AI-Powered Demand-Sensing: Transforming Supply Chain Planning and Forecasting

Learn how demand-sensing technology can improve supply chain forecasting in this report from Kearney and AWS.

Download the ebook

AI-driven demand-sensing supply chain technology

Companies have built resilience into their supply chains post-pandemic, shifting their focus from reactive adaptation to proactive strategic planning. However, today's market—with factors like multiple sales channels, changing consumer trends, and unexpected global events and political tensions—makes accurate forecasting incredibly challenging.

The transformative potential of AI-driven demand-sensing technology is reshaping supply chain forecasting and planning processes. This innovative approach leverages a wealth of internal supply chain and external market data to enhance the accuracy of predictions—even within the context of constant market volatility.

AI-driven demand-sensing supply chain technology

How demand-sensing differs from conventional forecasting:

  • It recognizes the need for a richer set of sourcing, production operations, shipment, order, inventory, and sales data encompassing the complexities of today’s supply chains.
  • Data is captured, structured, integrated, and shared in near-real time.
  • External data is increasingly crucial both because of data availability and validation.
  • It uses artificial intelligence (AI) and machine learning (ML), guided by human intervention, to fill in visibility gaps.
  • Demand sensing solutions can build precise, short-term forecasts of customer demand on a daily or even hourly basis.

► Download the ebook

How demand-sensing differs from conventional forecasting ebook

Demand sensing in supply chain management

Demand sensing, powered by artificial intelligence and machine learning, offers real-time insights into customer behaviors and potential outcomes, addressing the many complexities of today's supply chains. By capturing, structuring, and sharing data in near-real time, it provides a current, transparent, and dynamic view of the supply chain. This also creates a shift from traditionally adversarial relationships among supply chain partners to more collaborative work grounded in shared objectives.

Through the integration of external data from suppliers and vendors, demand sensing technology not only enhances the accuracy of forecasting but also encourages better collaboration across the entire supply chain ecosystem.

Demand sensing in supply chain management

"Even the highest-quality internal data is, by itself, no longer sufficient for extrapolating the future."

External data is increasingly crucial

And not only because 80 percent or more of today’s supply chain data is now generated externally, by suppliers, vendors, end users, and third parties. It is also important for validation, since recent historical internal data is often corrupted for forecasting purposes by the impacts of COVID. When it comes to demand sensing in supply chain, history is no longer a useful indicator of the future. Read more

Demand sensing in supply chain operations

Demand sensing is an advanced forecasting method that uses real-time data, machine learning, and analytics to reduce forecast errors and better predict customer demand. Unlike traditional forecasting, which relies heavily on historical data, demand sensing captures current demand signals to provide more accurate and responsive forecasts

Demand sensing integrates a wide array of data sources—including internal sales data, inventory levels, and external factors like market trends and social media sentiment. By leveraging AI-powered demand sensing, businesses can analyze this data in near real-time. Machine learning algorithms can detect patterns and correlations that might be missed by conventional methods, enabling companies to adjust their supply chain strategies almost immediately.

While both demand sensing and demand forecasting can help predict future demand, they differ significantly in terms of approach and accuracy. Traditional demand forecasting relies on historical data and generally assumes past patterns will continue. Demand sensing focuses on the present, using current data to capture immediate market shifts. This makes demand sensing more agile and better suited for modern, fast-paced markets.

Demand planning involves forecasting demand, aligning inventory levels, and scheduling production to meet customer needs. Demand sensing is essentially a component of this process, providing real-time data and insights that make demand planning more accurate and efficient. Incorporating demand sensing solutions into demand planning allows businesses to react quickly to changes, reducing stockouts and overstock situations.

Integrating demand sensing in supply chain operations can significantly improve forecast accuracy, inventory management, and customer satisfaction. For example, companies that have adopted demand sensing software report reduced inventory levels and increased service levels, directly impacting their bottom line.

To explore how demand sensing can revolutionize your supply chain forecasting, download our ebook, Signals Amid the Noise. It goes deeper into the concept of demand sensing, real-world case studies, and actionable strategies you can use to implement demand sensing in your own organization.

By embracing demand sensing, businesses can navigate the complexities of modern supply chains with greater confidence and agility. Don't let outdated forecasting methods hold you back—discover how demand sensing tools can provide the clarity you need in an unpredictable world.

► Download Signals Amid the Noise