AI-driven demand forecasting reshapes inventory and supply chain optimisation

By Nilesh Jahagirdar, Co-Founder & VP Marketing & Solutions at [x]cube LABS

For decades, demand forecasting has been a game of gut instincts augmented with manual processes and historical sales data. Across industries, businesses have struggled with overstocking, leading to excess inventory costs, or understocking, resulting in loss of sales and dissatisfied customers. The traditional methods are often plagued by challenges like inaccuracies and inefficiencies, which fail to meet evolving consumer expectations and supply chain disruptions.

With the widespread adoption of technology, AI and machine learning are changing the game of inventory management. By analysing vast datasets in real time, AI identifies patterns and predicts trends using actionable insights. Hence, AI undergoes a constant improvement and learning process to adapt to external factors like unforeseen events, market uncertainties, and shifts in the economy. This proactive approach in inventory management allows businesses to optimise their stock and ...