Efficiency and resilience: Key drivers of distribution network growth
- Ambra Amico , Giacomo Vaccario , Frank Schweitzer
- Pharmaceuticals , Economic networks , Resilience , Data driven models , Agent based models , Supply chain
- August 23, 2024 Official Link
Distribution networks, which supply goods from manufacturers to final buyers, are crucial for a functioning economy. A network model is proposed to describe the emergence and growth of these networks. The model considers two firm-level practices: centralization and multi-sourcing. Centralization enhances network efficiency by using short distribution paths, while multi-sourcing fosters resilience by providing multiple distribution paths. The model was validated using data on drug shipments in the US, and it 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. The findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures.
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.
Why This Matters for Scientists
You may want to consider using a network growth model to describe the emergence and growth of distribution networks. This model can help you understand the impact of firm-level practices on network efficiency and resilience.
Quick Technical Overview
The model is based on two fundamental necessities of distribution networks: efficiency and resilience. Efficiency is the ability to deliver goods to final buyers in a timely and cost-effective manner, while resilience is the ability to withstand, adapt, and recover from disruptions. The model formalizes these practices as interaction rules for link formation and uses them to explain the growth of large-scale distribution networks.
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.
Summary for Policy Makers
The proposed model has implications for policy and stakeholders. Distribution networks are critical for the supply of essential goods, and their efficiency and resilience are crucial for ensuring the smooth functioning of the economy. The model can help policymakers and stakeholders understand the impact of firm-level practices on network efficiency and resilience and inform decision-making.
The findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures.
Disclaimer
The above summaries were generated with the assistance of an AI system.
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.
