Manufacturing is predicated on precision. Whether it's the products being produced on the factory floor or the supply chain planning that governs material flow and timing, this reliance on accuracy is now being severely challenged by external volatility.

The repercussions can be stark.  One week, your production schedule is empty. Next, you have an emergency order for three times your capacity. The week after that, a massive order was canceled.

It’s feast or famine at a debilitating scale.

There are many reasons for this, including rising raw material costs, inflation, tariffs, and changes in consumer sentiment. Ultimately, these factors are just extensions of the bullwhip effect, in which small, regular changes in customer demand are wildly amplified as they move up the supply chain. The antidote is AI-powered tools that shift your focus from backward-looking statistical forecasting to demand sensing, producing significantly improved forecast results.

Why Your Traditional Forecast Is the Problem

For decades, data-driven manufacturers have relied on a similar supposition, that understanding the past is key to best predicting the future.  This picture is opaque at best.

What’s more, when other factors and fluctuations, such as new product introductions, promotions, or seasonality, enter the equation, the historical data often becomes an even less reliable guide. Deriving meaningful, actionable information is also a cumbersome, intensive process that requires planners to spend valuable time manually cleaning historical data to remove outliers. This entire process is reactive. It's an attempt to drive a precise, high-speed production line by looking in the rearview mirror.

It’s a lot of work for a limited payoff.  Unsurprisingly, the results are lackluster. In the food and beverage sector, the median demand forecasting error rate is 50 percent, while the durable consumer products industry endures a 56 percent error rate.  However, modern manufacturers have a suite of AI tools at their disposal that can transform data into understandable, searchable, and actionable insights, keeping them agile and responsive.

How to Implement Demand Sensing

To be sure, historical data is part of the data picture. However, modern manufacturers can’t just look back. They need to start sensing what’s happening right now. This approach is forward-looking and translates the flow of products through the distribution channel as far into the future as possible.  It is the most predictive information as it identifies patterns in real sales happening as they flow through the distribution network.

Instead of relying solely on your company's shipment history, AI-driven demand sensing monitors real-time data from the external world. This includes:

• Customer orders
• Consumer sales
• Inventory levels in the channel
• Social sentiment signals
• Weather patterns

This is the most predictive information available because it identifies patterns in real sales as they flow through the distribution network. As I share in my recently published industry analysis, AI uses real-time analytics to understand and predict customer demand.  Just as importantly, the AI takes over the tedious work of manually cleaning historical data, automatically flagging deviations and outliers.

When coupled with AI-driven query capabilities, manufacturing leaders can ask in natural language how a particular event will impact demand now and in the future.  The results can be immediate and tangible.

Beta clients using this AI-driven approach have seen a 30 percent reduction in forecast errors on the short-term horizon. Demand sensing anticipates disruptions, giving teams time to prepare and turning crises into manageable events.  Simply put, it allows manufacturers to work with precision in an unpredictable world.

Start Sensing This Season

Increasingly, manufacturers are facing significant uncertainty that undermines this core value. You’ve probably experienced this firsthand.

Bullwhip isn’t an immutable law. It’s a symptom of outdated processes and limited insights. It can be overcome.

By moving from backward-looking statistics to forward-looking AI, small changes throughout the supply chain (and the signals they send) can be detected, analyzed, and neutralized before they create widespread disruption.

In other words, demand sensing can restore sanity to manufacturing supply chains and the business objectives they support.

Piet Buyck is a global technology executive with over 30 years of experience in managing and positioning high-value IT applications that disrupt current practices and author of the new book AI Compass for SC Leaders. He is well-known as an influential and strategic business thought leader and entrepreneur with significant achievements and expertise in artificial intelligence, demand sensing, and demand planning. As Senior Vice President, Innovation Strategies at Logility, an Aptean company, Piet is on a crusade to make artificial intelligence for planning easy, accessible, and explainable while keeping human decision-makers in control.