Like other sectors, the pharmaceutical industry is subject to cyclical developments that impact demand. These changes may be due to purely seasonal or external factors (e.g. epidemics, loss of patents, the arrival of a competitor on the market, etc.).
Thanks to machine learning, Verteego Brain enables pharmaceutical companies to model the importance of these different factors in order to better anticipate these strategic flows.
The market for generic drugs suffers from a multitude of stock-outs caused by peaks in demand or by the export of drugs at the time of a price spike abroad. In general, a shortage is only experienced by one manufacturer, allowing other players to respond to the shortage by supplying the geographical area affected by the shortage more strongly or by increasing their production. However, the opacity of the market results in disruptions being detected too late, which reduces the responsiveness of producers.
Verteego Brain allows you to detect weak signals in time series by using your customers' data to detect outages at the earliest possible time.
Example ROI: Verteego Brain generates significant results by detecting out-of-stock at the product reference and producer level 1 month earlier with a minimal false positive rate (less than 3%).
In the parallel import of medicines, it is important to streamline data and automate the process of identifying opportunities. This process is essential for the customer to continuously identify new products to be purchased and then marketed.
Verteego Brain enables a rapid return on investment by reducing time spent and increasing revenue from additional sales.
Use case example :
Verteego Brain conducted this project in 3 steps:
Detailed data mining from databases of several tens of thousands of lines.
Development of a database of parallel import opportunities in the cloud to automate the detection of new products.
The project is now in operation.