Spatial decisions on distribution channel layout involve the layout of the transport and storage system between production and consumption as well as the selection of distribution centre locations. Both are strategic company decisions to meet logistics challenges, i.e. delivering the right product at the right location on time. In this paper we study the main factors and sub factors that drive spatial decisions on distribution channel layout. The current literature has a strong focus on normative approach and lacks descriptive research into these factors. In the second part of the study, we investigated the importance of the factors. Best-Worst Method (BWM) has been used to calculate the factor weights. BWM provides consistent results and requires fewer comparisons than ‘matrix based’ methods. An online survey was used to collect the data. According to total sample of respondents, the most important factors are ‘Logistics costs’, ‘Service level’ and ‘Demand level’. Logistics costs being the most important factor is in line with Supply Chain Management literature. Logistics experts consider ‘Customer demand’ as the second most important factor, whereas decision makers consider ‘Service level’ the second most important factor. A limitation of the research is that the majority of respondents are from Europe and the USA. For future research we suggest to test how respondents from non-Western countries value the importance of several factors.
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.
In the SensEQuake project, the Research Centre for Built Environment NoorderRuimte of Hanze University of Applied Sciences, StabiAlert, Target Holding and NHL Stenden Leeuwarden are investigating the following question:How can we provide relevant and understandable information to support decision makers when an earthquake has occurred?In case of a crisis such as an earthquake, parties such as the provincial government, large company sites, airports or hospitals need information on the scope and severity of the effect of the crisis.Systematic updates of the actual situation on site are of the essence for emergency services. At present only a small amount of the data necessary for this information needed is being collected. And the data that is collected is not processed into relevant and easily understandable information for the decision makers. This project aims to fill this gap.The objective of the project is to integrate the existing sensor technologies into a decision support system, allowing a wider and more immediate use of sensor data for public interest, particularly in crisis times.A heat-map will be produced based on scenario earthquakes and loss (hazard and risk assessment) estimation tools. After running several scenario quakes, critical points in respect to the expected damages and the distribution of existing sensors will be defined. More sensors in critical locations will also be placed to create a high enough resolution.
