Efficiency and resilience: key drivers of distribution network growth

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Ambra Amico, Giacomo Vaccario, Frank Schweitzer
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Abstract

Networks to distribute goods, from raw materials to food and medicines, are the backbone of a functioning economy. They are shaped by several supply relations connecting manufacturers, distributors, and final buyers worldwide. We present a network-based model to describe the mechanisms underlying the emergence and growth of distribution networks. In our model, firms consider two practices when establishing new supply relations: centralization, the tendency to choose highly connected partners, and multi-sourcing, the preference for multiple suppliers. Centralization enhances network efficiency by leveraging short distribution paths; multi-sourcing fosters resilience by providing multiple distribution paths connecting final buyers to the manufacturer. We validate the proposed model using data on drug shipments in the US. Drawing on these data, we reconstruct 22 nationwide pharmaceutical distribution networks. We demonstrate that the proposed model successfully replicates several structural features of the empirical networks, including their out-degree and path length distributions as well as their resilience and efficiency properties. These findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures.

Abstract Image

效率和复原力:配电网增长的主要驱动力
从原材料到食品和药品,商品流通网络是经济运行的支柱。它们是由连接全球制造商、分销商和最终买家的若干供应关系形成的。我们提出了一个基于网络的模型,用以描述分销网络出现和发展的内在机制。在我们的模型中,企业在建立新的供应关系时会考虑两种做法:集中化(倾向于选择联系紧密的合作伙伴)和多重采购(倾向于选择多个供应商)。集中化通过利用短分销路径来提高网络效率;多源化通过提供连接最终买家和制造商的多条分销路径来提高弹性。我们利用美国的药品运输数据验证了所提出的模型。根据这些数据,我们重建了 22 个全国性的药品分销网络。我们证明,所提出的模型成功地复制了经验网络的几个结构特征,包括它们的外度和路径长度分布,以及它们的弹性和效率特性。这些研究结果表明,所提出的企业级实践有效地捕捉到了导致观察到的结构的网络增长过程。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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