Demand Sensing vs Demand Forecasting

An introduction to the core concepts of what differentiates demand sensing and demand forecasting
Remi's Guest Blogger Jeanne Billy at work

Guest Blogger: Jeanne Billy

In an ideal world, businesses could accurately predict which products will be in demand. However, the perfect technological solutions for this do not exist yet. Concordia University also states that globalization, increasing market competition, and the surge in supply chain digitization practices have complicated this process even further.This results in an ever-growing need for customer behavior analysis and demand forecasting.

Fortunately, emerging technologies like machine learning (ML)are increasing the accuracy of supply chain management solutions. Fortune Business Insights explains that ML is a subset of artificial intelligence that teaches computers to learn from algorithms and data by mimicking how humans learn. Some relevant applications of ML in the supply chain include demand sensing and demand forecasting. These assist in demand planning, albeit for different lengths of time. Here’s what you need to know about how they differ — and which one may be best for your own operations.

What is demand sensing?

Demand sensing uses recent data, from days to hours ago, to make accurate short-term predictions in a volatile market. It uses machine learning, which Maryville University notes is changing business administration processes across a wide variety of industries. It explains that machine learning does so by using algorithms to process larger numbers of records and attributes per record for enhanced predictability. This allows retailers to predict how consumers will respond in the short term to various factors, including social media trends, weather data, competitor promotions, new product launches, and future orders. As a result, demand planners can act according to today’s trends rather than yesterday’s, effectively reducing supply chain latency.

However, the data being used can only be helpful for forecasting demand for up to around ten weeks in the future. Any longer than this and demand sensing could lose its accuracy. Nevertheless, demand forecasting can help your business improve your cycle time, on-time delivery, and inventory management.

What is demand forecasting?

While demand sensing is used for more immediate predictions, demand forecasting uses historical sales data for long-term forecasting. It is similar to traditional statistical forecasting, which looks at historical datasets to make predictions about the future. However, this requires having to experiment with a variety of techniques and metrics that can affect the accuracy of forecasts.

In contrast, modern demand forecasting solutions feature ML algorithms. These use additional data sets to improve your demand forecasting strategy. Our article ‘Machine Learning Powered Demand Forecasting’ explains that these datasets may include business-specific variables that change overtime, such as the price and weather, and related and categorical metadata, like brand or location. The ML-powered algorithms used by demand forecasting tools also collect new data or additional variables, like web traffic and online reviews, to automate forecast updates. And unlike traditional statistical forecasting methods, ML allows you to test multiple statistical algorithms to determine the most accurate ones. Once you have this figured out, you can then defer to a model that helps you best manage and utilize your forecasts.

Which one does your business need?

In general, all businesses would benefit from both demand sensing and demand forecasting. But what your business needs more would depend on how extensive your predictions need to be. Demand sensing allows your supply chain to be agile and adaptable to sudden changes or disruptions. This makes it suitable for industries that see shifts in day-to-day demand, like consumer goods.

Demand forecasting, on the other hand, is a long-term option.This makes it particularly useful for industries where data changes less predictably seasonally. These industries include energy, automobiles, and steel.

Retailers benefit greatly from short-term and long-term demand predictions, as they help with inventory management and allocation of resources. By understanding the difference between demand sensing and demand forecasting, you can understand how each can benefit your company’s specific processes.

Stay tuned on the Remi AI blog as we build out the complete supply chain offering!

Or, if you're ready to start seeing the benefits of A.I-powered inventory management, start the journey here.
Who are we?

Remi AI is an Artificial Intelligence Research Firm with offices in Sydney and San Francisco. We have delivered inventory and supply chain projects across FMCG, automotive, industrial and corporate supply and more.
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