Bayesian Inventory Planning with Imperfect Demand Estimation in Online Flash Sale

Ted Tao Yuan, Michelle Cai, Daniel Kao

Abstract

Daily deal, or flash sale, websites offer limited quantity of selected brands and products for a short period of time. The idea is that short-term sales event of branded products drives consumer interest. Flash sale sites like vip.com negotiate great deals from various vendors on a limited quantity of selected products. In operation, all merchandises need to be allocated to regional warehouses before a short-term sales event starts. The variety and quantity of merchandises change significantly from one sales event to another. Unsold items are typically shipped back to vendors after the sales event ends. In this paper, we discuss the design and implementation of a regional warehouse merchandise allocation model and strategy to maximize sales conversion rate. Our work reveals the uniqueness of inventory planning of flash sale and its similarity to that of general online retailers. Our machine learning prediction models and Bayesian Updating strategy are highly valuable to the improvement of regional warehouse efficiency and customer experience in dealing with highly volatile flash sale inventory.

References

  1. O'Sullivan, A. and Sheffrin, S., 2005. Economics: Principles in Action. Prentice Hall.
  2. DeGroot, M. and Schervish, M., 2002. Probability and Statistics (third ed.). Addison-Wesley.
  3. Savino, J., 2011. Inventory to Support Flash Sales Environments, MultiChannel Merchant (available at http://multichannelmerchant.com/opsandfulfillment/inv entory-to-support-flash-sales-environments-02022011/).
  4. Ghiani, G., Laporte G., Musmanno R., 2004. Introduction to Logistics Systems Planning and Control. John Wiley & Sons.
  5. Stevenson, W. J., 2009. Operations Management (10th edition). McGraw-Hill Education.
  6. Malakooti, Behnam, 2013. Operations and Production Systems with Multiple Objectives. John Wiley & Sons.
  7. Edgeworth, F., 1888. The Mathematical Theory of Banking. J. Royal Statistical Society. 51,113-127.
  8. Arrow, K., Harris, T., Marshak, J., 1951. Optimal Inventory Policy, Econometrica.
  9. Gallego, G. and I. Moon, 1993. The Distribution Free Newsboy Problem: Review and Extensions. Journal of Operational Research Society. 44, 825-834.
  10. Friedman, J., 1999. Greedy Function Approximation: A Gradient Boosting Machine. (available at http://wwwstat.stanford.edu/jhf/ftp/trebst.pdf)
  11. Hau L. Lee, V. Padmanabhan, and Seungjin Whang, 1997. Information distortion in a supply chain: The bullwhip effect. Mangement Science, 43(4):546-558, Apr. 1997.
  12. Gelman, A., Carlin, J., Stern, H., Rubin, D., 2003. Bayesian Data Analysis, Chapman and Hall/CRC, 2nd edition.
  13. Pearl, J., 1994. A probablistic calculus of actions. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Seattle. Morgan Kaufmann, 454-462.
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Paper Citation


in Harvard Style

Tao Yuan T., Cai M. and Kao D. (2016). Bayesian Inventory Planning with Imperfect Demand Estimation in Online Flash Sale . In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-171-7, pages 343-348. DOI: 10.5220/0005632303430348


in Bibtex Style

@conference{icores16,
author={Ted Tao Yuan and Michelle Cai and Daniel Kao},
title={Bayesian Inventory Planning with Imperfect Demand Estimation in Online Flash Sale},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2016},
pages={343-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005632303430348},
isbn={978-989-758-171-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Bayesian Inventory Planning with Imperfect Demand Estimation in Online Flash Sale
SN - 978-989-758-171-7
AU - Tao Yuan T.
AU - Cai M.
AU - Kao D.
PY - 2016
SP - 343
EP - 348
DO - 10.5220/0005632303430348