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Demand Sensing in 2024: A quick guide to get you started

Every supply chain manager’s dream is to know exactly which product will be sold in the future, at what time, and in what quantity. However, 100% accurate demand forecasting doesn’t exist yet. Digital technologies such as AI/ML are bringing supply chain managers closer to accurate demand forecasting.

While demand forecasting works well for mid to long-term demand planning, it has shown to be less effective for short-term planning, and that is where demand sensing steps in.

This article explores demand sensing, what it is, how it’s different from demand forecasting, what its benefits are, and some best practices to get supply chain planners started.

What is demand sensing, and how does it differ from demand forecasting?

Demand sensing is a combination of methodology and technology to predict near-future demand based on short-term data. Unlike demand forecasting, which uses data from a year ago, demand sensing uses data acquired days or even hours ago to make accurate short-term predictions.

In other words, demand sensing picks up on short-term trends to predict what will happen in a volatile market.

Source: LinkedIn

What are the benefits of demand sensing?

Demand sensing can benefit your supply chain in the following ways:

More supply chain resilience

With short-term data obtained from demand sensing, supply chains can quickly adapt to market volatility or sudden disruptions such as the covid-19 pandemic. The technology can make supply chains more resilient and robust.

Better inventory management

Demand sensing can provide daily demand data to optimize inventory levels, reduce stock levels and make the supply chain leaner while not compromising on resilience. This can ultimately reduce costs of both excess production and handling costs.

Better predictability

Demand sensing uses a wide range of signals, including real-world events such as order patterns, retail sales, promotions, and market changes, to understand trends sooner.

What are some demand sensing best practices?

To successfully implement demand sensing in your business, supply chain managers can use the following best practices:

Start with sell-in data

A simple way to start demand sensing in your supply chain is to use granular historical data. This can be obtained by analyzing sell-in demand data with a shorter time horizon. Shipping history will also be factored in while considering sell-in data which can be obtained from any supply chain planning or ERP system. This can be used for accurate B2B demand forecasting.

If you are looking for supply chain planning software, you can check out our data-driven list to find the option that best suits your business needs.

Incorporate all possible data sources

For accurate demand sensing, it is important to consider all relevant data such as downstream sell-out data which includes:

  • Customer order data
  • Consolidated POS (point-of-sale) data (real-time data on products sold, quantity, date and time, region, etc.)
  • Channel data

This data can, for example, help predict trends early and warn the supply chain of near future disruptions.

Incorporate all external factors

One of the key points that makes demand sensing accurate and advanced is that it incorporates many data points that are not considered in conventional forecasting methods. A supply chain planner may also consider the following factors while implementing demand sensing:

  • Consider macroeconomic factors including the country’s GDP, the overall stock market, employment data, and housing sales data. This impacts the end consumer’s demand.
  • Track competitor data such as promotional discounts or stock-outs. This data can help you adjust your offering to gain a competitive advantage. 
  • Businesses that depend on seasonal trends should also incorporate weather data. The data can help sense the short-term impact of the weather changes on consumer demand and help adjust raw material procurement, production, and distribution plans.

The main purpose of incorporating external factors is to widen the horizon of the forecasting that is predicted through demand sensing. The more factors considered, the wider the range of the predicted events.

You can also check out our data-driven list of Demand Planning Software to find the option that best suits your business needs.

Further reading

If you have any questions, feel free to contact us:

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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