Collaborate with SIEMENS and NIBRT to leverage bioprocess data for competitive advantage
BioMAC - Advanced Bioprocess Data Analytics
Collaborate with SIEMENS and NIBRT to leverage bioprocess data for competitive advantage
Biopharmaceutical companies struggle to manage the volumes and complexity of data generated all stages of the product lifecycle, and teams can spend significant time manually acquiring and engineering data from disparate siloed systems, then analysing it with tools not developed for bioprocess data needs.
Bioprocess data is generated and stored within a variety of systems such as process control, MES, historians, LIMS and various business intelligence systems. BioMAC provides the ability to efficiently combine these data sources and apply advanced analytics, machine learning and artificial intelligence to improve efficiency and competitiveness.
"We now have a customised app where all the selected data is consolidated, collated, contextualised for on-demand visualisation and analysis. This has empowered users to be able to.. assess these relationships across multiple batches"
Process Engineer Manager, Top 100 Biopharmaceutical Company
Consolidated all historical data in one platform for on-demand advanced analytics and visualisation
Ability to compare
by batch or process
step
Historical
Data identified,
collated, contextualised and prepared for advanced analytics and modelling including K-Means
Clustering, Principal
Components Analysis, Random
Forest,
Ability to add data from new
batches for comparison
Process insights to inform future direction
Enables demonstration
of process understanding for
audit purposes
Empowers users with self-service
data analytics for
improved collaboration
Award Winner
Awards
Pharma Project of the Year (Small) at the Irish Pharma Industry Awards 2019
Judges' Feedback: “Our panel wanted to herald this outstanding
performance by Siemens and NIBRT. BioMAC has a lot to bring to the industry and
the solutions it provides are sure to continue to improve efficiency and
effectiveness in bioprocessing.”