Huang, T;
Van Mieghem, JA;
(2014)
Clickstream Data and Inventory Management: Model and Empirical Analysis (Singhal, K, Trans.).
Production and Operations Management
, 23
(3)
pp. 333-347.
10.1111/poms.12046.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Clickstream Data and Inventory Management: Model and Empirical Analysis |
| Location: | USA |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/poms.12046 |
| Publisher version: | http://dx.doi.org/10.1111/poms.12046 |
| Additional information: | © 2013 The Authors. Production and Operations Management published by Wiley Periodicals, Inc. on behalf of Production and Operations Management Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | click tracking; advance demand information; inventory theory and control; empirical research; dynamic programming; econometric analysis; big data; |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1381246 |
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