Using AgEnt based models and NEtwork AnalySis to assess supply chains criticalities


CALL: 2019

DOMAIN: SR - Environmental and Earth Sciences


LAST NAME: Larrea-Gallegos




KEYWORDS: Network Analysis, Agent-Based Modelling, Supply Chain Resiliency, Consequential Life Cycle Assessment, Hybrid Life Cycle Assessment

START: 2020-01-06

END: 2023-01-05


Submitted Abstract

Global exchanges of raw materials and products in supply chains (SC) are highly interconnected and vulnerable to failure at different scales. Analysis of criticality in the overall environmental assessment of SC is therefore an emerging research area. In Life Cycle Assessment (LCA), two approaches for criticality assessment have emerged in recent years: agent-based models (ABM) and network analysis (NA). The project will apply NA to explore and quantify the strength of the links among the nodes of the network supplying the studied product, and to assess the resiliency of this network to external perturbations. The quantitative relationships inferred from NA will be used to improve the definition of the behavioural (decision) rules in a ABM that will simulate the evolution of the market demand patterns for the product. These patterns will then translate into a specific Life Cycle Inventory and related expected environmental impacts that will be simulated using the LCA tool Brightway2. The advantage of the proposed methodology compared to scenario-based analysis is that expert knowledge is embedded in the variables used to describe and run the ABM model. Results are no longer entirely depending on expert assumptions.The objective of the project is to develop an operational methodology to identify criticalities in SC and to suggest mitigation strategies. It will be tested on the SC of two products: 1) production of feed for aquaculture based on varying ratios of fishmeal production (from Peru) and soybean (from Argentina); 2) steel production in Luxembourg.The first case study is strongly influenced by natural climatic events (e.g. El Niño- Southern Oscillation – ENSO), while the second is influenced by commercial relationships and market trends (fluctuations in demand, raw materials supply and prices).The two case studies are therefore emblematic of two important and different categories of causes of disruptions of SCs.

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