Supply chain managers are increasingly turning to artificial intelligence to revolutionize their demand forecasting and inventory management strategies. This change 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 has highlighted the limitations of conventional forecasting methods. As consumer purchasing habits fluctuated widely – from stockpiling essential products to suddenly shifting to home office equipment and then to travel services – supply chains have had a hard time keeping up. This volatility has led to significant inventory imbalances, with many businesses simultaneously experiencing stock-outs of high-demand items and surpluses of suddenly unpopular products.
Using past sales as a primary motivator just isn’t as accurate post-pandemic. We’ve seen many companies try to expand the attributes they use to find that demand signal beyond just historical sales.
AI-based forecasting tools are emerging as a solution to these challenges. These sophisticated systems can process large amounts of data from a variety of sources, including weather conditions, social media trends, search engine data, local events and seasonal disease reports.
By integrating these diverse insights, AI can provide a more nuanced and accurate picture of future demand. This allows supply chain managers to make more informed decisions regarding inventory location and quantity.
The benefits of AI in supply chain management go beyond simple demand forecasting. With the rise of omnichannel retail and growing customer expectations for speed of fulfillment, supply chain managers must also optimize inventory placement.
Traditional supply chains must become much savvier to cater to 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 rapid availability and the costs of distributed storage.
While the potential of AI is clear, its implementation requires careful planning, including the following key elements:
- Clear objectives. Define what you want to achieve with AI predictions. Is it improved accuracy, reduced stock-outs, or optimized inventory placement?
- Data infrastructure. Make sure you have robust data collection and management systems. AI models are only as good as the data they are trained on.
- Cross-functional collaboration. Involve teams across the organization, including IT, operations, and business teams, in the implementation process.
- Start small. Start with pilot projects in specific product categories or regions before expanding.
- 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 the benefits. A major retailer reported that its AI-based system helped predict regional trends during the past cold and flu season, enabling more accurate storage of over-the-counter medications.
Another international e-commerce company has started using AI to forecast demand for clothing, producing weekly demand forecasts for every product, in every size, in every warehouse for upcoming seasons.
As AI technology continues to advance, its role in supply chain management is likely to grow. Future developments could include even more sophisticated models that can take into account factors such as viral social media trends or sudden geopolitical events.
For supply chain managers, the message is clear: adopting AI for demand forecasting and inventory management is no longer an option. This is a crucial step in creating resilient and responsive supply chains that can thrive in an increasingly unpredictable market.
Those who successfully implement these technologies will experience significant benefits in efficiency, cost savings and customer satisfaction. As we move forward in 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 CEO of Ydistri.