Optimal stopping problems determine the time to terminate a process to maximize expected rewards. Such problems are pervasive in the areas of operations management, marketing, statistics, finance, and economics. This dissertation provides a method that characterizes the structure of the optimal stopping policy for a general class of optimal stopping problems. It also studies two important optimal stopping problems arising in Operations Management. In the first part of the dissertation, we provide a method to characterize the structure of the optimal stopping policy for the class of discrete-time optimal stopping problems. Our method characterizes the structure of the optimal policy for some stopping problems for which conventional methods fail. Our method also simplifies the analysis of some existing results. Using the method, we determine sufficient conditions that yield threshold or control-band type optimal stopping policies. The results also help characterize parametric monotonicity of optimal thresholds and provide bounds for them. In the second part of the dissertation, we first generalize the Martingale Model of Forecast Evolution to account for multiple forecasters who forecast demand for the same product. The result enables us to consistently model the evolution of forecasts generated by two forecasters who have asymmetric demand information. Using the forecast evolution model, we next study a supplier's problem of eliciting credible forecast information from a manufacturer when both parties obtain asymmetric demand information over multiple periods. For better capacity planning, the supplier designs and offers a screening contract that ensures the manufacturer's credible information sharing. By delaying to offer this incentive mechanism, the supplier can obtain more information. This delay, however, may increase (resp., or decrease) the degree of information asymmetry between the two firms, resulting in a higher (resp., or lower) cost of screening. The delay may also increase capacity costs. Considering all such trade-offs, the supplier has to determine how to design a mechanism to elicit credible forecast information from the manufacturer and when to offer this incentive mechanism. In the last part of the dissertation, we study a manufacturer's problem of determining the time to introduce a new product to the market. Conventionally, manufacturing firms determine the time to introduce a new product to the market long before launching the product. The timing decision involves considerable risk because manufacturing firms are uncertain about competing firms' market entry timing and the outcome of production process development activities at the time when they make the decision. As a solution for reducing such risk, we propose a dynamic market entry strategy under which the manufacturer makes decisions about market entry timing and process improvements in response to the evolution of uncertain factors. We show that the manufacturer can reduce profit variability and increase average profit by employing this dynamic strategy. Our study also characterizes the industry conditions under which the dynamic strategy is most effective.