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Juan Godoy. (2007). A holistic approach to emergency evacuation information support systems. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 345–354). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In the USA the basic objective of local and state government's Emergency Operations Plans (EOP) is to implement mitigation measures to reduce the loss of life and property damage by the efficient mobilization and deployment of resources. The evacuation of citizens out of harms way either before an impeding disaster or after the occurrence of one is a critical component of any EOP. This document represents a summary of the Evacuation Plan designed for the City of New Orleans. Results of live field exercises conducted during the 2006 Hurricane Season and suggestions for improvement will be highlighted. The ideal Emergency Evacuation Tracking System will be designed to operate within a System of Systems framework with interfaces: to field personnel, emergency managers and logisticians operating in an Emergency Operations Center (EOC), with state and local government systems such as public information emergency hotline (311 Centers in the USA), asset tracking management systems and others.
Keywords: Human resource management; Interface states; Systems engineering; Critical component; Emergency evacuation; Emergency operations; Emergency operations centers; Information support systems; Management systems; Mitigation measures; Public information; Information management
Janzen, S., Baer, S., Ahiagble, A. P., & Maass, W. (2023). Tackling Non-transparency – Identification of Hidden Problems in Component-Based Supply Chains. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (p. 1075). Omaha, USA: University of Nebraska at Omaha.
Abstract: Component-based supply chains, e.g., in sensor industry, can be very complex and non-transparent, with multiple tiers of suppliers involved. This leads to hidden problems (e.g., component shortages) that propagate and reinforce in supply chains before popping up as crisis situation at tier-1 with significant consequences as production delays. To tackle non-transparency in supply chains, it is crucial to detect and localize those hidden problems for supporting users in conducting pro-active measures (e.g., search of missing parts at spot-market) and creating more resilient supply chains. With the Hidden Problem Detector, we present a prototype (Flask, Python, Neo4j, Octopart), that uses multiple graph-theoretic centrality measures for determining critical components in the supply chain. Bill-of-Materials data are automatically transformed into a knowledge graph, semantically enriched, and fed with historical and actual market data (e.g., prices). Within the demonstration, we show the detection of hidden problems in the supply chain of a sensor manufacturer.
Keywords: Supply Chain Disruptions; Non-transparency; Hidden Problems; Critical Components; Knowledge Graph