Serel, D. A.2016-02-082016-02-0820120925-5273http://hdl.handle.net/11693/21442Quick response mechanisms based on effective use of up-to-date demand information help retailers to reduce their inventory management costs. We formulate a single-period inventory model for multiple products with dependent (multivariate normal) demand distributions and a given overall procurement budget. After placing orders based on an initial demand forecast, new market information is gathered and demand forecast is updated. Using this more accurate second forecast, the retailer decides the total stocking level for the selling season. The second order is based on an improved demand forecast, but it also involves a higher unit supply cost. To determine the optimal ordering policy, we use a computational procedure that entails solving capacitated multi-item newsboy problems embedded within a dynamic programming model. Various numerical examples illustrate the effects of demand variability and financial constraint on the optimal policy. It is found that existence of a budget constraint may lead to an increase in the initial order size. It is also observed that as the budget available decreases, the products with more predictable demand make up a larger share of the procurement expenditure.EnglishBayesian estimateBudget constraintForecast updateInventoryMulti-product newsvendor problemBayesian estimateBudget constraintForecast updatesInventoryMulti-productsBayesian networksComputer programmingForecastingInformation managementInventory controlSalesBudget controlArticle10.1016/j.ijpe.2012.02.004