Demand sensing: the key to reacting to market shifts

The ability to adapt to market volatility has become one of the biggest challenges for modern supply chains. Shoppers change their buying habits at short notice, digital channels create new demand dynamics, and external factors such as weather or geopolitical events can throw sales forecasts off course. In this context, demand sensing emerges as an advanced approach that helps businesses respond quickly and accurately.

Demand sensing combines real-time data with predictive models powered by artificial intelligence and machine learning. Its aim is to narrow the gap between the initial demand forecast and what actually happens in the market, giving planners an up-to-date, fine-tuned view so they can make lower-risk tactical decisions.

Today’s challenges in demand forecasting

Demand forecasting has always been complex, but in recent years the difficulty has grown exponentially thanks to market volatility and rising customer expectations.

Limitations of traditional models

Classic forecasting models rely mainly on historical time-series analysis. Methods such as exponential smoothing, ARIMA or regression are useful for spotting trend and seasonality patterns, but they come with important limitations:

• Reliance on sales history: when consumer behaviour shifts sharply, algorithms that depend on past data are slow to react.
• Over-wide horizons: most traditional tools work to monthly or quarterly cycles, leaving a gap when it comes to handling unforeseen events.
• Poor integration of external data: forecasts often ignore factors such as weather, live promotions or competitor activity, all of which directly influence demand.

In sectors such as fresh food, retail or pharma, these limitations can lead to stockouts, excess inventory or missed commercial opportunities.

The short-term opportunity

Today’s market demands a complementary approach: the ability to adjust the forecast at very short notice. This is where demand sensing comes in. Rather than waiting for long forecast review cycles, demand sensing analyses daily, or even hourly, data to spot deviations and automatically adjust the forecast.

This approach makes it possible to pick up early signals of changing consumption and adapt quickly. For example, an unexpected heatwave can spike soft-drink sales; a surge in social-media mentions can foreshadow rising demand for a new product; a delay at a critical supplier can flag the need to replan.

What is demand sensing?

Demand sensing is an advanced forecasting approach that uses real-time data and AI-driven algorithms to refine demand forecasts more accurately over short horizons (days or weeks). Unlike traditional models, which depend on history and trends, demand sensing incorporates external, dynamic signals that reflect what’s actually happening in the market.

It’s a natural evolution of demand planning: not a replacement for the traditional forecast, but a layer that adds agility and responsiveness. In that sense, demand sensing acts like a sensor that translates market change into actionable insight for the supply chain.

Dynamic adjustments to real demand

The main value of demand sensing lies in its ability to correct deviations almost in real time. Algorithms detect emerging patterns and continuously recalculate the forecast. This gives planners the ability to:

• Adjust production and distribution orders within hours.
• Redirect inventory to locations with the highest likelihood of sale.
• Avoid logistics overspend caused by late decisions.

In practice, this leads to a significant reduction in forecast error, which directly improves service levels and optimises working capital.

External data that enrich the forecast

Demand sensing draws on multiple information sources that enrich the forecast:

• Point-of-sale (POS) data: measure true store-level off-take and detect immediate consumption trends.
• Weather and climate information: useful for sectors sensitive to seasonal conditions, such as beverages, fashion or cosmetics.
• Promotions and marketing campaigns: the algorithm can factor in the expected and actual impact of commercial activity.
• Digital behaviour: online searches, social-media interactions or spikes in web traffic act as indicators of interest in a product.
• Macroeconomic and regulatory variables: input costs, legislative change or exchange-rate movements.

This ability to combine internal and external data makes demand sensing an essential tool for cutting uncertainty.

Demand sensing and demand planning: two parts of the same puzzle

Although demand sensing brings speed and accuracy in the short term, it should not be seen as a substitute for demand planning. Both concepts sit within an integrated demand-planning strategy and naturally complement one another.

Demand planning remains the foundation for defining strategic scenarios and aligning sales, operations, production and finance. Without this reference plan, short-term adjustments would lose coherence and risk generating isolated decisions.

The medium-term base plan

Demand planning builds a robust forecast over months, or even years, by integrating historical data, seasonality, market trends and the company’s strategic objectives. This forecast is used to:

• Define production and procurement plans.
• Plan plant capacity and resources.
• Set financial budgets.
• Guide strategic decisions within the S&OP cycle.

In short, demand planning provides the stability needed to coordinate every part of the organisation.

Day-to-day course correction

Demand sensing acts like a radar that corrects course against market reality. It allows that base plan to be adjusted to respond to unexpected events without undermining the coherence of the overall forecast.

Combining the two gives the best of both worlds: strategic medium-term vision with short-term adaptability.

Competitive advantages of integrating both approaches

Bringing both methods together gives companies a clear competitive edge. It’s not about choosing one or the other, but about combining them intelligently.

Key benefits include:

• Lower forecast error: strategic forecasts plus dynamic adjustments produce a more accurate outlook.
• Inventory optimisation: less overstock and a lower risk of stockout.
• Better customer service: the ability to react quickly without compromising availability.
• Greater supply-chain resilience: the capacity to adapt to crises or sudden shifts in demand.
• Organisational alignment: one approach anchors strategic objectives, the other provides data for tactical decisions.

It’s worth stressing that demand sensing doesn’t make sense on its own. The foundation is still demand planning, which provides the big picture and strategic frame. Demand sensing is the adjustment layer that keeps that plan valid even in volatile conditions.

Demand sensing as a way to improve your ability to respond

Demand planning is no longer a static exercise. In a world where volatility is the norm, companies that anticipate market shifts will enjoy a clear advantage.

Demand sensing is the tool that turns market signals into concrete actions. Its ability to adjust forecasts dynamically, incorporate external data and reduce error makes it an indispensable ally for any modern supply chain.

However, its real value is only unlocked when it’s integrated with demand planning. The medium-term base plan is still essential to coordinate operations, finance and strategy. Demand sensing, by contrast, provides the flexibility to correct course and keep the overall forecast aligned with market reality.

In short, demand planning and demand sensing don’t compete, they complement each other. Together they enable organisations to plan with vision and respond with agility, ensuring both operational efficiency and customer satisfaction in an increasingly uncertain environment.

If you’d like help improving your planning processes and optimising your end-to-end supply-chain operations, book a free consultation with our experts. We’d love to help.