One way to optimize inventories is through improved demand forecasting, which consists of estimating probable future demand for a product or service. This activity is usually also known as demand planning, which is a process that begins with forecasts but is not limited to this.
As a company increases the levels of accuracy of its forecasts, its inventory levels can be adjusted and it can perceive the benefits of this change
For forecasting processes, traditional statistical methods have existed for years and are still used today. These methods use data from the past to forecast the future (common practice is to use data from two or more years); time series can be constructed to forecast sales, although these models are not entirely accurate due to changes in demand and market volatility. That is why forecasting activities are complemented with machine learning techniques.
It is important to consider that not all information is always available to forecast all products, so it is important to determine how feasible it is to accurately forecast a product before starting a project of this type. It should be analysed that the minimum data required are available:
- Historical prices and sales quantities
- Product & Shop Directory
- Marketing Campaigns
Some of the cases in which machine learning models have a better performance than traditional models where
Demand patterns are volatile
Short and medium-term planning scenarios should be carried out
You have products with a short life cycle
There are many models of Machine learning and although there is not one that fits all situations, some of the most efficient and most used for forecasting are
- Linear regressions
- Random trees
- Gradient boosting
- Deep learning