The Ultimate Guide to Demand Sensing for Accurate Demand Planning

April 20, 2023 · 3 minutes
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Supply chain planning that is purely based on forecasts is no longer relevant in today’s fast-paced market. Just as we step out of the post-covid impact, the need of the hour is holistic demand planning which can drive results in today’s dynamic and ever-changing business environment. 

The most important aspect of demand planning is to maintain the right balance between customer demand and stock inventory. This becomes necessary to deliver superior customer satisfaction and also purposefully channel the sales funnel of the business.

However, the complexity associated with demand planning comes from the fact that this requires a seamless synchronization with the changing demand patterns and a volatile market makes this quite impossible.

Demand Variability is the Deciding Factor

Demand variability has long been a key cost driver for several industries, and is a complex subject to tackle, which current demand planning capabilities can’t quite solve.

An end-to-end visibility of daily demand is essential to optimize profitability, and conventional demand forecast-based planning falls short of providing a holistic prediction. 

Broadly speaking, the traditional demand-planning process uses historical shipment, sales and other data to predict future product demand. It is as simple as saying “History teaches us what the future can bring”. But this is not the case in the real-world with demand variability which creates erratic demand fluctuations.  

The major flaw here is predicting future demand simply based on past experiences, as this approach cannot point out changing market conditions in real-time. It also falls short of evolving production and delivery patterns across the supply chain pipeline in response to those dynamic changes.

So, What’s a Better Solution?

The question here is obvious: If not using historical data, how can you manage the rapidly evolving demand-supply dynamics and how to do it right?

The answer lies in demand sensing. Demand sensing involves a combination of methodology and technology to predict near-future demand based on short-term data.

Unlike demand forecasting which depends on data from the past or even a few years ago, demand sensing uses data acquired recently – maybe days or even hours ago to make accurate short-term predictions.

Demand sensing acts as the essential upgrade to existing demand planning efforts as it does not replace traditional demand forecasting, but rather enhances it. In fact, according to Gartner, when demand planning is done with a more accurate and manageable prediction of daily demand, forecast accuracy can boost by up to 40%.

Demand planners can leverage these daily demand insights for more accurate forecasting. It incorporates predictive analytics to produce daily demand forecasts,  and ensures the latency in supply chains is slashed down. It enables today’s demand planners to accurately respond to what’s happening on the shop floor right now, rather than using yesterday’s prediction.

Strengthening Demand Planning Efforts with Demand Sensing

In today’s times, market shifts occur with zero warning. Demand planners have to be ready to adapt instantaneously or risk losing their market position – or, worse, market share. Therefore, there is a need for real-time planning that not only offers a clear line of sight into all relevant internal and external influencers of demand, but also helps anticipate and drive it.

Demand sensing helps achieve this. Traditional demand planning which is based on past sales records can only predict forecast levels in mid and long-term planning horizons. Short-term planning needs a little more, which is where demand sensing comes into the picture. 

To put it simply, demand sensing when applied to the holistic demand planning process for comprehensive supply chain planning, helps provide real-time responses to real-time demand.

Leap From Insights into Action with AI-Driven Demand Sensing

The first step towards reaping the benefits of demand planning with demand sensing would be to start building an internal data ecosystem that facilitates accuracy and eliminates delays in business processes.

AI (Artificial Intelligence) fuels accuracy, efficiency, and reliability across your supply chain. Using AI and Machine Learning (ML) for demand sensing & forecasting in the supply chain is one of the most promising areas of Artificial Intelligence (AI).

AI-powered demand sensing uses machine learning to take supply chain demand forecasting to the next step by using internal and external data sources such as demographics, weather, online reviews, and other trends.

Using external data and modern machine learning algorithms, organizations can outperform networks that are managed manually and adapt to external changes faster. This is when adapting to the right demand sensing technology can create significant impact across the value chain. 

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Anita Raj
Product Marketing Specialist