All You Need to Know About Demand Sensing

Retailers need to predict which products will be in high demand in the future to stock the inventory accurately and meet the consumer demand. This results in improved revenue, and businesses never fail to fulfill orders. It can also reduce the risks of inventory overstocking and understocking, thereby reducing wastage. However, it is impossible to anticipate the demand with 100% accuracy. But by implementing advanced technologies such as machine learning and artificial intelligence, supply chain managers can predict demand accurately through demand sensing software.

While demand forecasting generates accurate results for long-term and mid-term planning, it is generally less productive in the case of short-term planning. This can be achieved by demand sensing.

Let us first have a look at what demand sensing is.

 

What is Demand Sensing?

Consumer demand fluctuates significantly with time, and retail businesses should estimate demand while considering short-term trends and patterns. Rather than working with the exact forecast for a month, you can modify your forecasts by inspecting the newest sales data and making alterations to gain more profits.

 

Demand sensing anticipates demand on a short-term basis by considering consumers' buying habits. Attributes influence sales and track who is buying what and what affects demand for particular products. It uses artificial intelligence and real-time data to generate granular-level forecasts. It leverages machine learning and advanced analytics to determine complex trends and provide insights into external factors affecting demand that might not be noticed otherwise.

 

The Necessity for Demand Sensing in Retail

Demand sensing helps businesses in various aspects such as procuring goods, manufacturing, improving customer service, and scheduling. Businesses can make informed decisions required for producing and distributing goods by providing better customer satisfaction without overstocking items. Several external and internal factors influence demand, such as weather forecasts and seasonality, competitive data, and point of sale, which should be considered for identifying demand trends. Traditional forecasting methods leave scope for errors, but demand sensing can improve the forecasting accuracy by about 40%.

 

Using near-term data for anticipating short-term demand enables retailers to organize the supply chain properly. It ensures that the correct amount of stock is in the proper location at the right time. The local stock can also be utilized for fulfilling demand. It also assists in estimating when the inventory in transit and the warehouse would become obsolete and analyzes the manufacturing ability to meet demand fluctuations quickly.

 

Importance of Demand Sensing in Supply Chain Operations

Demand sensing can be significant for supply chain operations in the following way:

1. Improved Estimations - Demand sensing uses various signals that include real-world events such as market changes, retail sales, order patterns, and promotions for understanding trends and patterns in an effective way.
2. Enhanced Supply Chain Agility - With short-term insights collected from demand sensing, supply chains can efficiently meet market volatility and unexpected fluctuations in demand. Demand sensing software offers the necessary technology to make the supply chains more agile and robust.
3. Improved Inventory Planning - Demand sensing can provide daily demand data for streamlining inventory levels, making the supply chain flexible, and reducing stock levels. It can minimize the handling expense and the cost of overproduction.

 

Steps of Demand Sensing

 To be able to conduct demand sensing in a better way, supply chain managers can adopt the following practices:

 Start with Sell-In Data

Using granular past sales data is a great way to combine demand sensing with the supply chain. It is done by inspecting sell-in demand data quickly and then transforming the forecast as required. The data for shipping history should also be considered while examining sell-in information that can be taken from any supply chain planning system. It can be utilized for correct B2B demand sensing. 

 Incorporate all External Factors

One of the essential factors that help in advanced and accurate demand sensing is that it involves a lot of information points that are not considered during traditional forecasting methods. While conducting demand sensing, a planner may take into account the following things:

●       Businesses based on seasonal trends need to account for weather forecasts and seasonality. The information can help predict weather fluctuations' short-term effects on purchasing decisions and demand. It can also help modify distribution strategies, production, and raw material acquisition.

●       Consider macroeconomic aspects that comprise the whole stock market, the country's GDP, sales, and employment data.

The fundamental aim of amalgamating external factors is to maximize the forecast horizon predicted through demand sensing. If more factors are considered, more accurate forecasts can be generated.

 

3. Encompass all the Valid Data Sources

For obtaining accurate outcomes in demand sensing, it is necessary to consider all the crucial data, such as  the downstream sell-out information that includes:

●       Customer order data

●       Channel data,

●       Comprehensive point-of-sale data includes real-time information on region, quantity, date, and time.

This data can help determine trends, alert the supply chain of any fluctuations, and align the plan with what is happening in the supply chain.

When all the three aspects are considered together, that is, the sell-in data, the sell-out data, and the right demand signals, you can get an accurate picture of consumer demand. It also provides you with the opportunity of automated demand sensing that frees supply managers to enhance customer service.

Fountain9 offers efficient demand sensing software for retail, DTC, and eCommerce. Their software, Kronoscope, utilizes artificial intelligence to predict demand correctly. It assists in avoiding stockout situations, and the software also advises methods of minimizing the effect of stockout situations. Chronoscope considers nine factors such as seasonality, promotional marketing, holidays, pricing changes, historical sales trends, markdown events, and so on, for anticipating future demand and optimizing prices.

fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9 fountain9