Supply chain leaders are increasingly turning to artificial intelligence to revolutionize their demand-forecasting and inventory-management strategies. This shift comes as traditional forecasting models, based primarily on historical sales data, have proven inadequate in the face of rapidly changing consumer behaviors and market volatility.

The COVID-19 pandemic exposed the limitations of conventional forecasting methods. As consumer buying patterns fluctuated dramatically — from stockpiling essentials to sudden shifts toward home office equipment, and later to travel services — supply chains were left struggling to keep pace. This volatility resulted in significant inventory imbalances, with many businesses simultaneously experiencing stockouts of high-demand items and surpluses of suddenly unpopular goods.

Using past sales as your primary driver is just not as accurate after the pandemic. We've seen a lot of companies try to expand the attributes they're using to find that demand signal beyond just historical sales.

AI-powered forecasting tools are emerging as a solution to these challenges. These sophisticated systems can process vast amounts of data from diverse sources, including weather patterns, social media trends, search engine data, local events and seasonal illness reports.

By incorporating these varied inputs, AI can provide a more nuanced and accurate picture of upcoming demand. This allows supply chain managers to make more informed decisions about inventory placement and quantity.

The benefits of AI in supply chain management extend beyond mere demand prediction. With the rise of omnichannel retail and increasing customer expectations for rapid fulfillment, supply chain leaders must also optimize inventory placement.

Traditional supply chains have to become much more savvy in meeting this new type of consumer, who wants to shop anywhere and expects the highest level of service. AI can help determine the most efficient locations for inventory, balancing the need for quick availability against the costs of distributed storage.

While the potential of AI is clear, implementation requires careful planning, including the following key elements:

• Clear objectives. Define what you want to achieve with AI forecasting. Is it improved accuracy, reduced stockouts, or optimized inventory placement?
• Data infrastructure. Ensure you have robust data collection and management systems in place. AI models are only as good as the data they're trained on.
• Cross-functional collaboration. Involve teams from across the organization, including IT, operations, and sales, in the implementation process.
• Starting small. Begin with pilot projects in specific product categories or regions before scaling up.
• Continuous learning. Regularly review and refine your AI models based on their performance and changing market conditions.

Early adopters of AI in supply chain forecasting are already seeing benefits. One major retailer reported that its AI-driven system helped predict regional trends during the last cold and flu season, allowing for more accurate stocking of over-the-counter medications.

Another international e-commerce company has begun using AI to forecast demand for clothing items, producing weekly demand predictions for each product in every size at each warehouse for upcoming seasons.

As AI technology continues to advance, its role in supply chain management is likely to grow. Future developments may include even more sophisticated models that can account for factors like viral social media trends or sudden geopolitical events.

For supply chain leaders, the message is clear: embracing AI for demand forecasting and inventory management is no longer optional. It's a critical step in building resilient, responsive supply chains capable of thriving in an increasingly unpredictable market.

Those who successfully implement these technologies stand to gain significant advantages in efficiency, cost savings and customer satisfaction. As we move further into this AI-driven era, the ability to accurately predict and respond to demand will be a key differentiator in supply chain performance.

Roland Dzogan is co-founder and chief executive officer of Ydistri.