AN ADAPTIVE INTELLIGENT MODEL FOR MANAGEMENT DECISION-MAKING IN ENTERPRISES BASED ON ARTIFICIAL INTELLIGENCE
Keywords:
artificial intelligence; adaptive decision support; management decision-making; concept drift; fuzzy TOPSIS; reinforcement learning; explainable AI; enterprise managementAbstract
Background. Enterprises operate in volatile environments in which the statistical properties of data, customer behavior and market conditions change over time. Most AI-based decision-support systems are trained once and applied statically, so their recommendations degrade when conditions shift.
Objective. This study develops and evaluates an adaptive intelligent model (AIDM) for management decision-making that combines predictive learning, multi-criteria decision analysis, concept-drift detection, online reinforcement of criteria weights, and an explainable human-in-the-loop governance layer.
Methods. The model is formalized as a five-layer architecture. Predictions from an ensemble learner feed a fuzzy TOPSIS ranking of managerial alternatives; criteria weights are updated online from observed outcomes, and a drift detector triggers retraining and weight re-calibration. The model was tested in a simulated 36-month procurement and inventory-management environment with three regime shifts, against three baselines: a static rule-based policy, static AHP-TOPSIS, and a machine-learning-only recommender.
Results. AIDM achieved the highest cumulative utility and the lowest adaptation lag after each regime shift, and it reduced total operating cost relative to all baselines. Ablation showed that the drift-triggered adaptation module contributed most to the improvement.
Conclusion. Combining prediction, multi-criteria evaluation and online adaptation yields more robust managerial decisions in non-stationary environments than static or single-technique approaches, provided that explainability and managerial oversight are retained.
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