In the city of Amsterdam commercial transport is responsible for 15% of vehicles, 34% of traffic’s CO2 emissions and 62% of NOx emissions. The City of Amsterdam plans to improve traffic flows using real time traffic data and data about loading and unloading zones. In this paper, we present, reflect, and discuss the results of two projects from the Amsterdam University of Applied Sciences with research partners from 2016 till 2018. The ITSLOG and Sailor projects aim to analyze and test the benefits and challenges of connecting ITS and traffic management to urban freight transport, by using real-time data about loading and unloading zone availability for rerouting trucks. New technologies were developed and tested in collaboration with local authorities, transport companies and a food retailer. This paper presents and discusses the opportunities and challenges faced in developing and implementing this new technology, as well as the role played by different stakeholders. In both projects, the human factor was critical for the implementation of new technologies in practice.
MULTIFILE
Distribution structures, as studied in this paper, involve the spatial layout of the freight transport and storage system used to move goods between production and consumption locations. Decisions on this layout are important to companies as they allow them to balance customer service levels and logistics costs. Until now there has been very little descriptive research into the factors that drive decisions about these structures. Moreover, the literature on the topic is scattered across various research streams. In this paper we review and consolidate this literature, with the aim to arrive at a comprehensive list of factors. Three relevant research streams were identified: Supply Chain Management (SCM), Transportation and Geography. The SCM and Transportation literature mostly focus on distribution structure including distribution centre (DC) location selection from a viewpoint of service level and logistics costs factors. The Geography literature focuses on spatial DC location decisions and resulting patterns mostly explained by location factors such as labour and land availability. Our review indicates that the main factors that drive decision-making are “demand level”, “service level”, “product characteristics”, “logistics costs”, “labour and land”, “accessibility” and “contextual factors”. The main trade-off influencing distribution structure selection is “service level” versus “logistics costs”. Together, the research streams provide a rich picture of the factors that drive distribution structure including DC location selection. We conclude with a framework that shows the relative position of these factors. Future work can focus on completing the framework by detailing out the sub factors and empirically testing the direction and strength of relationships. Cooperation between the three research streams will be useful to further extend and operationalize the framework.