Omnichannel pricing and inventory strategies considering live streaming selling: A data-driven distributionally robust optimization approach
Yuxia Mou, Hao Zhou, Xiaopeng Yang, Zhimin Guan
TL;DR
This paper proposes a data-driven optimization approach to manage pricing and inventory in omnichannel retail with live streaming selling, ensuring robust performance despite uncertain demand.
Contribution
A novel data-driven distributionally robust optimization model is developed to jointly optimize pricing, inventory, and fulfillment in omnichannel operations with live streaming.
Findings
The DRJCCP model based on WMQD shows superior out-of-sample performance and robust service levels.
Increasing live streaming efforts can enhance free-riding effects without necessarily boosting profits.
Numerical experiments confirm the model's efficiency and practicality in managing uncertainty.
Abstract
Recently, Live Streaming Selling (LSS) has become increasingly prevalent. Numerous omnichannel retailers are striving to introduce live streaming channel to absorb additional demand. However, it is challenging to investigate robust pricing and inventory strategies that consider the characteristics of omnichannel operations and LSS with uncertain demand. We consider a joint optimization of ordering, replenishment, order fulfillment, and pricing, where customers are sensitive to prices and delivery times. LSS can influence demand and benefit other channels to take free-riding. Furthermore, service level requirements are formulated as joint chance constraints to guarantee adequate performance. The Worst-case Mean Quantile-Deviation (WMQD) is employed to measure risks. The Wasserstein metric is adopted to design the data-driven ambiguity set. Accordingly, a data-driven Distributionally…
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Taxonomy
TopicsSupply Chain and Inventory Management · Consumer Market Behavior and Pricing · Advanced Queuing Theory Analysis