A Gartner survey indicates that demand volatility is the top pain point for business executives - irrespective of the industry they are in. It indicates how the demand forecasting has become a critical part of growth strategies for businesses.
According to the Institute of Business Forecasting and Planning, demand planning refers to the “forecasts and experience to estimate the demand for various items at various points in the supply chain”. Simply put, it enables the organization to plan their inventory better, ensure the availability of products as per market need and monitor the difference between actual sales and predicted sales to optimize their production.
The results from a demand forecasting solution define the anticipated customer needs and are often taken as the starting point for supply planning and optimization, warehousing, price forecasting, and shipping. But for an accurate forecast, the organization needs to ensure that its data is up-to-date across the entire supply chain and take-to-market strategy, at all times.
Now the traditional statistical methods (TSM) have been in use for decades. But it involved having to manually add and analyze data from across the supply chain, leading to delayed or inaccurate forecasts.
In addition to the manual labor, it was also difficult for organizations with a volatile market to add variables and sources into the forecast continually. The prediction would then have to be made right from scratch, doubling the effort and the time it takes to get actionable data to plan supply chain, inventory and more.
But with an increased market demand volatility today and the need for being consumer-friendly, organizations need a more efficient way to make demand forecasts on an ongoing basis. That’s where demand forecasting solutions fueled by machine learning come in.
Machine learning has been implemented across different industries in various ways. Its application depends on the availability of data, task complexity and what the organization wants to achieve from the demand forecast.
Is it building a market for an upcoming product, predicting the change in sales for an existing product as the season changes or simply planning the change in prices - the use case of demand forecasts often vary from one organization to the other.
But the two demand planning solutions and methods that seem to be a common use-case across different industries, and have proven to help improve supply chain, market capture, profits, and revenue, include:
1. Predictive sales analytics
One of the most common applications of machine learning in a demand forecasting solution is being able to model the future. Predictive analytics allows the organization to estimate the demand for their products or services, and also understand what will drive the sales and how consumer behavior is likely to change under certain conditions like trend alerts or seasonal changes.
A demand forecasting solution using machine learning aggregates your historical and new data from different sources. This includes data from your ERP (enterprise resource planning system), CRM (customer relationship management system), POSs (point of sales), customer demand studies, marketing surveys, social media engagement and more.
It then cleanses this data to identify data variables that are relevant to the organization and can impact its sales. The algorithm uses this cleansed data to build predictive models to help organizations identify possible take-to-market outcomes and also discover opportunities.
But more importantly, unlike the traditional demand forecasting method, a demand planning solution that uses machine learning, continually monitors the predictive analytics model. It helps the business compare the actuals with the prediction, and improve the forecast accuracy in the future.
For predictive modeling to work, an organization needs to combine its own data with on-ground actualities. Right from economic indicators to things like weather changes that can affect customer preferences and purchase decisions, every little detail needs to be taken into account and fed to the demand planning solution for the machine learning algorithms to make accurate market predictions.
2. Demand sensing
Demand sensing is another application of machine learning that focuses on capturing real-time fluctuations in market demand and consumer purchase behavior. With this demand planning solutions, organizations are able to optimize their forecasts or predictions better on-the-go to match anticipated needs. This is a critical demand forecasting method in industries that have a fast-changing market.
Take, for instance, the fashion industry. What might be trending in the market today, could completely change within a few days simply because a celebrity was seen donning a different trend.
In demand sensing, the data is aggregated from point-of-sale systems and warehouses and is backed by marketing data that highlights customer engagement, trending content and more. Basically, a demand sensing solution captures demand trends on a short term basis and the factors that cause fluctuations in it, enabling the organization to build a data-driven supply chain.
Simply put, in a competitive market, it is becoming increasingly important for organizations to understand their target consumers and needs better. But to be able to do so, they need the technology to analyze their historical data and the existing market trends and use the actionables to predict future demand.
This is where implementing the right demand planning solution comes into play.
Not every business out there requires implementing a costly demand forecasting solution that is enabled by machine learning. But if your market is known to be highly volatile or has absolutely no historical data and requires you to look into a number of variables, a demand planning solution is exactly what you need.
Some of the industries and scenarios in which demand forecasting solutions using machine learning have proven to be successful include new product introduction, products with a short life cycle, weather-sensitive products, and similar.
It all comes down to understanding what demand forecasting is, its importance for your business growth, its possible impact on the bottom-line, the resources available for implementing a machine learning solution and then, choosing the right demand planning solution to cater to your existing as well as changing needs.
The BluePi supply chain optimization suite is powered by artificial intelligence forecasting models. The algorithm takes into account all known features like holidays, the season trends, weather as well as planned changes in collections, stores and promotions/ markdowns for effective demand forecasting that is turned to align with the procurement cycles to ensure better supply chain optimization and headroom for logistics. Learn more about it here.
Want to learn more about demand forecasting or how BluePi uses artificial intelligence in its demand planning solution to help your organization grow? Contact us today.