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Open AccessArticle

Using Sensor-Based Quality Data in Automotive Supply Chains

BIBA–Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Hochschulring 20, 28359 Bremen, Germany
Faculty of Production Engineering, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany
Author to whom correspondence should be addressed.
Machines 2018, 6(4), 53;
Received: 30 September 2018 / Revised: 19 October 2018 / Accepted: 26 October 2018 / Published: 1 November 2018
(This article belongs to the Special Issue Smart Manufacturing, Digital Supply Chains and Industry 4.0)
In many current supply chains, transport processes are not yet being monitored concerning how they influence product quality. Sensor technologies combined with telematics and digital services allow for collecting environmental data to supervise these processes in near real-time. This article outlines an approach for integrating sensor-based quality data into supply chain event management (SCEM). The article describes relationships between environmental conditions and quality defects of automotive products and their mutual relations to sensor data. A discrete-event simulation shows that the use of sensor data in an event-driven control of material flows can keep inventory levels more stable. In conclusion, sensor data can improve quality monitoring in transport processes within automotive supply chains. View Full-Text
Keywords: Industry 4.0; digital supply chain; sensors; smart logistics; simulation Industry 4.0; digital supply chain; sensors; smart logistics; simulation
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Teucke, M.; Broda, E.; Börold, A.; Freitag, M. Using Sensor-Based Quality Data in Automotive Supply Chains. Machines 2018, 6, 53.

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