Do you want to be one step ahead of the competition?
The combination of artificial intelligence with inventory control brings significant improvements in logistics centers and manufacturing companies. That is why they are looking for dedicated solutions minimizing inventory to an optimal, safe level and ensure high flexibility in delivery.
Learn a story of our client, who believed that his inventory management methods were outdated and time-consuming.
Our client, a global manufacturing brand, started an aggressive development plan to get more customers at a better price. To pursue this goal, making meaningful data transformations and analyses. In addition, he wanted to monitor the prices of products, Our customer, a supply chain company, noticed that they had a considerable amount of data on products, suppliers, orders, shipments, customers and retailers that they could not process and properly use by conventional methods. In particular, the uncertainty in predicting demand trends and accurate forecasting was the reason for lost opportunities.
The deep analysis allowed us to see the big picture of the client’s business and deliver solutions to the team with much more frequency and granularity than ever. In addition, the system helped spot patterns and suggest actions, thanks to which our client was able to replenish almost expiring stocks and meet the demand faster quickly.
We exploited AI to analyze the numbers of manufactured products based on data from the past and predict the production volume in the current period. Predictive analytics identified stock turns, ensured reliable forecasts and a lean inventory.
In addition, we moved the entire IT infrastructure to the Cloud to ensure smooth data handling and storage using Big Data techniques. That greatly improved the security of the technological environment and provided space for scalability.
How did we overcome obstacles
The most outstanding value of our client was the data collected over the years, which suited perfectly for effective forecasting. However, its enormity also turned out to be a weakness of this project, which we had to overcome. That is why we: