ADAPTIVE ENTERPRISE MANAGEMENT FORMATION FACTORS

Abstract

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The study explores adaptive enterprise management as a strategic framework for maintaining optimal efficiency in dynamic and uncertain environments. It was stated that modern technological platforms play a dual role, either accelerating or constraining adaptive management processes. On one hand, traditional data distribution across departments can limit real-time visibility required for timely and accurate economic judgments. On the other hand, cloud-based infrastructures with integrated data repositories empower managers to analyze aggregated indicators swiftly and adjust strategies based on updated patterns. As was declared, adaptive management in contemporary organizations faces challenges stemming from the complexity of data sources, such as transactional databases, consumer feedback, operational logs, and financial reports. Subsequently, these diverse and increasingly sophisticated data sources necessitate advanced analytical methodologies capable of extracting meaningful insights from unstructured text, high-dimensional numerical data, and time-series signals reflecting system performance. Relational databases, widely used in transactional processing and resource management, often rely on numerical and categorical metrics like query execution times and error counts. However, these metrics fail to capture the broader semantic context of issues documented in textual logs and feedback. BERT embeddings transform raw textual inputs, such as error messages or policy documentation, into high-dimensional vectors retaining semantic detail. Simultaneously, LSTM networks manage temporal dependencies in performance trends, selectively retaining long-term patterns while discarding irrelevant fluctuations. The combined feature vectors—comprising BERT embeddings and numerical metrics—are fed into LSTM layers to model fluctuations in database performance dynamically. The research emphasizes the critical role of real-time analytics and adaptive decision-making facilitated by advanced information architectures. It highlights the economic value of integrating data-driven insights into adaptive enterprise management, enabling rapid optimization of processes and resource allocation. However, technological advancements alone are insufficient without organizational readiness to act on insights, balanced incentives, and a culture of adaptability. The study underscores the balance between centralized oversight for compliance and decentralized decision-making for local adaptability. It was concluded, that while centralization supports risk management standardization, excessive control can stifle frontline innovation. Conversely, excessive decentralization risks incoherence and inefficiency. This work proposes an economic model that integrates organizational structures and technological infrastructures to improve decision-making processes in real-time.