Demand sensing, a forecasting technique that uses sophisticated analytical techniques to record real-time fluctuations in purchasing behavior, is a relatively recent idea in the planning process. Companies that operate in rapidly shifting marketplaces might benefit greatly from technology.

Demand forecasting tools take data from point-of-sale (POS) systems, warehouses, and external sources daily to detect an increase or reduction in sales by comparing current patterns to historical ones. The system analyses each divergence’s importance automatically, examines influence factors and provides short-term plan modifications.

According to reports, demand sensing reduces near-time forecast inaccuracies by 30 to 40%. It enables businesses to respond quickly to unexpected changes in client needs and makes it easier to create a data-driven supply chain. This approach does not work for mid or long-term planning, so you cannot rely on all of your decisions only on it. However, it might be a useful addition to conventional forecasting techniques.

Add Demand Signals With AI-Based Demand Sensing

Building an internal data ecosystem that promotes accuracy and avoids delays in business operations is the first step towards realising the benefits of demand sensing.

Accuracy, efficiency, and dependability are ensured by AI (Artificial Intelligence) throughout your supply chain. One of the most potential uses of AI and Machine Learning (ML) is in the field of demand sensing and forecasting in the supply chain (AI).

Demand forecasting software can work to increase forecasting accuracy by merging AI learning algorithms with Big Data to assess an infinite number of contributing aspects concurrently. AI-enabled methods continuously improve and refine the supply chain’s demand forecasting process by learning from data on past and present performances.

With the aid of machine learning and artificial intelligence, demand forecasting for the supply chain can be taken to the next level by integrating internal and external data sources, including demographics, the weather, online reviews, and social media trends. Organizations can outperform networks that are administered manually and quickly react to external changes by using external data and contemporary machine learning algorithms. At this point, using the appropriate demand forecasting software can significantly affect the entire value chain.

External Data and Demand Causals

Demand sensing integrates many data points that are not taken into account in traditional forecasting techniques, which is one of the primary factors that makes it accurate and advanced.

The first step in incorporating external demand signals into your demand forecasting process is to identify the relevant signals that can impact demand. Some of the common external demand signals include:

  • Weather patterns: Businesses that rely on seasonal trends ought to include data on the weather as well. The information can be used to modify plans for the acquisition of raw materials, production, and distribution as well as detect the short-term effects of weather variations on consumer demand. Weather can have a significant impact on consumer behavior and demand. For example, during hot weather, sales of air conditioners and fans may increase, while during colder weather, sales of heating equipment may go up.
  • Holidays: Holidays can also have a significant impact on consumer behavior. For example, during the Christmas holiday, sales of gifts, decorations, and other holiday-related items may increase.
  • Local events: Local events such as concerts, festivals, and sports events can also have an impact on consumer behavior. For example, during a major concert or festival, sales of food, drinks, and souvenirs may increase.
  • Economic indicators: Economic indicators, such as interest rates, inflation, and unemployment rates, the country’s GDP, the overall stock market, and housing sales data can also impact consumer behavior and demand. For example, during a period of high unemployment, consumer spending may decrease, leading to lower demand for products and services.
  • Competitors: Follow competitor information, such as sales discounts or supply shortages. You can modify your offering using this data to get a competitive advantage.

The major goal of including external elements is to extend the forecasting horizon of demand sensing-based forecasting. The range of the projected events expands as more criteria are considered.

About the Company

Fountain9 is one of the trustworthy companies that offers potent artificial intelligence-based demand planning software. By providing precise forecasts of how demand will change over the next few days, Fountain9’s machine learning-based demand sensing helps businesses plan their supply needs well in advance. Incorrect product stocking by merchants can lead to both overstocking and understocking of products.


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