Inventories Optimization with Machine learning

Inventory management is a reality for businesses that produce or market tangible goods, and their managers must face the daily challenge of how to manage it optimally, i.e. ensure the availability of goods to meet customer demand, but at the same time keep inventory levels at reasonable levels.

Companies that optimize their inventories achieve benefits such as cost reduction, working capital recovery, sales improvements due to reduction of out-of-stocks, customer loyalty and lower losses due to damage or obsolescence

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: 

Relying on statistical models, machine learning uses external and internal information sources to make more accurate and data-based predictions.  Machine learning engines can use information such as sales reports, marketing surveys, macroeconomic indicators, social media signals, weather forecasts and more. Companies that are incorporating machine learning into their existing systems are achieving 5-15% improvements in the reliability of their forecasts (reaching accuracies of up to 95%).  Additionally, they eliminate activities such as manual readjustments and model calibrations, however, it is important to clarify that these methods require high availability of good quality data and computing power.  

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