Order timing for seasonal products with demand learning and capacity constraints

Order time and order quantity of seasonal products significantly affect profits gained at the end of the period due to high demand uncertainty. Delaying order time enables a company to gain more information on demand, while decreasing the possibility of realizing the best order quantity due to capacity constraints. This thesis analyzes the problem of determining the best order time for a seasonal product manufacturer in an environment, where there exists a single opportunity for ordering and capacity is a decreasing function of the order time. Main feature of the study is utilizing demand information collected until the order time for resolving some portion of the demand uncertainty. A Bayesian update procedure is utilized to capture the essence of the gathered demand information. Three models are proposed for determining the order time, each having a different level of flexibility with respect to possible order times considered. Analytical results for structural properties, as well as extensive numerical results are obtained. A computational study is carried out in order to compare the performance of the models under different settings and to identify the conditions under which the demand learning is most beneficial.