A disruption management system for automotive inbound networks: concepts and challenges

Production processes in the automotive industry are highly dependent on reliable inbound logistics processes, because in lean production systems delays or mistakes often result in expensive interruptions of production processes. However, transport processes are always subject to unavoidable disturbances, delays, or mistakes. The goal of the research project ProveIT is to provide an IT system improving the transparency by monitoring transport processes in real-time: deviations from the transport plans are identified predictively, and classified dynamically as disruptions if they have negative impacts on the subsequent processes. If a disruption occurs, the operations managers are provided with mitigation actions automatically generated by escalation-based online optimization algorithms. In this contribution, we introduce the use cases, the architecture and main concepts of the ProveIT disruption management system, and report on challenges faced during field experiments with our application partners, Bosch, ZF, and Geis.

[1]  David Luckham,et al.  The power of events - an introduction to complex event processing in distributed enterprise systems , 2002, RuleML.

[2]  Gilbert Laporte,et al.  Fifty Years of Vehicle Routing , 2009, Transp. Sci..

[3]  Michel Gendreau,et al.  Intelligent Freight Transportation Systems : Assessment and the Contribution of Operations Research , 2009 .

[4]  Andreas Metzger,et al.  Predictive Monitoring of Heterogeneous Service-Oriented Business Networks: The Transport and Logistics Case , 2012, 2012 Annual SRII Global Conference.

[5]  Michel Gendreau,et al.  Time-dependent routing problems: A review , 2015, Comput. Oper. Res..

[6]  Rajagopalan Srinivasan,et al.  How to Handle Disruptions in Supply Chains – An Integrated Framework and a Review of Literature , 2012 .

[7]  Andreas Metzger,et al.  Proactive event processing in action: a case study on the proactive management of transport processes (industry article) , 2013, DEBS '13.

[8]  Yan Shi,et al.  A recovery model for combinational disruptions in logistics delivery: Considering the real-world participators , 2012 .

[9]  Jan Mendling,et al.  Towards the Enhancement of Business Process Monitoring for Complex Logistics Chains , 2013, Business Process Management Workshops.

[10]  A. Baader,et al.  Transparency in Global Supply Chain Networks — Methods and Tools for Integrated Supply Chain Event Management , 2008 .

[11]  F. Bodendorf,et al.  Agent-Based Supply Chain Event Management — Concept and Assessment , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[12]  G. Heinecke.  Resilient Automotive Production in Vulnerable Supply Networks: A Supply Chain Event Management System , 2013 .

[13]  Stephan M. Wagner,et al.  Assessing the vulnerability of supply chains using graph theory , 2010 .

[14]  Michel Gendreau,et al.  A review of dynamic vehicle routing problems , 2013, Eur. J. Oper. Res..

[15]  Remco M. Dijkman,et al.  A software architecture for a transportation control tower , 2014 .

[16]  Mei-Shiang Chang,et al.  The real-time time-dependent vehicle routing problem , 2006 .

[17]  V. Boschian,et al.  Business Models for Advanced ICT in Logistics , 2016 .

[18]  Christine Solnon,et al.  A Time-Dependent No-Overlap Constraint: Application to Urban Delivery Problems , 2015, CPAIOR.

[19]  Gilbert Laporte,et al.  Rich routing problems arising in supply chain management , 2013, Eur. J. Oper. Res..

[20]  Anne Meyer,et al.  Milk Run Design: Definitions, Concepts and Solution Approaches , 2017 .

[21]  Bernhard Seeger,et al.  Anomaly management using complex event processing: extending data base technology paper , 2013, EDBT '13.