Articles
DOI DOI: 10.5281/zenodo.18442592

Inventory and Pricing with AI Forecasting: Robust vs. Adaptive Policies

Mehmet A. Begen1
  1. 1 Western University, Ivey Business School, London, Canada

Abstract

Background: AI-driven demand forecasting expands the signal space for inventory
and pricing decisions, enabling faster reactions to market changes. However,
forecast error, non-stationarity, and distribution shifts raise a governance question:
should decisions be designed to be robust to uncertainty, or adaptive to feedback?
Methods: This review integrates inventory control, probabilistic demand forecasting,
dynamic pricing, and robust optimization into a unified decision architecture. We organize
prior findings around a closed-loop cycle: data ingestion, forecasting (point and distribution),
policy selection (robust/adaptive/hybrid), execution, monitoring, and recalibration.
Results: Robust policies protect against tail risk by optimizing over uncertainty sets
and worst-case scenarios, but may be conservative and costly in stable environments.
Adaptive policies leverage frequent feedback to improve average performance, yet
can become unstable under regime changes, delayed signals, or strategic customer
responses. The synthesis supports a hybrid design: adaptive learning within robust
guardrails (service constraints, pricing move limits, and inventory safety floors).
Conclusions: The practical frontier is not “robust versus adaptive” as a binary choice.
Best practice is layered: robust feasibility and risk limits at the outer layer, with adaptive
learning tuned inside auditable constraints. Future research should prioritize regimeswitching demand, decision-focused learning, and explainable pricing and replenishment
rules.

How to Cite

Begen, M. A. (2026). Inventory and Pricing with AI Forecasting: Robust vs. Adaptive Policies. Transnational Academic Journal of Economics, 2(2), 145–151. https://doi.org/10.5281/zenodo.18442592
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