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The Case
As a globally operating company, Vetter employs analysts, who have to write many reportings every month. These contained a copious amount of standardized statements and took them a lot of time to write. Therefore, Vetter was looking for a solution to automate their financial reportings for their senior vice presidents to get better insights in their financial status quo and internal KPIs. Above all, they wanted to save their analysts, who had to create these reportings, time to concentrate on other tasks.
The Challenge
So, Vetter’s analysts were writing these reportings manually and had to invest a lot of their work capacity into creating them. As Vetter turned to AX Semantics to be able to automate these reportings, data aggregation was very laborious, due to the fact that all reportings had been created manually by individual analysts. So, at first, all necessary business data had to be collected and verified. Also, Vetter Pharma works with sensitive information, so data protection is essential. That is why the data needed to remain on Vetter’s servers exclusively and must not be saved with a third party software.Their goals:
- simplify internal company reports
- give SVPs better insights into financial situation and KPIs
- reduce workload for analysts
- save money
The Solution
The data scientists at Vetter knew they needed to find a way to automate the whole process of reporting creation. So they decided to use AX Semantics.
Internal server structure
To be able to create automated reportings, initially, Vetter needed to gather data and information needed for the generation of their reportings. However, as such a datapool did not exist, Vetter decided to compile an internal server structure to save all needed data necessary for the automation of the reportings. As a positive side effect, this also had business intelligence benefits outside of the cooperation with AX Semantics.Direct API
Data protection was essential, so Vetter and AX Semantics decided on the solution of using a direct API. The API integration ensured the connection between Vetter’s datapool and the system of AX Semantics, so that data exchange was possible. This way, the data between Vetter and AX was easily kept in sync, so the automation could run smoothly, but, at the same time, ensured data storage on Vetter’s servers exclusively.Automated reportings
For example, Vetter Pharma is now able to distribute important reportings to their tier of management automatically. These reportings stem from their structured data pool and are scalable as well as reusable for possible generation of new reports. After Vetter’s data scientists configured the basic setup, the automation of dozens of reportings per month commenced. Due to the automation, the high quality of the reportings is assured.
Example of a monthly report of Vetter, generated with the software of AX Semantics
The Results
Vetter was able to reduce their labor costs and, simultaneously, save time for their analysts to concentrate on other important tasks. Annually, 600 hours of working time is saved due to the automated creation of the reportings. Furthermore, the reportings have a consistent report structure throughout the company and are less susceptible for errors. This means that the overall quality of the reportings has increased due to their automation. The reportings can be delivered much faster than before and many parts of them are scalable as well as reusable. As new drugs have to undergo many regulations, the time-to-market is quite time consuming and a complex process. Therefore, the success of pharmaceutical companies will not only depend on whether it invents great appliances, but rather on its ability to get those appliances to market quickly. One key to success is to generate appropriate financial reportings and track business developments, monitor the market launch and sales.
