Big data processing in the logistics industry

Abstract

The aim of modern logistics is to achieve maximum connectivity in the supply chain. Companies are using increasingly innovative technological solutions, which creates the opportunity of generating a wide variety of data. This leads to several challenges and the need to change data storage and processing models. The aim of the study is to analyze the technological aspects of the digital transformation in logistics and to propose a conceptual framework for big data management and processing in the logistics industry. It is based on the discovery of existing prototype methodologies for big data processing which are used in all areas of business, as well as on the research of existing specific approaches to the processing of different types of big data in logistics. Basic principles for building a modern architecture for managing and processing big data in logistics are presented. The defined framework can be used by the companies to process structured, semi-structured and unstructured data in real time or for batch processing and to help optimize several business processes in the logistics industry. As a result, using it will help the analytical processes in these companies and it will be possible to make informed business decisions in dynamic conditions and in globalization. A software implementation of a conceptual framework with the Apache Handoop open-source software is proposed. The study is part of Project BG05M2OP001-1.002-0002-C02 "Digitalization of Economy in a Big Data Environment".

Keywords

• logistics
• big data
• digitization
• conceptual framework