Learning from Historical Transactions: Robust Supplier Pricing and Stocking with Sparse Data
Abstract
Upstream suppliers often set wholesale prices and stocking levels without direct access to detailed end-market demand information, which is instead held by downstream retailers. We study how a supplier can learn from a small number of historical transactions consisting of wholesale prices, retailer-chosen retail prices, and the resulting demand responses. We develop a robust, nonparametric framework that combines inverse optimization with distributionally robust optimization. Historical retailer decisions are used to restrict the set of demand distributions consistent with the observed transactions, after which the supplier optimizes against the least favorable demand response within this ambiguity set. We further accommodate imperfect retailer decisions through two error models (condition error and price error) and derive tractable worst-case response computations based on linear programs and one-dimensional price searches. Numerical experiments across six demand environments show that incorporating historical decision information can generate substantial profit improvements with only a few transactions and that these gains persist under moderate decision errors.
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BibTeX
@misc{chen2026learning,
title = {{Learning from Historical Transactions: Robust Supplier Pricing and Stocking with Sparse Data}},
author = {Zhiqiang Chen and Yi Cao and Caihua Chen},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.22027},
eprint = {2607.22027},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.22027},
}
RIS
TY - UNPB
TI - Learning from Historical Transactions: Robust Supplier Pricing and Stocking with Sparse Data
AU - Zhiqiang Chen
AU - Yi Cao
AU - Caihua Chen
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2607.22027
UR - https://arxiv.org/abs/2607.22027
AB - Upstream suppliers often set wholesale prices and stocking levels without direct access to detailed end-market demand information, which is instead held by downstream retailers. We study how a supplier can learn from a small number of historical transactions consisting of wholesale prices, retailer-chosen retail prices, and the resulting demand responses. We develop a robust, nonparametric framework that combines inverse optimization with distributionally robust optimization. Historical retailer decisions are used to restrict the set of demand distributions consistent with the observed transactions, after which the supplier optimizes against the least favorable demand response within this ambiguity set. We further accommodate imperfect retailer decisions through two error models (condition error and price error) and derive tractable worst-case response computations based on linear programs and one-dimensional price searches. Numerical experiments across six demand environments show that incorporating historical decision information can generate substantial profit improvements with only a few transactions and that these gains persist under moderate decision errors.
ER -