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An R-Based Landscape Validation of a Competing Risk Model

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Increasing supply chain resilience through efficient redundancy allocation: a risk-averse mathematical model.

Aldrighetti Riccardo¹, Battini Daria¹, Ivanov Dmitry²

¹Department of Management and Engineering, University of Padua, Stradella San Nicola, 3, 36100. Vicenza, Italy.

Ifac-Papersonline

|April 15, 2024

Summary

This study introduces a resilient supply chain (SC) model to minimize costs during disruptions. Proactive investments and recovery actions enhance network stability against risks like the COVID-19 pandemic.

Keywords:

COVID-19 disruption risk efficient redundancy allocation resilient supply chain risk-averse mathematical model supply chain network design

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Area of Science:

Operations Research
Supply Chain Management
Risk Management

Background:

The COVID-19 pandemic highlighted supply chain (SC) vulnerabilities and the need for resilience.
Uncertainty and disruption events pose significant risks to SC network design and planning.

Purpose of the Study:

To develop a risk-averse mathematical model for designing and planning resilient two-echelon SC networks.
To minimize total costs by optimizing facility location, capacity, flow allocation, and resilience strategies.

Main Methods:

A mixed-integer nonlinear programming model was formulated to incorporate disruption events.
Computational experiments and a numerical example were used to test the solution procedure.
Analysis focused on various disruption configurations, including long-term crises.

Main Results:

Recovery activities are most effective for short-term SC disruptions.
Proactive investments in protection systems and flexibility enhance SC resilience.
Resilience strategies can manage disruptions with minimal increases in network costs and overcapacity.

Conclusions:

The proposed model provides a framework for building robust and cost-effective SC networks.
Strategic resilience investments are crucial for mitigating the impact of SC disruptions.
The findings offer managerial insights for SC planning in uncertain environments.