Artificial intelligence powered predictions: enhancing supply chain sustainability

Emerging advanced digital technologies, such as Blockchain and artificial intelligence (AI), have had a substantial impact on performance improvement and operations optimization in industrial organizations. This study presents a network model designed for sustainable supply chains based on a real case study in the oil industry that deals with recursive outputs using data envelopment analysis (DEA) approach. When designing this network, recurrent loops are considered as factors that exit the stages and re-enter the previous stages as inputs. These factors should be designed in a way to minimize their generation while maximizing their utilization. The designed network model is then extended to a dynamic DEA model. Finally, the performance of supply chains is predicted and evaluated with the least error for future time periods using an explainable artificial neural network before they become inefficient. The findings indicate that a rise in undesirable outputs notably impacts the efficiency of decision-making units (DMUs) across different time periods. This paper's approach not only identifies these factors for forecasting trends in supply chain efficiency but also allows for the observation of the effects of research and development budget allocations as a dual-role factor influencing supply chain efficiency in future time frames. The model presented, which takes into account the interaction between time periods, provides managers with a framework to analyze the nature of each of these factors in the fluctuations seen in supply chain efficiency. This paper emphasizes the role of explainable AI in forecasting supply chain efficiency, enabling decision-makers to anticipate future trends beyond past performance. By integrating growth trends, progress rates, and current efficiency levels, this approach refines unit rankings. Analyzing projected efficiency trends, particularly in relation to investments in green research and development, highlights their significant impact on long-term supply chain performance. Managers can use these insights to allocate resources effectively and optimize strategies for sustained success.