Demand forecasting is an important activity that directly impacts the supply chain's functioning, offering a solid foundation for decision-making. The operational strategy has long focused on demand forecasting to manage inventories better and maximize customer satisfaction. However, most demand forecasting methods fail to reveal anything to businesses since they don't account for product seasonality, current market trends, or how forecasting affects the bullwhip effect. There is a pressing requirement to establish technologies capable of intelligently and swiftly examining massive amounts of data in the supply chain. Big Data may assist firms in resolving their issue. At the same time, Fuzzy Logic models help capture and manage uncertainty in situations lacking historical data, subjective consumer preferences, or unpredictable market circumstances. Hence, this paper proposes a Fuzzy Logic based Big Data Driven Demand Forecasting framework (FL-BDDF) that determines the role promotional marketing efforts, past demand, and other variables have in making predictions that can shape, sense, and react to actual consumer needs. With Big Data Analytics (BDA), businesses may enhance the accuracy of their demand forecasts. Fuzzy Logic lets them include qualitative indications like market sentiment, expert views, or subjective risk assessments with the typical quantitative information. Operations and Supply Chain Management (OSCM) is like any other field, providing several chances to create enormous amounts of data in realtime. This study's results may help academics and industry professionals better grasp the possibilities presented by Big Data for SCM and demand prediction. The experimental outcomes illustrate that the suggested FL-BDDF model increases the accuracy ratio by 98.4%, the supply chain forecasting ratio by 97.3%, the customer satisfaction level by 95.4%, and reduced cost by 57% compared to other existing models. © 2025 Elsevier B.V., All rights reserved.