Why You Need Demand Forecasting Solutions Using Machine Learning?
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 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 labour, 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 the 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 fuelled by machine learning come in.
Machine Learning In Demand Forecasting Solutions
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.
