What is demand sensing and how does it work?

Markets move in hours, not months. By reading real-time signals from orders, shipments, and the outside world, demand sensing lets you get out in front of whatever is happening. And early insights allow your teams to translate this data into practical next steps for supply, inventory, and fulfillment. Demand sensing is not a replacement for demand forecasting; it’s simply an additional layer of agility that keeps your plans as current and accurate as possible.

Demand sensing is a short-horizon forecasting practice that uses real-time internal data (for example, customer orders) and external context (for example, market intelligence). Modern supply chain solutions then apply AI and machine learning to this data, to detect deviations and patterns quickly – and update plans continuously. It is typically applied to fast-moving items and near-term execution windows. It complements your broader, longer-range demand forecasting and planning processes.

Modern product cycles are shorter than ever – as are customers’ patience levels. Channel volatility and an increased range of real and digital disruptions mean batch updates can arrive too slowly. By streaming incremental changes and refreshing short-term forecasts, demand sensing tools and practices help you reduce stockouts, trim excess, and keep your supply activities synchronized with what customers are actually doing – today, not last week. For businesses with large assortments and complex fast-moving inventories, the ability to update at scale and in the moment can mean the difference between too late and just in time.

It takes more than speed and real-time data to make demand sensing work. You must also be able to quickly understand and make sense of all that data. And that’s precisely where AI and machine learning (ML) come in. These technologies are able to amplify demand sensing by interpreting complex signals faster and more accurately than any other traditional methods. Instead of relying on fixed models, they continuously learn from live data – getting more precise and specialized over time.

• Adaptive model selection: Tests and compares algorithms to find the best fit for each product, region, or time frame. This means systems are able to match different demand patterns automatically – whether driven by seasonality, price changes, or sudden supply shifts – without needing manual tuning.
• Pattern recognition: Identifies subtle demand changes that manual systems often miss, such as early sales surges or promotion-driven shifts. AI can correlate dozens of data factors simultaneously – surfacing early warning signs that help your teams act before service levels are at risk.
• Automated recalibration: Prevents overreactions and instability by refreshing short-term forecasts as new data streams in. The model then evaluates which updates represent meaningful shifts versus noise, so forecasts evolve smoothly instead of spiking with each data fluctuation.
• Scenario learning: Allows faster reaction to shifts in pricing, lead times, or customer mix by analyzing “what-if” scenarios. It simulates alternative conditions to test how specific supply or market variables might affect demand, making planning operations more purposeful and agile.
• Continuous improvement: Refines models automatically by learning from historical results. This leads to increased forecasting accuracy and relevance over time. Each cycle adds intelligence to the next, creating a self-correcting system that becomes more reliable the longer it runs.

While demand sensing is employed across a range of industries, retail use cases are often some of the most pressing due to B2C dynamics and high-volume SKUs and assortments. Live point-of-sale (POS) data, online orders, and store-level replenishment needs can shift very quickly in this environment. Promotions or viral trends can also change demand patterns – within hours. Demand sensing helps retailers and CPG brands adjust inventory across stores, warehouses, and omnichannel settings. So, whatever market event occurs, teams can adjust assortments or respond to any situation on the fly. This helps to protect working capital and coordinate decisions across suppliers, manufacturers, and retail partners.