A Review of Contemporary Forecasting in Strategic Management: Models, Trends, and Applications

This study employs a systematic literature review based on PRISMA guidelines to analyze and synthesize contemporary forecasting research in strategic management. A total of 47 peer-reviewed articles published between 2015 and 2025 were examined to identify conceptual, technological, and applied developments in the field. While the review primarily focuses on recent studies, selected classical works were incorporated to strengthen the theoretical foundation.The study aims to trace the transition of forecasting from traditional statistical extrapolation methods to adaptive, data-driven, and intelligent models, while identifying critical theoretical gaps. Findings indicate that the literature has evolved along three interconnected streams: traditional quantitative models, strategic foresight and scenario planning, and AI-driven approaches utilizing machine learning and big data analytics. Since 2018, research attention has increasingly shifted toward predictive algorithms for risk analysis, organizational performance, and strategic decision support. Despite rapid technological advances, the integration of these tools into strategic management theory remains limited. The study proposes a conceptual framework to support integrative forecasting and proactive decision-making in dynamic environments.