On Modelling Big Data Guided Supply Chains in Knowledge-Base Geographic Information Systems

Abstract

We examine the existing goals of business- and geographic - information systems and their influence on logistics and supply chain management systems. Modelling supply chain management systems is held back because of lack of consistent and poorly aligned data with supply chain elements and processes. The issues constraining the decision-making process limit the connectivity between supply chains and geographically controlled database systems. The heterogeneous and unstructured data are added challenges to connectivity and integration processes. The research focus is on analysing the data heterogeneity and multidimensionality relevant to supply chain systems and geographically controlled databases. In pursuance of the challenges, a unified methodological framework is designed with data structuring, data warehousing and mining, visualization and interpretation artefacts to support connectivity and integration process. Multidimensional ontologies, ecosystem conceptualization and Big Data novelty are added motivations, facilitating the relationships between events of supply chain operations. The models construed for optimizing the resources are analysed in terms of effectiveness of the integrated framework articulations in global supply chains that obey laws of geography. The integrated articulations analysed with laws of geography can affect the operational costs, sure for better with reduced lead times and enhanced stock management.