2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 14 , Issue 2 , PP: 26–36, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ilhan Ozturk 1 *
Doi: https://doi.org/10.54216/AJBOR.140203
Business intelligence has emerged to be a high-level managerial competency among organizations that aim to enhance the quality of planning, responsiveness in operations and evidence-based decision making in uncertain market environments. Short-term demand forecasting is one of its most important business applications since fluctuations in demand expectations affect budgeting, inventory planning, staffing, procurement timing and revenue management. The paper formulates and tests a business intelligence system of consumer demand prediction over short-term with the help of the public macroeconomic variables. It aims to show how external economic signals may be converted into an explainable, reproducible, and useful forecasting layer to be used in dashboards and decision support systems. The research forecasts next-period real consumer spending using lagged indicators based on output, disposable income, investment, unemployment, inflation, and short-term interest rates using a publicly available U.S. macroeconomic data, which is periodically updated. Ordinary least squares, ridge regression, random forest and gradient boosting are compared by using a chronological holdout design. The empirical findings indicate that the regression-based models that are interpretable have the best out-of-sample performance, and ordinary least squares model has the lowest error and greatest explanatory power. The results suggest that effective business forecasting support can be offered using transparent analytics without the need to use complex black-box models. The study is valuable because it adds to the body of business intelligence literature a reproducible external-signal prediction pipeline, a comparison of the explainable and non-explainable models in a management context, and a translation of the forecasting results into operational and strategic planning consequences.
Business intelligence , business analytics , Demand forecasting , Decision support , Predictive analytics , Consumer demand
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