An Integration Technology for Long Distance Logistics and Supply Chain Management
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Type of Work9 pages
conference papers and proceedings
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Supply chain management
UMBC Ebiquity Research Group
Despite the global economic downturn, import/export trade will continue to grow. That trade depends on many intercontinental, long-distance, logistics partners. Those partners include exporters, brokers, truckers, customs, carriers, and importers who depend on information to do their jobs. The software support systems that generate, use, and exchange that information are exceedingly complex and highly heterogeneous in nature. Inadequate approaches to integration of such systems means that trading partners encounter recurring problems including late deliveries, inaccurate tracking, insufficient visibility, poor security, unreliable planning, and unnecessary disruptions. In this paper, we propose an integration approach that will reduce or eliminate these problems. That approach must (1) ensure interoperability among logistics applications; (2) provide shipment visibility to all trading partners; (3) reduce the average and variance of transport times; (4) accommodate associated international standards; (5) reuse legacy applications, where possible; and, (6) facilitate recovery from supply chain disruptions. Our approach has four proposals. First, we propose a collaboration architecture and a federated network framework using SOA-based concepts. Second, we propose a standards-based approach to message specification that will facilitate interoperability among trading partners. Third, we propose a framework for fast recovery from supply chain disruptions. Fourth, we propose the concept of a test bed to verify that proposed commercial solutions conform to those standards.. We believe that our approach will impact the intercontinental logistics industry significantly – in terms of quantitative and qualitative measures. First, it will increase shipment visibility dramatically among global trading partners. Second, it will decrease both the average and variance of transport times significantly. Third, it will reduce the cost associated with premium freight, and improve data quality and integrity.
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