
2024
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| Schriftenreihe: | Findings from production management research
Research |
| Schlagworte: |
BUSINESS & ECONOMICS / Production & Operations Management
> BUSINESS & ECONOMICS / Quality Control
> COM094000
> COMPUTERS / Database Management / General
> Databases
> Datenbanken
> Fertigungstechnik und Ingenieurwesen
> Machine learning
> Management: Produktion und Qualitätskontrolle
> Maschinelles Lernen
> Production & quality control management
> Production engineering
> TECHNOLOGY & ENGINEERING / Industrial Engineering
> TECHNOLOGY & ENGINEERING / Manufacturing
> Hochschulschrift
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| Online Zugang: | Cover |
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| Zusammenfassung: | Dissertation, Universität Siegen, 2024 Introduction.- Theoretical Background for the Prediction of Missing Parts for Assembly.- Publication I: Approaches for the Prediction of Lead Times in an Engineer to Order Environment - a Systematic Review.- Publication II: Impact of Material Data in Assembly Delay Prediction - a Machine Learning-based Case Study in Machinery Industry.- Publication III: Machine Learning-based Prediction of Missing Components for Assembly - a Case Study at an Engineer-to-order Manufacturer.- Publication IV: Predicting Supplier Delays Utilizing Machine Learning - a Case Study in German Manufacturing Industry.- Critical Refection and Future Perspective.- Summary.- References. Manufacturing companies face challenges in managing increasing process complexity while meeting demands for on-time delivery, particularly evident during critical processes like assembly. The early identification of potential missing parts at the beginning assembly emerges as a crucial strategy to uphold delivery commitments. This book embarks on developing machine learning-based prediction models to tackle this challenge. Through a systemic literature review, deficiencies in current predictive methodologies are highlighted, notably the underutilization of material data and a late prediction capability within the procurement process. Through case studies within the machine industry a significant influence of material data on the quality of models predicting missing parts from in-house production was verified. Further, a model for predicting delivery delays in the purchasing process was implemented, which makes it possible to predict potential missing parts from suppliers at the time of ordering. These advancements serve as indispensable tools for production planners and procurement professionals, empowering them to proactively address material availability challenges for assembly operations |
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| Beschreibung: | Literaturverzeichnis: Seite 141-155 |
| Beschreibung: | xxii, 155 Seiten Diagramme |
| ISBN: | 9783658450328 978-3-658-45032-8 |