Article ID: | iaor201524697 |
Volume: | 23 |
Issue: | 3 |
Start Page Number: | 333 |
End Page Number: | 347 |
Publication Date: | Mar 2014 |
Journal: | Production and Operations Management |
Authors: | Van Mieghem Jan A, Huang Tingliang |
Keywords: | inventory, management, e-commerce, retailing, decision, simulation |
We consider firms that feature their products on the Internet but take orders offline. Click and order data are disjoint on such non‐transactional websites, and their matching is error‐prone. Yet, their time separation may allow the firm to react and improve its tactical planning. We introduce a dynamic decision support model that augments the classic inventory planning model with additional clickstream state variables. Using a novel data set of matched online clickstream and offline purchasing data, we identify statistically significant clickstream variables and empirically investigate the value of clickstream tracking on non‐transactional websites to improve inventory management. We show that the noisy clickstream data is statistically significant to predict the propensity, amount, and timing of offline orders. A counterfactual analysis shows that using the demand information extracted from the clickstream data can reduce the inventory holding and backordering cost by 3% to 5% in our data set.