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Multi-variate time-series for time constraint adherence prediction in complex job shops

May, Marvin Carl; Behnen, Lukas; Holzer, Andrea; Kuhnle, Andreas; Lanza, Gisela

Abstract:
One of the most complex and agile production environments is semiconductor manufacturing, especially wafer fabrication, as products require more than several hundred operations and remain in Work-In-Progress for months leading to complex job shops. Additionally, an increasingly competitive market environment, i.e. owing to Moore’s law, forces semiconductor companies to focus on operational excellence, resiliency and, hence, leads to product quality as a decisive factor. Product-specific time constraints comprising two or more, not necessarily consecutive, operations ensure product quality at an operational level and, thus, are an industry-specific challenge. Time constraint adherence is of utmost importance, since violations typically lead to scrapping entire lots and a deteriorating yield. Dispatching decisions that determine time constraint adherence are as a state of the art performed manually, which is stressful and error-prone. Therefore, this article presents a data-driven approach combining multi-variate time-series with centralized information to predict time constraint adherence probability in wafer fabrication to facilitate dispatching. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000140338
Veröffentlicht am 25.11.2021
Originalveröffentlichung
DOI: 10.1016/j.procir.2021.10.008
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000140338
Erschienen in 9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19. Ed.: K. Medini
Veranstaltung 9th CIRP Global Web Conference (CIRPe 2021), Online, 26.10.2021 – 28.10.2021
Verlag Elsevier
Seiten 55-60
Serie Procedia CIRP ; 103
Schlagwörter Production Planning; Control; Time Constraints; Data Analytics; Time Series Analysis; Machine Learning
Nachgewiesen in Scopus
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