Researchers at Washington State University have developed a new forecasting model that helps businesses more accurately estimate customer demand, even when data is incomplete. This innovative model leverages advanced algorithms and statistical methods to address the common issue of data gaps in demand forecasting. Traditional methods often rely heavily on extensive historical data, which may not be available when introducing new products or in rapidly changing market conditions. In contrast, the new model enhances accuracy and reliability by integrating multiple data sources and refining the underlying algorithms. According to the researchers, the model has been tested in a real-world retail setting, where it demonstrated significantly higher prediction accuracy compared to existing methods. They highlighted that precise demand forecasting can lead to more efficient supply chain management, reducing both overstock and stockout issues. This, in turn, enhances customer satisfaction and boosts profitability for businesses. The model's flexibility is one of its key strengths, allowing it to be tailored to the specific needs of different industries and products. For companies striving to maintain a competitive edge in the market, this tool offers a valuable advantage. The research team is actively collaborating with several businesses to further optimize the model and plans to roll out the technology in the coming months. This research, published in the International Journal of Production Research, brings together experts from information technology, statistics, and management science. The team's goal is to provide businesses with more precise market insights, enabling them to make better-informed decisions in an ever-changing market environment.