Abstract
One of the biggest challenges that the current manufacturers and retailers are facing is to manage huge varieties of short lifecycle items to meet various consumer preferences. This study proposes an inventory management policy with a demand forecasting algorithm. In this algorithm, the discrete probability distribution is assumed and the parameters to define the distribution are estimated dynamically so that it adapts demand pattern changes during the sales season. The proposal is evaluated with the sales data disclosed by a real company and shows that the policy successfully increases the expected profit of the company.
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