A Survey on Data-Driven Demand Forecasting and Decision Support Systems for Agrochemical Supply Chain Management
TL;DR
This survey presents a comprehensive review of recent research on data-driven demand forecasting and decision support systems in agrochemical supply chain management and compares the strengths, limitations, application domains, and performance characteristics of existing approaches while identifying current research gaps and practical challenges.
Abstract
Demand forecasting plays a pivotal role in enhancing the efficiency, sustainability, and profitability of agrochemical supply chain management. Accurate demand prediction enables organizations to optimize inventory levels, minimize operational costs, reduce product shortages, and improve customer satisfaction. Traditional statistical forecasting methods have long been employed for demand estimation; however, the increasing complexity of agricultural markets, seasonal variations, climate uncertainties, and dynamic consumer demand have encouraged researchers to adopt advanced forecasting techniques. In recent years, Machine Learning (ML), Deep Learning (DL), and time-series forecasting models such as ARIMA and Facebook Prophet have demonstrated significant improvements in forecasting accuracy and decision-making capabilities. Simultaneously, Decision Support Systems (DSS) integrated with Enterprise Resource Planning (ERP) platforms have transformed supply chain operations by enabling real-time monitoring, inventory optimization, and data-driven production planning. This survey presents a comprehensive review of recent research on data-driven demand forecasting and decision support systems in agrochemical supply chain management. The study systematically analyzes statistical forecasting approaches, time-series models, machine learning algorithms, deep learning architectures, and intelligent decision support frameworks adopted by researchers over recent years. Furthermore, the survey compares the strengths, limitations, application domains, and performance characteristics of existing approaches while identifying current research gaps and practical challenges. Finally, future research directions involving hybrid forecasting models, explainable artificial intelligence, Internet of Things (IoT), cloud computing, and digital twin technologies are discussed to highlight emerging opportunities for intelligent and sustainable agrochemical supply chain management.