A Comprehensive Analysis of Demand Prediction Models in Supply Chain Management

Abstract

The capacity to provide accurate demand projections is essential for supply chain management to work well. Because their projections influence important choices at every stage of the supply chain, including sourcing raw materials, manufacturing, transportation, and inventory management, the demand team bears a great deal of obligation. Precise demand projections greatly impact customer satisfaction and organizational efficiency in general, ultimately saving money and effort. Even though they are essential, historical demand and sales statistics frequently fall short. Traditional statistical models neglect to account for complex variables and do not have the necessary accuracy. In demand forecasting, advertisements are often underutilized while being a significant source of complexity. Forecast accuracy is improved by combining time series approaches with expert opinion and data from unique occurrences. This research aims to develop a thorough grasp of demand planning and offer workable methods to improve supply chain oversight. Our technique includes both quantitative and qualitative study design methodologies developed from comprehensive data obtained through published examination of secondary sources and critical observation. The collected data is analyzed, and conclusions are drawn from our data analysis using a variety of data analysis methodologies, such as statistical, thematic, and content assessment. Not only are different approaches to model construction highlighted, but study comparisons and recommendations are also examined, along with a review of the literature gathered from various academic publications and papers. The study recognizes the inherent difficulties in demand planning, such as the requirement for sophisticated ideas and applications, a trained labor force, and the coordination of deliberative techniques with supply chain and logistical procedures. In conclusion, this paper seeks to improve logistical and supply chain operations by tackling current issues, offering insights into demand factors, refining demand forecasting models, and presenting helpful suggestions to companies on maximizing demand planning procedures to in-How to cite this paper: Badr, H., & Ahmed, W.