Demand Sensing: Are You Picking up the Right Signals?
- Forecasting based on historical data is giving way to AI algorithms that optimize capacity by factoring in real-world events
- Demand sensing software solutions create real-time visibility of changing demand signals
- This leads to better forecasting, eases inventory management and reduces operating costs
Supply chain disruptions accompanied by sudden surges in demand have caught many businesses off guard these days. This growing uncertainty in a pandemic-hit economy has compelled businesses to find new ways to accurately determine demand.
But in a rapidly changing market, how can you estimate demand, that too with precision?
Let’s find out.
It starts with demand sensing
In an age of instant gratification, meeting immediate market demand is key to retaining competitive advantage. If a business cannot meet the immediate demand, it can alienate its customers and lose revenue.
Demand sensing is a software solution that leverages advanced technologies such as artificial intelligence (AI), machine learning and real-time data capturing to close the gap between your demand planning and what is happening in your supply chain.
In other words, demand sensing leverages technology to create real-time visibility of the changing demand patterns.
In this process, it factors in real-world events such as market shifts, weather changes, natural disasters, and changes in consumer buying behavior.
The objective of demand sensing is to overcome latency issues by reducing the time between an event and the response to that event. In this way, it helps planners do a better job of anticipating short-term changes in demand.
Real-time visibility of demand enhances forecast accuracy. Accurate forecasts translate directly into better business decisions that can, in turn, ease inventory management, reduce operating costs and help realize key business objectives.
Traditional demand forecasting vs. modern demand sensing
Traditional demand forecasting methods rely on historical shipment and sales data to estimate future demand.
The drawback of this approach is that it does not factor in real-time changes in market conditions and other events that can likely impact demand. There is too much reliance on historical data.
While looking at historical data may be helpful, predicting future demand solely based on past record has its risks.
Also, with such an approach, you cannot effectively respond to market changes and quickly facilitate production and delivery across the supply chain pipeline.
Unlike traditional forecasting methods, demand sensing uses a broader range of demand signals and mathematical tools to create more accurate forecasts that factor in real-world events.
This technique automates the process by collecting massive amounts of data from different sources such as point-of-sale terminals. It then analyzes this data to derive meaningful insights that are then used to estimate demand.
Machine learning algorithms use a predictive modeling technique called classification or decision tree. Advancements in computing power and modeling techniques enable forecasters to run hundreds of slightly different decision tree models, each learning from previous models as well as from observation of actual sales fluctuations.
How does demand sensing work
- Algorithms shift through a large volume of metadata to determine which ones currently have the strongest impact on demand.
- The software constantly monitors real-time, downstream data to provide warnings when your channel partners are running low on inventory for specific items.
- It creates forecasts that consider the impact of real-world events such as hurricanes, snowstorms, shift in consumer preferences and even relevant social media trends. For example, if social media posts about a product become viral, the demand for the product is expected to fluctuate.
- It also monitors where your inventory is within the supply chain. As you become aware of actionable demand signals, it equips you with the knowledge you need to optimally deploy that inventory to capture sales.
Demand sensing and competitive edge
Demand sensing solutions have already been adopted by businesses across a wide range of industries, particularly those with large sales volumes. Some of the big names in the retail sector, for example, have successfully used demand sensing to reduce lead times.
With supply chain digitalization topping the agenda for many companies, demand sensing will allow them to stay ahead of market rivals.
To learn more, download our bulletin: What’s Demand Sensing and Why Should Retailers Care?