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Data-Driven Stochastic Dynamic Pricing and Ordering

In: Operations Research Proceedings 2018

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  • Rainer Schlosser

    (Hasso Plattner Institute)

Abstract

In many markets, firms use data-driven dynamic pricing and ordering strategies to increase their profits. To successfully manage both inventory levels as well as offer prices is a highly challenging task as (i) demand is typically uncertain and (ii) optimized pricing and ordering decisions are mutually dependent. In this paper, we analyze stochastic dynamic joint pricing and ordering models for the sale of durable goods. In a first step, a data-driven approach is used to estimate demand intensities and to quantify sales probabilities. In a second step, we use a dynamic programming model to compute optimized feedback pricing and ordering strategies. We are able to study the impact of ordering costs, inventory holding costs, and a delay in delivery. Further, we discuss potential extensions of the model proposed.

Suggested Citation

  • Rainer Schlosser, 2019. "Data-Driven Stochastic Dynamic Pricing and Ordering," Operations Research Proceedings, in: Bernard Fortz & Martine LabbĂ© (ed.), Operations Research Proceedings 2018, pages 397-403, Springer.
    • Handle: RePEc:spr:oprchp:978-3-030-18500-8_49
    DOI: 10.1007/978-3-030-18500-8_49
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