Applying Data Mining Tools in Transportation : Data-Driven Supply Chain View

Abstract

Despite the big data research and relevance of data analysis there has been limited empirical research and implication of data-driven supply chain networks. This paper explores the effect of data-driven supply chain capabilities on transportation (train based). In order to illustrate the shortest path calculation, London Underground Transportation open source data have been analysed through implementing different data mining tools and using programming language Python and R. The findings indicate that a data-driven supply chain has a significant time efficient effect on the logistics support. Coordination, using available data, and supply chain responsiveness are positively and significantly related to time and cost efficient performance. This system can be implemented in train based logistic support to consider the route selection.