Data analytics for sustainable global supply chains

Abstract

Based on the key metrics to monitor energy sector improvements from the International Energy Agency (IEA), transport emissions must decrease 43% by 2030. Freight logistics operations in Europe are struggling with ways to reduce their carbon footprints in order to adhere to regulations on governing logistics, while providing the increasing demand for sustainable products from the customers. This study investigates the anonymised microdata from the European Road Freight Transport Survey (2011–2014) to acquire patterns in logistic operations based on over 11 million journeys within 27 EU and EFTA countries involved. Different algorithms were implemented (Horizontal Cooperation, Pooling and Physical Internet) to analyse efficiency, in terms of vehicle utilisation, degree of vehicles’ loading during each journey and sustainability in terms of the amount of CO2 emissions per journey. This study shows that existing data can provide invaluable information on the efficiency of logistics operations and the positive effects data analytics can provide. Physical Internet algorithm has performed better in terms of reducing emissions and improving the logistics’ efficiency, especially when the sample sizes are large, but this would require a shift to an open global supply web.