The design of a spatial distribution structure is of strategic importance for companies, to meet required customer service levels and to keep logistics costs as low as possible. Spatial distribution structure decisions concern distribution channel layout – i.e. the spatial layout of the transport and storage system – as well as distribution centre location(s). This paper examines the importance of seven main factors and 33 sub-factors that determine these decisions. The Best-Worst Method (BWM) was used to identify the factor weights, with pairwise comparison data being collected through a survey. The results indicate that the main factor is logistics costs. Logistics experts and decision makers respectively identify customer demand and service level as second most important factor. Important sub-factors are demand volatility, delivery time and perishability. This is the first study that quantifies the weights of the factors behind spatial distribution structure decisions. The factors and weights facilitate managerial decision-making with regard to spatial distribution structures for companies that ship a broad range of products with different characteristics. Public policy-makers can use the results to support the development of land use plans that provide facilities and services for a mix of industries.
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.
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.
