This study explores the application of neural networks to predict product delivery times in procurement processes, utilizing a large synthetic dataset. As timely delivery is crucial for supply chain efficiency, accurate prediction of procurement timelines can significantly enhance operational planning and resource allocation. Our research employs a multi-layer neural network model trained on a synthetically generated dataset of 1 million entries. The dataset incorporates key procurement attributes including purchase value, complexity, procurement method, product type, number of potential suppliers, urgency, organizational size, team experience, budget availability, geographical location, season, and industry sector. By using synthetic data, we overcome common limitations in procurement research such as data scarcity and confidentiality issues, while still capturing the complex interrelationships between variables. The neural network model demonstrates promising results in predicting delivery times, outperforming traditional linear regression models. Our findings suggest that certain attributes, such as complexity, procurement method, geographical location and budget availability have a more significant impact on delivery time predictions. The study also highlights the potential of machine learning techniques in procurement analytics and decision support. While based on synthetic data, this research provides a foundation for future studies using real-world procurement data. It also offers insights into the key factors influencing procurement timelines and demonstrates the potential of neural networks in enhancing procurement efficiency.