In the heart of Saudi Arabia, a researcher is revolutionizing how industries predict and mitigate supply chain disruptions. Waleed Abdu Zogaan, from the Department of Computer Science at Jazan University, has developed a deep learning framework that could redefine supply chain resilience (SCR) across critical sectors, including energy. His work, published in the Journal of Big Data, offers a glimpse into a future where artificial intelligence (AI) fortifies supply chains against operational shocks.
Imagine a world where energy providers can anticipate demand fluctuations with pinpoint accuracy, ensuring a steady power supply even during peak times or unexpected surges. This is not a distant dream but a reality that Zogaan’s research brings closer. By leveraging deep learning (DL) models, his framework predicts supply chain risks, providing actionable insights to enhance resilience.
The energy sector, with its complex and dynamic supply chains, stands to gain significantly from this innovation. Traditional methods of demand forecasting often fall short, leading to inefficiencies and potential blackouts. However, Zogaan’s DL models, particularly the artificial neural networks (ANN), have shown remarkable promise in predicting electricity needs. “The ANN model performed exceptionally well in the energy sector,” Zogaan explains, “offering superior accuracy in demand forecasting compared to traditional machine learning models.”
But how does this translate into commercial impacts? For energy companies, accurate demand prediction means optimized resource allocation, reduced operational costs, and enhanced customer satisfaction. It means fewer surprises and more control over an inherently volatile market. “This research provides firms with a valuable tool to predict future disruptions,” Zogaan asserts, “improving resilience by identifying potential risks and taking proactive measures to address them.”
The implications extend beyond the energy sector. In the pharmaceutical industry, DL models optimized drug inventory and logistics, reducing wastage and stock-outs. For agriculture, they predicted food demand, ensuring efficient supply management. In the automotive industry, they forecasted car demand, demonstrating the versatility and broad applicability of Zogaan’s framework.
The study applied five DL models—recurrent neural networks (RNN), long-short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), and artificial neural networks (ANN)—across four key case studies. The results were striking. The CNN model achieved the highest accuracy, with 99.3% in the pharmaceutical case study. In the food and automotive sectors, the GRU model had the lowest mean absolute error and mean squared error, showcasing the diverse strengths of different DL models.
As we look to the future, Zogaan’s research paves the way for more resilient and efficient supply chains. It challenges us to rethink our approach to risk management, embracing AI and deep learning as powerful tools in our arsenal. For the energy sector, this means a future where demand is met with precision, where disruptions are anticipated and mitigated, and where resilience is not just a goal but a guarantee. The journey towards this future has begun, and it is led by innovators like Waleed Abdu Zogaan, who are reshaping the landscape of supply chain management with their groundbreaking work published in the Journal of Big Data.
