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.
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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.
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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.
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Research statementOur study analyses the factors that drive decision-making on distribution structures, including the layout of distribution channels and the locations of distribution centres. Distribution is a primary firm activity, which strongly influences logistics costs and logistics performance. Distribution is a challenging activity as customer demand is often volatile and unpredictable. Consumers continuously expect higher service related to distribution, e.g., same day delivery and more flexibility in delivery locations. Therefore, it is of strategic importance to shippers and Logistics Service Providers (LSPs) to decide which distribution channel layout to use and, accordingly, plan distribution centre location(s). Distribution structure selection concerns the number and locations of distribution centres, as part of the larger corporate planning process. The main questions we strive to answer in this paper are: (1) what are the main criteria that determine the spatial layout of distribution structures? and (2) how important are these criteria, relative to each other?Methodology The literature on distribution channel design mostly revolves around optimization methods; we are not aware of literature that takes a descriptive approach. We therefore develop a descriptive conceptual model that includes these factors, developed from the contextual literature around this decision. The second part of the study concerns the measurement of the relative importance of these factors. We implemented an elaborate survey and used the Best-Worst Method (BWM) to identify these weights. The survey considers different experts (e.g., logistics managers versus logistics professors) and population segments (e.g., in-house versus outsourced distribution).Data and resultsCurrently we are completing the survey dedicated to evaluating the above factors. We have received sufficient response to estimate a first model. These first estimations already provide useful results. Final estimations will be completed and reported in June 2017. At the I-NUF conference we will be able to present the results and analysis of all factors when comparing respondents and parameters.Preliminary conclusionsBased on literature review, eight main factors – divided into 33 sub factors – are included in our research: 1) Demand factors, 2) Service level factors, 3) Product Characteristics factors, 4) Logistics costs factors, 5) Proximity-related location factors, 6) Accessibility-related location factors, 7) Resources-related location factors and 8) Institutional factors. A number of hypotheses were built from the literature analysis relating, for example, to the relative importance of service- and cost- related factors within different industries. We will revisit these hypotheses and provide the quantitative results of the importance of the individual factors in our paper and at the conference.
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Humanitarian logistics is regarded as a key area for improved disaster management efficiency and effectiveness. In this study, a multi-objective integrated logistic model is proposed to locate disaster relief centers while taking into account network costs and responsiveness. Because this location problem is NP-hard, we present a genetic approach to solve the proposed model.
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This paper studies the factors that drive distribution structure design (DSD), which includes the spatial layout of distribution channels and location choice of logistics facilities. We build on a generic framework from existing literature, which we validate and elaborate using interviews among industry practitioners. Empirical evidence was collected from 18 logistics experts and 33 decision-makers affiliated to shippers and logistics service providers from the fashion, consumer electronics and online retail sectors. It turns out that interviewees share similar rankings of main factors across industries, and even confirm factor weights from earlier research established using multi-criteria decision analysis, which would indicate that the framework is sector- neutral at the highest level. The importance attached to subfactors varies between sectors according to our expectations. We were able to identify 20 possible new influencing subfactors. The results may support managers in their decision-making process, and regional policy-makers with regard to spatial planning and regional marketing. The framework is a basis for researchers to help improve further quantitative DSD support models.
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Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.
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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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Several studies show that logistics facilities have spread spatially from relatively concentrated clusters in the 1970s to geographically more decentralized patterns away from urban areas. The literature indicates that logistics costs are one of the major influences on changes in distribution structures, or locations and usage of logistics facilities. Quantitative modelling studies that aim to describe or predict these phenomena in relation to logistics costs are lacking, however. This is relevant to design more effective policies concerning spatial development, transport and infrastructure investments as well as for understanding environmental consequences of freight transport. The objective of this paper is to gain an understanding of the responsiveness of spatial logistics patterns to changes in these costs, using a quantitative model that links production and consumption points via distribution centers. The model is estimated to reproduce observed use of logistics facilities as well as related transport flows, for the case of the Netherlands. We apply the model to estimate the impacts of a number of scenarios on the spatial spreading of regional distribution activity, interregional vehicle movements and commodity flows. We estimate new cost elasticities, of the demand for trade and transport together, as well as specifically for the demand for the distribution facility services. The relatively low cost elasticity of transport services and high cost elasticity for the distribution services provide new insights for policy makers, relevant to understand the possible impacts of their policies on land use and freight flows.
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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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