Inventory forecasting is the process of predicting future inventory requirements based on historical data, market trends, seasonality, and other influencing factors. It plays a vital role in inventory optimization by aligning stock levels with anticipated demand, thereby minimizing both shortages and excess inventory. Traditional forecasting methods rely on statistical models such as moving averages or exponential smoothing. More advanced approaches leverage machine learning and artificial intelligence to incorporate external data sources like market signals, promotional calendars, weather patterns, and macroeconomic indicators. The goal is to increase accuracy and responsiveness, especially in volatile or complex environments. Effective inventory forecasting reduces holding costs, improves service levels, and supports operational efficiency. It also allows for better collaboration with suppliers and proactive capacity planning. Inventory forecasting is no longer a siloed activity; it’s deeply integrated into supply chain orchestration platforms, enabling adaptive, data-driven decisions that drive resilience and profitability in today’s dynamic markets.
