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Effects of Sensor-Based Quality Data in Automotive Supply Chains – A Simulation Study

  • Daniel Sommerfeld
  • Michael Teucke
  • Michael Freitag
Conference paper
Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

Supply chain risk management (SCRM) is becoming increasingly attractive as it opens up various control opportunities in case of rising volatility in value-added networks. Sensor-based, real-time quality data will be the founding an event-driven organization of supply chains with regard to more transparency. The following article presents the opportunities of using real-time, sensor-based quality data in automotive supply chain (SC) analyzed within a simulation study. Therefore, a discrete-event simulation of an automotive SC evaluates the usage of quality data. Different scenarios of control mechanisms are developed in three test cases characterized by different quality failure probabilities. For each of the test cases, the effect on stocks is described. The investigations show the positive effect of using real-time quality data to reduce stocks. The most positive effect is related to methods like special transports, but their cost-intensive structure has to be optimized. In conclusion, sensor-based quality data can face the rising volatility. Further research should focus on innovative controlling methods.

Keywords

Supply chain management Supply chain risk management Quality data Early warning system Simulation study 

Notes

Acknowledgments

The authors would like to thank the German Federal Ministry Economic Affairs and Energy (BMWi) as part of the collaborative research and development project with funding code 01MA16004 “SaSCh – Digital Services for Shaping Agile Supply Chains”.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Production EngineeringUniversity of BremenBremenGermany
  2. 2.BIBA – Bremer Institut für Produktion und Logistik GmbH at the University of BremenBremenGermany

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