Year: 2024
Type: article
Abstract: Achieving net-zero emissions is one of the most challenging goals of our time, requiring large-scale integration of renewable energy (RE) into national energy supply chains. This demands new competencies for firms to preserve their competitive advantages in rapidly evolving market environments, often called dynamic capabilities. A promising technology for integrating, scaling, and diffusing renewable energy within energy supply chains is artificial intelligence. However, the literature on AI-renewable energy supply chains is still in its early stages and often lacks broader theoretical development or managerial insights. In response, we introduce a theoretical framework to identify and develop AI-based dynamic capabilities in renewable energy supply chains through a case study approach. Supply chain predictability and optimization, key components of sensing and seizing capabilities, are crucial for developing effective renewable energy supply chains. Our study provides valuable insights also for practitioners aiming to establish AI-driven renewable energy supply chains.
Author: Gabriele Mauro
Institution: Université Libre de Bruxelles
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
Funders: European Commission, Innoviris
OpenAlex ID: W4404486625