A machine learning system analyses past sales data and uses it to forecast future sales in a specified time interval. A suitable selection of training and test sets will ensure the extremely high effectiveness of the forecasts generated. These enable the precise projection of sales, optimised product rotation and reduction of storage costs, while at the same time maximising turnover. All of this translates directly into higher profits.
Sounds great, doesn’t it? But how does it work? How do these methods differ from the standard forecasts based on the experience and intuition of the sales team, or at least on simple statistics, like the average values of sales in previous periods? Put simply, the difference is that automated methods are able to consider vast sets of non-uniform data and to detect relationships within them. Machine learning methods enable much deeper analyses, leading to the identification of previously unknown interactions between factors. Computer algorithms detect causal relationships by looking at a much wider context than a human can.
In inventory planning and the forecasting of volumes of orders, the aim is to maximise sales while achieving the greatest possible savings on the warehousing side. These are two opposite vectors of the same process, and machine learning methods enable them to be appropriately balanced.
Having read about the possibilities available, are you encouraged to implement solutions of this type? We will be happy to hear what you think. Get in touch with us!