Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems

While the achievement of mix flexibility is a key driver of competitive advantage for a manufacturing organization, it also represents a source of disruption on the shop floor that may compromise managers' ability to diagnose problems, predict behavior, and make decisions adequately. Given this...

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Veröffentlicht in:International journal of production economics 2023-06, Vol.260, p.108846, Article 108846
Hauptverfasser: Neto, Anis Assad, Ribeiro da Silva, Elias, Deschamps, Fernando, do Nascimento Junior, Laercio Alves, Pinheiro de Lima, Edson
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Sprache:eng
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Zusammenfassung:While the achievement of mix flexibility is a key driver of competitive advantage for a manufacturing organization, it also represents a source of disruption on the shop floor that may compromise managers' ability to diagnose problems, predict behavior, and make decisions adequately. Given this challenge, the digital twin emerges as a potential tool to reestablish managers’ operational visibility. Its architecture allows for a manufacturing system model to be continuously updated and for management support services to be promptly delivered. Although some studies explore the use of the digital twin to solve specific flexibility-related challenges, the literature lacks a normative and systematic approach to guide the application of this technological architecture in a comprehensive set of flexibility scenarios. To address this gap, based on the normative knowledge acquired from the literature and subject-matter experts, we present procedures to design, implement, and use the digital twin within organizations that suffer flexibility-driven disruptions. We demonstrate the application of these procedures through a case in a large automotive parts manufacturer. Results show that the procedures effectively operationalize a digital twin architecture aimed at contributing to the achievement of a flexible production strategy.
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2023.108846