Social networks in logistics system decision-making

Abstract

Social networks, such as Facebook, Twitter and LinkedIn have been becoming very popular during the last few years. Facebook is currently the world’s most populous “country” with more t han 1 .3 b illion “ inhabitants”. A ccording to t he s tatistical d ata, t he u sers share their impressions daily in the form of statuses about upcoming events and present state of affairs, their problems, plans, novel experiences about the products, political stances, and alike. Having the possibility to extract the information of interest from a huge amount of hand-created data about the users’ personal affinities and their usage within logistics system, it is facilitated to meet the customers’ needs. In this paper we present a procedure for finding and analyzing valuable information related to the specific products, and its effect on logistics system decision-making. Filtering is being done by already developed software for neurolinguistics social network analysis - “Symbols”. This software offers graphical representation of statistical data for selected brands based on the social network statuses, its implications, as well as target group demographic and territorial structure. The results obtained point out possible increasing/decreasing demands among separate user groups, therefore giving a factual basis for logistics changes.