Resilience of Circular Supply Chains: Analyzing the Impact of Take-Back Strategies via Efficient Computation of Post-Disturbance Equilibrium

Francesco Cafforio, Sang Woo Park, Ilaria Giannoccaro, Layek Abdel-Malek

Research output: Contribution to journalArticlepeer-review

Abstract

Circular supply chains (CSCs) are designed to be restorative and regenerative, recapturing value from end-of-life resources to increase efficiency and extend product lifecycles. Take-back systems play a crucial role in many CSCs by enabling the recovery and reintegration of waste and by-products from customers and other supply chains (SCs), creating closed and open loops. Since disruptions affect CSCs differently than linear SCs, it is critical to investigate their resilience. This paper addresses this issue by providing a novel conceptualization of CSCs resilience, which integrates the traditional approach focused on the ability to preserve demand fulfillment with a new emerging definition recognizing the need to ensure the circularity function in the face of disruptions. We develop an optimization-based mathematical framework that computes post-disruption equilibrium states in CSCs, offering a general methodology applicable across various CSC configurations. To demonstrate the utility of this approach, we apply it to CSCs implementing take-back strategies and analyze how these strategies affect resilience. A case study involving a multinational fashion group known for its circular economy initiatives is used to explore two take-back system configurations—open and closed loops—and related incentive mechanisms. Numerical simulations under different disruption scenarios provide managerial insights into designing more resilient CSCs through strategic adoption of take-back systems.

Original languageEnglish (US)
JournalSustainable Development
DOIs
StateAccepted/In press - 2025

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Development

Keywords

  • circular supply chains
  • optimization-based mathematical model
  • supply chain resilience

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