This thesis consists of three essays. The first essay is on quality disclosure for experience goods with customer bounded rationality. Deciding whether to disclose quality information is of strategic importance for firms. For experience goods, in practice, customers may not take a firm’s nondisclosure strategy as a “signal” to infer low quality. Under a nondisclosure strategy, customers tend to rely on the experiences of those who previously bought the product or experienced the service, and then deduce quality information based on a limited number of these samples (dubbed as “customer bounded rationality”). It remains unclear how customer bounded rationality affects a firm’s quality disclosure decision. We build a behavioral model to study firm incentives to disclose quality information of experience goods under customer bounded rationality in the sense of anecdotal reasoning. We find that a firm with a high or low quality level prefers not to disclose information on this quality, whereas a firm with a medium quality level prefers to do so. Our findings are consistent with some recent empirical evidences and provide a new explanation for the incomplete voluntary disclosure observed in many markets, particularly those for experience goods. Ignoring customer bounded rationality can lead to a significant profit loss. When there is congestion in the service context, the demand rate also plays a critical role. We also provide the managerial implications of our findings.The second essay establishes performance bounds on the minimum cost of a classic one-warehouse multi-retailer distribution system, in which any inventory replenishment at each location incurs a fixed-plus-variable cost and takes a constant lead time. The optimal policy is unknown and even if it exists, must be extremely complicated. The goal of this essay is to identify an easy-to-compute heuristic policy within the class of modified echelon (r, Q) policies that does not require an integer-ratio property or a synchronized, nested ordering property, yet has certain performance bounds. We first develop a cost upper bound for any given modified echelon (r, Q) policy. Computation of the bound does not require an exact evaluation of the system-wide cost, which is notoriously difficult. We next adopt parameters of the heuristic by minimizing the cost upper bound, which is equivalent to solving a set of independent single-stage (r, Q) systems. With a cost lower bound that has been established in the literature, we then develop easy-to-computeperformance bounds for the heuristic policy. Finally, using those bounds, we show that the proposed modified echelon (r, Q) heuristic policy is asymptotically optimal as a pair of system parameters is scaled up, e.g., when the ratios of the fixed cost of the warehouse over those of the retailers become large. Numerical study demonstrates that our proposed heuristic performs well and tends to outperform the echelon-stock (r, nQ) heuristic policy studied in the literature.The third essay is on inventory control for a single-item periodic-review stochastic inventory system with both minimum order quantity (MOQ) and batch ordering requirements. In each time period, the firm can order either none or at least as much as the MOQ. At the same time, if an order is placed, the order quantity is required to be an integral multiple of a given specific batch size. We first adopt a heuristic policy which is specified by two parameters (s, k). Applying a discrete time Markov chain approach, we compute the system cost and optimize this (s, k) policy under the long-run average cost criterion. We also consider a simpler one-parameter policy, theso-called S policy, which is a special case of the (s, k) policy. In an intensive numerical study, we find that 1) both policies perform well in comparison with other policies; and 2) the S policy also performs well and is compatible with the (s, k) policy; only in a few cases where demand variation is small, the latter outperforms the former significantly. We also evaluate the effects of some important parameters on system performance.