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http://hdl.handle.net/10773/41326
Title: | A data-driven model with minimal information for bottleneck detection - application at Bosch thermotechnology |
Author: | Brochado, A. F. Rocha, Eugénio Almeida, Duarte de Sousa, Amaro Moura, A. |
Keywords: | Minimal information Data-driven model Autonomous system generation Bottleneck detection Manufacturing production system |
Issue Date: | 2022 |
Publisher: | Taylor & Francis |
Abstract: | In the context of bottleneck detection, most data-driven approaches employ data from diverse production variables (machine processing times, machine state tags, input timestamps, etc.) for a detailed analysis of bottlenecks. However, for manufacturing companies initiating their digitalization process (i.e. requiring the smallest hardware investment), a bottom-top approach is still missing. In this work, a data-driven model based on minimal information (MI) retrieved from a manufacturing execution system is proposed for bottleneck detection. We consider MI timestamps when each product exits each station and show that this is the most elementary information from production-line operations, enough to autonomously generate an abstract manufacturing layout, and to detect and predict bottlenecks. A general abstract model of a production line is proposed, named queue directed graph (QDG). Incorporating the MI, the QDG model is able to represent a job-shop with a discrete production environment and to calculate production metrics. This work has been employed in the production system of a Bosch factory, in Portugal, using their manufacturing data sets for validation. Different variants of two well-known bottleneck detection methods were implemented and adapted to Bosch’s use case: the Active Period Method and the Average Active Period Method. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/41326 |
DOI: | 10.1080/17509653.2022.2116121 |
ISSN: | 1750-9653 |
Appears in Collections: | CIDMA - Artigos DEGEIT - Artigos DETI - Artigos DMat - Artigos GOVCOPP - Artigos FAAG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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A data-driven model with minimal information for bottleneck detection - application at Bosch thermotechnology.pdf | 5.85 MB | Adobe PDF |
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