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Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19.

Ipek KazancogluMelisa Ozbiltekin-PalaSachin Kumar ManglaAjay KumarYigit Kazancoglu
Published in: Annals of operations research (2022)
In rapidly changing business conditions, it has become extremely important to ensure the sustainability of supply chains and further improve the resiliency to those events, such as COVID-19, that can cause unexpected disruptions in the value supply chain. Although globalized supply chains have already been criticized for lack of control over sustainability and resilience of supply chain operations, these issues have become more prevalent in the uncertain environment driven by COVID-19. The use of emerging technologies such as blockchain, Industry 4.0 analytics model and artificial intelligence driven methods are aimed at increasing the sustainability and resilience of supply chains, especially in an uncertain environment. In this context, this research aims to identify the problematic areas encountered in building a resilient and sustainable supply chain in the pre-COVID-19 era and during COVID-19, and to offer solutions to those problematic areas tackled by an appropriate emerging technology. This research has been contextualized in the automotive industry; this industry has a complex supply chain structure and is one of the sectors most affected by COVID-19. Based on the findings, the most important problematic areas encountered in SSCM pre-COVID-19 are determined as supply chain traceability, demand planning and production management as well as purchasing process planning based on cause and effect groups. The most important issues to be addressed during COVID-19 are top management support, purchasing process planning and supply chain traceability, respectively.
Keyphrases
  • coronavirus disease
  • sars cov
  • artificial intelligence
  • machine learning
  • big data
  • climate change
  • respiratory syndrome coronavirus
  • depressive symptoms
  • deep learning