Demand Sensing

Demand Sensing – The New Approach to Predicting Short-Term Demand and Stay Future-Ready

The supply chain never gets simpler with each passing year. In fact, it has emerged as an intricate web of suppliers, distributors, vendors, and retailers, alongside a huge queue of service providers vying to get consumer attention. Things get more challenging when sudden disruptions in the demand-supply beget short-term trends causing inventory waste and stockpiling. Thankfully for demand sensing, companies can better predict what customers want, when and where and curb unnecessary loss or waste of goods. Hence, demand sensing technology has become the need of the hour.

Now, the demand sensing process will not provide apt predictions in the absence of intelligent supply chain technology. Hence, our focus should be on understanding how new-age technologies can help businesses predict short-term trends ahead of time with speed and precision.

What is Demand Sensing?

Demand sensing leverages new technologies to accurately forecast demand based on current trends in the supply chain by capturing real-time data. This new approach is a striking contrast to legacy approaches of forecasting demand based on historic data. There is a fundamental difference in the new approach, which considers a much broader range of demand signals, including current supply chain data, and uses other combinations for demand forecasting. The resultant data acknowledge real-world events, like market shifts, weather changes, and natural disasters, influencing customer buying behavior.

Short-term forecasts transcend into long-term savings for businesses.

Demand sensing solutions focus on two aspects –

Short-Term Forecasting 

Here, the forecasting method leverages granular data to analyze daily demand near closeness to end-users preferences and identify potential changes in demand behavior.

Extending Supply Chain Visibility 

Demand sensing technology follows patterns in point-of-sale (PoS), promotion, social media, NPI, weather, IoT, internet search and economic data to improve forecast and inventory placement.

These methods are constructed to reduce the risk of demand uncertainty and help businesses make critical adjustments in their forecasting in real-time without waiting for the next forecast cycle.

Traditional Forecasting Vs. Demand Sensing

Usually, traditional forecasting methods rely heavily on past sales records and other historical data. Such insights are enough to predict trends in the long term. Afterall, market trends usually repeat themselves but with many years in-between.

However, such data might not pinpoint the sudden, short-term change in trends, especially when uncontrollable disruptions occur like the pandemic. Hence, real-time changes in the market that are likely to impact current demand remain largely unaddressed. Businesses still following the legacy methods often fail to respond to those sudden changes and cannot facilitate production and delivery quickly.

Contrarily, demand sensing considers a broader range of demand signals, including historical and real-time data and other variables, to create more accurate forecasts. By leveraging the power of new-age technologies, like Predictive Modeling, Machine Learning and Artificial Intelligence, the demand sensing software automate the entire data capturing process from different PoS terminals, analyze, extract meaningful insights, and give valid estimations.

Hence, demand sensing is good for short-term predictions mostly.

How does Supply Chain Technology Help Businesses in Demand Sensing?

With the help of demand sensing technology, companies get actionable insights to work on and create supply matching the current market demand on time.

Here are the different ways by which demand sensing helps businesses

Demand Latency Reduced

Demand sensing software helps businesses to extract valuable data directly from PoS to forecast sales better without waiting for distributors to pass on information. Earlier demand signals help with the timely supply of products/services.

Stock and Inventory Pile-Up Addressed

Stock and inventory pile-up during sudden short-term demands is an existing reality. However, a comprehensive supply chain technology can help businesses better utilize existing inventory in the short term. It becomes much easier to optimize available inventory by redirecting them to where the demand for those inventories still exists or will arise shortly.

Precise Seasonal Demand Forecasts Generated

Businesses need to be ready with the supply for seasonal or sudden demands to remain ahead of their competitors. By expanding the visibility of the demand-distribution network, companies can improve their supply. Further, companies get valuable insights into inventories not attracting enough buyers so that adjustments can be made immediately to avoid obsolescence.

Understand Promotion Performance Better

Sell-in-data like promotion attributes and market data captures promotional uplifts to forecast future promotional demands.

New Product Demand Timely Catered

New products keep entering the market at accelerated frequency; hence, product life cycles are cut short by many months. But, with demand sensing solutions, companies can have the confidence in their existing inventories and numbers to meet the demand for similar but new products.

How to Get Started with Demand Sensing?

The ultimate objective of demand sensing is to shorten reaction time and boost profit. The following are the ways by which businesses can get started with demand sensing –

Sell-in-Data for Short-Term Forecasting

Historical data might not paint an apt picture of the short-term future demand. However, daily sell-in or ship-to data can adjust the forecasting accurately using shorter time horizons.

Focus on the Sell-out-Data

Sell-in-data paints half the picture. But what is exactly happening at the other end of the demand-supply trajectory is hard to predict. Here, downstream sell-out data such as customer, PoS, and channel data can easily capture any change in demand trends or generate early warnings of possible problems and highlight the gap between the supply plan and what is happening in the supply chain.

Consider External Data and Demand Casuals

To create a robust and responsive forecast, a good demand sensing software should integrate other variables in the demand-supply chain network, including demand casuals. Such forecasting should be able to adhere to any sudden changes in the demand triggered by known or unknown events, like stock market fluctuations, competitors’ promotions, viral social media trends, new product introductions, weather and other external factors.

With a combination of sell-in, sell-out, and demand casual data, businesses understand the actual market demand and plan their supply accordingly. Such data caters to automated demand sensing that functions independently without planners applying their market knowledge to perfect forecasts.

Demand Sensing – The Best Bet to Beat Competition

The pandemic of 2022 has proved that legacy approaches to forecasting demand are ill-equipped to consider sudden disruptions. Hence, many businesses suffered immense losses, and many others faced premature deaths. The global event drew the spotlight on demand sensing, which emerged as the new way to stay alive during sudden demand surges and competitive in the market.

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