For businesses to gain an edge over the competition, strategy makers must pick up signals in the demand-supply value chain of what’s trending in the market or where potential customers live. It is impossible to meet future end-user’s continuously changing demands and preferences without accurate insights.
Demand sensing is that missing piece in the market puzzle that can ensure business readiness in the face of precedented and unprecedented changes. Manually picking hints from consumer interactions and digital activities can paint half the picture. In order to strike the right chord, businesses need bigger guns; hence, a comprehensive demand sensing solution is the need of the hour.
Demand sensing captures demand data from the downstream supply chain. It leverages new-age technologies to capture insights and signals of what is happening in the supply chain. Such data helps businesses to forecast demand aptly and plan their supplies accordingly.
It is the art and science of extracting short-term trends in real-time pertaining to what customers want, where they want, and when they want. Unlike traditional forecasting, demand sensing technology relies on the latest sales data to make changes accordingly to boost profits.
There are broadly two approaches to demand sensing; such as: –
These approaches reduce uncertainty in demand forecasting and ensure critical adjustments are made without waiting for the following forecast cycle.
There are different ways in which insights, captured by demand sensing technology, help businesses in their planning. A few of which are described below: –
Demand sensing enables businesses to extract relevant information directly from PoS data for better sales forecasting without waiting for insights from distributors. With earlier demand insights, companies are better positioned to capture supply signals. This makes the production plan more efficient and market-ready.
Often, businesses have inventory stocked in the wrong place. Demand sensing helps enterprises optimize existing stocks and balance their supply by considering expected or new customer demands.
With demand-sensing technology’s rapid insights, businesses can forecast seasonal demands and react quickly to meet fast movers and seasonal products.
Product life cycles are getting shorter as new products enter the market to supersede existing product demands. With the help of the right demand sensing tools, businesses can meet the need for new products with the right amount of the previous products in stock.
With the help of sell-in data, promotion metrics, and data related to products and markets, businesses can understand how the campaigns are performing and how to forecast demand for future promotions.
There are various ways by which a demand sensing technology can improve forecast accuracy, such as: –
Point of sale data carries vital real-time information, including the product sold, quantity, date and time, region, and so on. Businesses can adjust the overall short-term forecast beyond the agreed norms by consolidating and comparing POS data with the short-term forecast and deviation.
For any upstream manufacturer, demand sensing technology helps improve forecast accuracy by reflecting on order history. And, by order history, we mean sales history, open order history, and currently available orders.
Macroeconomic indicators such as gross domestic product, the overall stock market, employment data, and housing sales data can directly impact the production and distribution of many industries. These indicators trigger changes in end-customer demand. With the help of demand sensing in the supply chain, businesses can resort to proactive steps to adjust the short-term forecast and avoid missed customer orders or excess inventories.
Demand sensing also considers what competitors are doing, including how they promote their products/services. Unexpected last-minute changes in promotion, such as offers and discounts put out by competitors, can take others in the market by surprise, giving them little time to make adjustments. Hence, demand sensing allows marketers to keep a watchful eye on competitors, timely predict the impact on sales due to such movements, and act accordingly.
It frequently happens – a competitor facing sustained stockout situations. This can arise due to a shortage in the availability of raw materials, quality issues in end products or overall production breakdowns, and so on. Businesses can leverage the situation by repositioning regional inventories to handle unexpected demand and increasing production accordingly to meet short-term requirements. Hence, they shift customer demand to alternative products available on the shelf.
Many industries work with seasonal raw materials and are often affected by changes in weather conditions. The sudden shift in weather patterns can directly impact the production process or the sales of certain products. Then, there are unprecedented challenges to consider, for instance, natural calamities impacting sales. Hence, with the help of demand sensing technology, businesses can make subtle adjustments in the short-term sales forecast, sourcing of raw materials, and production and distribution plan to meet consumer demands accordingly.
As mentioned earlier, this approach considers both historical and short-term data for forecasting both long-term and short-term demands, unlike demand forecasting.
The latter is inherently short-sighted since it can only draw inferences based on historical data to estimate what’s coming in the future. Hence, sudden changes in market conditions and the impact of unforeseen calamities escape its radar.
History repeating itself is not necessarily true in every event. The COVID-19 pandemic, for instance, negated demand forecasting of companies that only relied on past records. That was an eye-opener for many.
Besides the data part, there are other shortcomings in the legacy approach used by most companies. And that being the lack of automation and suitable technology to capture granular insights from the demand-supply value chain. Hence, demand sensing, with its broader range of signals, mathematical tools, automation, machine learning algorithms, and predictive modeling, can give a near-accurate picture of what’s about to happen in the market.
Demand sensing is genuinely the future of demand forecasting.
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