Accelerating Probabilistic Forecasting: A GPU-Based Approach to Reducing Computational Time

High-performance computing based on general-purpose graphical processing units (GPUs) is a powerful tool for reducing computational time. In a context where big data is becoming increasingly relevant, GPUs may play a crucial role. This study analyzes the performance of GPUs by implementing probabilistic forecasts based on single exponential smoothing combined with simulated predictive distributions. In supply chain environments, companies must generate a large number of forecasts at the SKU level. Therefore, reducing computational time can provide a significant competitive advantage. Since forecasts are typically computed independently for each SKU, the problem is naturally parallelizable, making it well-suited for GPU computing. To the best of the authors’ knowledge, this is the first study to apply GPU computing to demand forecasting in a supply chain context. First, we show how to adapt typical probabilistic forecasting algorithms to a parallel computing framework. Then, real data from a manufacturing company are used to compare GPU and traditional CPU implementations.Theresults indicate that GPUs can deliver computational speedups ranging from 28 to 42 times relative to CPU-based implementations.