Volume 15 • Issue 1 • PP: 27–37 • 2027
From Demand Signals to Retail Decisions in Business Intelligence Research
Open Access & Copyright
© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Retail analytics has moved beyond improving point forecasts, yet evidence about the value of a forecast for pricing, replenishment, fulfillment, and managerial adoption remains dispersed. This structured review compares 36 journal studies published from 2023 through 2025, identified within a search window ending in January 2026, across four connected areas: demand forecasting (12), pricing and commercial levers (7), inventory and fulfillment (8), and decision systems (9). Each study is examined for its information setting, analytic approach, decision connection, and evidence boundary. The synthesis identifies four recurring discontinuities: sales data can conceal unmet demand; forecast scores can misrank policies; a policy that works in a model may be infeasible in a distributed retail organization; and staff may override recommendations whose value has not been made legible. A decision-centered evaluation structure links data availability, uncertainty estimation, feasible action, adoption, and realized economic outcomes. The paper develops a research agenda around observable-demand corrections, decision-calibrated uncertainty, joint pricing and fulfillment, transportable evaluations, and human-in-the-loop deployment. The review is deliberately bounded rather than a claim of exhaustive coverage or pooled effect sizes. It offers business intelligence researchers a common basis for studying whether analytical improvements survive the operational decisions they are meant to inform.
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References
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