<![CDATA[This study presents a Systematic Literature Review (SLR) on demand forecasting and its role in managerial decision-making. Articles published between 2015 and 2025 were collected from Scopus, Web of Science, Dimensions, and Google Scholar using Publish or Perish software. Following PRISMA guidelines, eleven relevant studies were selected. Findings reveal that traditional time series methods such as ARIMA and exponential smoothing are still widely applied, while machine learning and hybrid models are increasingly used for higher accuracy in complex demand patterns. Bibliometric analysis with VOSviewer identified four main research clusters: model development, forecasting methods, managerial approaches, and sectoral applications. The study concludes that demand forecasting is not only a technical tool but also a strategic instrument to enhance managerial decision-making. Keywords: decision-making, demand forecasting, machine learning, managerial economics, systematic literature review.]]>
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