q_alizer: more efficient quality control in the pharmaceutical industry
Q_alizer is a joint venture of Meocon and Panter, which specializes in business intelligence (BI) and process optimization in quality control (QC) for pharmaceutical companies. The web application of the same name helps QC laboratories to visualize and optimize their work processes. Meocon as a domain expert in the field of QC processes and Panter as a software agency have developed it together to facilitate data-driven decisions in QC laboratories.
Spreadsheets at the limit
After production, medicines or vaccines undergo a multi-stage quality control process. The resulting process data is usually recorded by the QC laboratories in Laboratory Information Systems (LIMS), but there is often a lack of simple ways to gain insights and derive measures for optimization and planning. How does the workload develop over time? Which process steps form bottlenecks and lead to costly delays? Which batches can be analyzed together to increase efficiency?
As a specialized consulting boutique, Meocon has developed a set of tools to visualize the processes and make them easier to record. The application developed for spreadsheets made it possible to process and analyze the data quickly. While these analyses were of great value to the laboratories, their provision via spreadsheets soon reached its limits: Countless versions were in circulation and had to be maintained, which involved a great deal of manual effort for both Meocon and the users.
The challenges of cloud processing of process data
In contrast to the spreadsheet solution, the process data is no longer processed on premise at the customer’s premises, but by Q_alizer in the cloud. This requires an integrated data connection and processing and places increased demands on data security.
To ensure the smoothest possible data connection, Q_alizer can access data sources defined together with the customer directly and automatically. Depending on the customer’s infrastructure and requirements, this is done by exchanging data files or by connecting the customer’s on-premise or cloud database via a VPN connection.
As the process data has different formats and structures from laboratory to laboratory and comes from different sources, it must be converted into a standardized data model during the feed-in process. The data therefore passes through an automated pipeline for processing, which also detects incorrect data.
Finally, particular attention was paid to data security. With Google Cloud, an infrastructure was chosen that offers highest standards offers. For example, data storage and transmission is always encrypted. Security is also a priority in application development and is tested on a regular basis.
”Thanks to Panter, we were able to significantly accelerate the development cycles at q_alizer and adapt our application to customer requirements more quickly.
Juri SarbachData Engineer q_alizer
Scalable, cloud-based web application
With Q_alizer, Panter and Meocon have developed a web application that can be used as cloud-based software as a service. QC laboratories can now analyze their processes directly in the web browser. Thanks to the cloud infrastructure, the application is highly scalable and can process data sets of any size. At the same time, there is no need to deliver and install software at the customer’s premises. This simplifies the further development of the application and enables a faster pace of development.
Q_alizer accelerates and reduces laboratory costs
Thanks to Q_alizer, QC laboratories worldwide gain valuable insights from their process data and can therefore process their analysis batches more efficiently. This means that they can be released more quickly, which in turn reduces the capital tied up in the process and enables shorter production times.
Functions and innovation
Dashboard: The process data and key performance indicators (KPI) are visualized in such a way that users can see the workload of their laboratory at a glance. The KPIs are calculated directly in the data warehouse (BigQuery). This allows even the largest amounts of data to be processed efficiently.
Automatic data feed: Depending on the infrastructure and requirements of the laboratories, the process data can be fed directly from the LIMS, retrieved from a customer SharePoint or imported manually via file upload. This guarantees an up-to-date view of the data.
WIP simulator: Laboratories can use a simulator to simulate their workload (work in progress, WIP) depending on the sample receipt. This allows scenarios to be developed and the use of resources to be better planned.
Planning: The planning module can be used to calculate the optimal composition and bundling of analysis batches so that resources (employees and analysis tools) can be deployed and used in the best possible way.
Single Sign-On: Q_alizer supports identity providers for user authentication. Therefore, in addition to the traditional email password entry, existing customer user accounts can be integrated for login, for example from Microsoft Azure or Google Workspace or via SAML. This allows customers to enforce their own company security standards when logging in to Q_alizer.
Technology
Q_alizer runs completely serverless on Google Cloud, so that the Q_alizer team can focus entirely on application development and does not need any resources for operating and maintaining the infrastructure. This scales automatically with the number of users and the amount of data, thus ensuring constant performance. The core components of the architecture are Firebase for the frontend, Cloud Run for the backend/middleware and BigQuery as the data warehouse. The frontend was developed in Angular, the backend in Python.
Google Cloud was selected because it provides cutting-edge technology for this type of serverless data processing and analysis and meets the highest requirements in terms of scalability and data security. The data is stored and processed in data centers in Switzerland.
Beat Seeliger
Co-Founder & Partner
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