Enhancing Stock Management

We recommended an AI-based machine learning engine to indicate the expected amount of time needed for a product to find its customer depending on the location of the point of sale.

We began the project by analysing data that included the history of hundreds of thousands of products, which had rotated through our customer’s stock in the previous two years. The analysis revealed several data-related issues which were caused by imperfections in the data input process. Providing workshops with business specialists allowed all participants to gain insight into how to better prepare data throughout the process.

In the first stage of the project, data was analysed. It included the history of hundreds of thousands of products, which rotated through customers’ stock in the time frame of two previous years. Analysis revealed several issues related to data, which were caused by imperfections in the data input process. Workshops with business specialists allowed all participants to gain insight making it possible to prepare the data for further steps.

In the next stage, our team concentrated on further analysing the data, and developing, and testing models. Prepared models varied significantly. They included regression, classification, and clustering models, and they used data sets with varying levels of complexity.

The most valuable models ended up being classification models which indicated which products would be sold with the highest probability within a particular time frame. The selected model was implemented using BRAINHINT’s AI Farm software, which is a platform for managing machine learning engines and integrating them with business processes.