Optimizing inventory management requires implementing advanced demand forecasting methods, such as machine learning or big data-based algorithms. Traditional methods based on historical data cannot cope with rapid changes in the market. It is important to integrate data with suppliers and customers to create a unified supply chain management system in real time. It is also worth implementing flexible manufacturing strategies that allow you to quickly reconfigure lines to meet new demands. The use of AGVs (automated guided vehicles) and warehouse robotics will reduce the response time to demand fluctuations. Predictive analytics for monitoring the condition of equipment will reduce unplanned downtime.

In corporate sector, implementation of SAP for inventory management, can foster budgeting of inventory as per demand changes. A coordination team formed from purchase, stores, manufacturing & sales, providing data in the projections for inventory management can be a key for staying ahead for demand & supply fluctuations.

Managing demand volatility is crucial in manufacturing. A proactive approach using predictive analytics can enhance demand forecasting. Implementing a just-in-time (JIT) system reduces excess inventory and costs. Collaboration among sales, marketing, and operations ensures quick responses to market changes, while regular strategy reviews maintain efficiency.