Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real time. We describe a family of statistical models that addresses these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These ¿regime¿ models are developed using statistical analysis of historical data and are used in real time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management, a supply chain environment characterized by competitive procurement, sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and long-term resource allocation decisions. Results show that our method outperforms more traditional short- and long-term predictive modeling approaches.