Enabling situation awareness with supply chain event management. (1st March 2018)

• Record Type: Journal Article
• Authors: Vlahakis, George; Apostolou, Dimitris; Kopanaki, Evi
• Is Part Of: Expert systems with applications. Volume 93(2018)
• Page Start: 86
• Page End: 103
• Publication Date: 2018-03-01
• Subjects: Supply chain management -- Supply chain event management -- Situation awareness -- Event processing; SCM Supply Chain Management -- SCEM Supply Chain Event Management -- CEP Complex Event Processing -- SC Supply Chain -- SA Situation Awareness -- LAT Latitude -- LONG Longitude -- EL Event Listener -- CA Correlation Agent -- KPI Key Performance Indicator -- EDL Event Description Language -- SOA Service Oriented Architecture -- CQL Continuous Query Language; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.10.013
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Supply chain event management refers to methods that process supply chain events. We present a graph approach to represent supply chain events and their relations. We describe a framework that implements event correlation and situation detection. Key benefits include capability to adapt to dynamic conditions and scalability. Abstract: Supply chain event management exploits synergies between IT and logistics and refers to the set of methods and technologies used to efficiently integrate events from all actors and processes of the supply chain. In the context of supply chain event management, we examine how events can be used to leverage situation awareness. In our approach, situation awareness is facilitated by providing the capability to detect situations, which are represented as correlations between simple events, complex events and supply chain objects (e.g., suppliers, 3PL companies, retailers and material resources). We introduce a two-phase event correlation method which first correlates simple events into complex events and then events with supply chain objects. We describe how the proposed model has been implemented in a software framework and we conduct evaluation tests to examine its situation detection capabilities.