The mobile objects in the supply chain present the means of transportation, and they have an influence on the functioning of the supply chain. The mobile object bring a correct information, where and when necessities, to reduce the uncertainty, increase the visibility of products and increase the global efficiency of the supply chain. The supply chain is a system characterized by the mobility between the various processes of the chain as well as the mobility pattern of materials including the vehicle which carries in transportation network. Mobility mining is the process of extracting hidden knowledge from moving object trajectories. This is a concept paper which visualizes the scope of various mobility mining techniques for analysis and optimization of objects moving in transportation network. Also we demonstrate how the trajectory similarity technique which is one of the mobility mining technique could be used for an efficient and effective supply chain infrastructure.

In this fast developing period the use of RFID have become more significant in many application domaindue to drastic cut down in the price of the RFID tags. This technology is evolving as a means of tracking objects and inventory items. One such diversified application domain is in Supply Chain Management where RFID is being applied as the manufacturers and distributers need to analyse product and logistic information in order to get the right quantity of products arriving at the right time to the right locations. Usually the RFID tag information collected from RFID readers is stored in remote database and the RFID data is being analyzed by querying data from this database based on path encoding method by the property of prime numbers. In this paper we propose an improved encoding scheme that encodes the flows of objects in RFID tag movement. A Trajectory of moving RFID tags consists of a sequence of tagsthat changes over time. With the integration of wireless communications and positioning technologies, the concept of Trajectory Database has become increasingly important, and has posed great challenges to the data mining community.The support of efficient trajectory similarity techniques is indisputably very important for the quality of data analysis tasks in Supply Chain Traffic which will enable similar product movements.