Enhancing Manufacturing Supply Chain Resilience through AI: Advances in Demand Forecasting and Inventory Management

Abstract

Changing demand, global disruptions, and market conditions affect manufacturing. Because global supply chains require it. AI enhances inventory and demand forecasts in supply chains. Industrial supply chain demand forecasting and inventory management are strengthened by AI. Researchers study how ML, deep learning, and reinforcement learning forecast demand and monitor industrial inventory. Industrial supply chains are more agile due to fast-growing AI-powered data-driven decision-making. 

Manufacturers suffer from supply network failures, demand fluctuations, and inventory imbalances. Covid-19 and geopolitical tensions have raised global supply chain risks, necessitating stronger measures. AI automates decision-making, improves demand predictions, and provides real-time data. AI models and algorithms predict demand and enhance supply chain product and resource flow. 

We examine numerous AI supply chain management methods. Deep neural networks, supervised, unsupervised, reinforced. They are assessed for demand prediction, inventory reduction, and trend prediction. From massive historical data, machine learning predicts inventory planning insights. Deep learning algorithms can find complex, non-linear demand data correlations, improving prediction accuracy and flexibility. Trial-and-error reinforcement learning may enhance inventory and production schedules for real-time supply chain decisions. 

Industrial supply chain AI models need data, processing power, and infrastructure. Paper's second section covers AI model training data. Need real-time, high-quality historical data processing. AI model training data quality impacts inventory optimisation and demand forecasting. AI solutions simplified by big data, cloud, and edge computing may help factories expand and improve real-time choices. 

AI should enhance supply chain operations and structure. AI models must be commercially and technically viable. Industry struggles to incorporate AI due to change resistance, expensive technological prices, and supply chain management system integration problems. Data scientists, IT experts, and supply chain managers must collaborate on AI. 

The study closes with industrial supply chain AI model applications. These case studies demonstrate how AI estimates demand, manages inventory, and plans auto, electronics, and consumer production. Automakers anticipate demand using AI. Production and inventory are streamlined. AI helps electronics companies forecast market demand to ensure component availability. AI speeds up supply chains and lowers stockouts and overstocking. 

This article discusses AI-driven supply chain management technologies and trends. AI research produces digital twins, self-driving supply networks, and superior forecasting. Studies show that AI raises moral challenges such data privacy, algorithmic unfairness, and clear decision-making. Manufacturers must use AI ethically to streamline processes.