Artificial Intelligence and Big Data Analytics for Supply Chain Sustainability

Business sustainability is turning from a leading competitive advantage into an absolute necessity. Companies are increasingly adopting sustainable practices in their supply chains due to mounting pressure from consumers, governments, and investors. These may be different strategies to minimize environmental impact, ensure prospective economic stability, or contribute to the social well-being of vulnerable groups of people. This report applies the semi-systematic literature review method to explore the potential of integrating artificial intelligence (AI) and big data analytics (BDA) technologies to improve sustainability in supply chains. AI and BDA are increasingly asserting themselves as leading solutions for the sustainability of today’s complex supply chains. They affect the three main dimensions of supply chain sustainability— environmental, social, and economic. Thanks to AI, companies make more accurate forecasts, more efficient production and logistics models, and, as a result, minimize stocks, waste, and excess production, reduce fuel consumption, and reduce harmful emissions. Big data (BD) and BDA technologies increase the quality of management decisions and contribute to improving the consistency and transparency of processes in complex supply chains. Despite the very good start, the implementation of AI and BD to achieve supply chain sustainability requires serious research. This implementation is still in its early stages of development and has faced several challenges. At the current stage, results have been achieved in terms of economic sustainability, and more serious ones are expected in other areas as well.