IoT-Enabled Demand Forecasting in Supply Chain Management: A Bibliometric Analysis and Future Directions

The Internet of Things (IoT) is changing how organizations forecast product demand by enabling real-time data collection through connected sensors. Despite growing interest in this area, research on IoT-enabled forecasting remains scattered across multiple disciplines, making it difficult to identify key contributions and overarching trends. To address this gap, a bibliometric analysis was conducted using Biblioshiny software to answer two research questions: (1) Which contributions have been most influential in IoT-based demand forecasting for supply chains? and (2) What emerging topics are likely to shape future developments in the field? The PRISMA methodology was applied to gather and screen articles from the Scopus database. The analysis includes publication trends, leading authors, top journals, highly cited documents, contributing institutions, and countries. Keyword analyses are presented through word clouds, trend-topic maps, conceptual structure diagrams, and thematic evolution plots. The findings offer a structured overview of the field’s development and propose future directions, including sustainable forecasting practices and greater interdisciplinary collaboration. Keywords Internet of Things, Supply Chain Management, PRISMA, Bibliometric Analysis, Demand Forecasting.