Abstract: Accurate and efficient prediction of outbound volume of product oil is the source and basis of scientific management for product oil inventory. In order to increase the inventory efficiency and lower the inventory cost of product oil, 166 product oil depots of a company in the western region were studied to analyze the influencing factors of inventory management on the base of the related research results of inventory management and sales prediction. Thereby, the multi-stage prediction algorithm of dual-time granularity was designed, and the algorithm was integrated with the time series model algorithm library of multi-model as well as Mamdani's fuzzy inference systems to predict the monthly and daily outbound volume of product oil and conduct the quantitative analysis. As indicated by the results, the proposed algorithm and the algorithm designed by the model library could automatically select the appropriate model according to the data characteristics of the outbound volume, as well as match and predict the outbound volume of the oil depot on large scale with high quality in short time. Generally, the median of the average absolute percentage error of the prediction results is higher than 85%, the prediction confidence is close to 95%, and the average accuracy of the monthly outbound volume prediction of the application cases can reach 90%. Conclusively, the research results could provide scientific recommendations for the decision-making on inventory management of oil depot, and have practical significance for establishing a scientific and efficient modern oil supply and logistics system.