Distribution structures and distribution centre (DC) locations are essential for logistics companies to optimise logistics costs and service levels. This paper reviews Supply Chain Management (SCM), Geography and Economic Geographic literature on distribution structure and DC locations decision-making. Two central decision-making elements are discussed: process steps and decision-making factors. Added value of our paper is 1) A literature review 2) Conclusions on the state of current scientific knowledge in three research streams 3) A research agenda. Reviewing literature shows decision-making factors are renowned, however, importance of factors in each process step is unknown. Results also show literature diverges on which process steps logistics companies take (descriptive) or should optimally take (prescriptive) in distribution structure and DC location decision-making. Thus, more research is needed. Developing a descriptive conceptual model and testing on several industry sectors will be valuable to understand differences on distribution structure and DC location decision-making.
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Distribution centres are becoming more and more relevant for spatial planning, due to their rapidly increasing size and number. There is little literature, however, that provides a generalized analysis of the size and functional attributes of distribution centres, and none that discusses the relationships between these attributes. Our aim is to fill this gap by providing new evidence and analysis to understand this relationship. We make use of an extensive database of 2888 DCs in the Netherlands to develop a new typology of DCs based on the geographical location of DCs, their functional attributes and client sector characteristics. The analysis shows that the context in which medium sized DCs are operating is more heterogeneous than in the case of very large and small size DCs. This study is a first attempt to analyse this relationship between facility size and functions based on a rich and extensive dataset of large population of DCs. The results can serve as input for further quantitative statistical analysis and international comparison.
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The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low. As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers. To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is: How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems? HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases: I. Highway: non-professional drivers; II. Distribution Centre: professional drivers.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low.As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers.To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is:How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems?HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases:I. Highway: non-professional drivers;II. Distribution Centre: professional drivers.Collaborative partners:Bielefeld University of Applied Sciences, Bricklog B.V., Goudappel B.V., HaskoningDHV Nederland B.V., Rhine-Waal University of Applied Sciences, Rijkswaterstaat, Saxion, Sencure B.V., Siemens Industry Software Netherlands B.V., Smits Opleidingen B.V., Stichting Innovatiecentrum Verkeer en Logistiek, TNO Den Haag, TU Delft, University of Twente, V-Tron B.V., XL Businesspark Twente.
