The power of AI in demand forecasting

Technological advances have paved the way for new tools in supply chain management. Among the most powerful is artificial intelligence (AI). A promising application of AI already embedded in numerous supply chains but with significant potential for enhancement is demand forecasting.

Such forecasts allow supply chains to anticipate and prepare for changes in consumer demand. With accurate forecasts, businesses can optimize inventory management, reduce costs, and improve customer satisfaction.

AI enhances demand forecasting by using machine learning algorithms to analyze vast amounts of data more efficiently than humans. This technology uncovers patterns, correlations and insights that might otherwise be missed. With AI, people can evaluate various potential demand drivers, including subtle factors that would be too labor-intensive for humans to consider.

AI's capabilities extend beyond traditional static models, offering further improvements in demand forecasting quality. AI can adjust its forecasting models based on real-time events like market fluctuations, changing customer demands and supply chain disruptions. While traditional models might fail under such conditions, AI-driven algorithms can learn from past data and events to correct forecasts.

These algorithms can also use current data to adapt to demand changes, a process known as online learning. With this approach, AI continuously evaluates incoming data, enabling it to learn, adapt models and make decisions rapidly.

As an example use case, 4flow helped a B2C business increase its revenue by 11% by implementing AI-powered demand forecasting algorithms for cost reduction. Improved demand prediction enabled the business to adopt an intelligent warehousing strategy. This example highlights the significant impact AI can have on demand forecasting and overall operational efficiency.

To start utilizing AI in demand forecasting, businesses should define clear objectives and identify a task well suited to AI. AI excels at both repetitive tasks and gathering insights from large amounts of data, such as in demand forecasting. On the other hand, it is currently not suitable for high level tasks like long-term decision making. After defining a task, assemble a team of domain experts and data scientists who can implement and maintain AI solutions.

Data challenges are a common obstacle to implementing AI. High-quality data is essential, and businesses may want to use data sources like digital twins to enhance AI performance. Businesses that require assistance in these steps can benefit from the expertise of service providers for successful deployment.