Data-drive root cause analysis

Root Cause Analysis is one of the most important factors in determining quality in the pharmaceutical industry. It is the initial activity of knowing the sequence that causes problems and finding a way to solve those problems.

Using root-cause analysis to troubleshoot and resolve problems in the Pharmaceutical, Healthcare, and Life Science industries enables greater uptime and smoother operations. This includes finding the root of problems through data, learning to visualize the possible causes of your problems using statistical methods, and understanding where you need to optimize and what new initiatives are needed.

short, root cause analysis provides a basis for streamlining the

Predictive Analytics with AI

Predictive Analytics is the use of data, statistical algorithms, and AIML (artificial intelligence and machine learning) to predict future outcomes based on past data.

The goal is to provide the best assessment of what may happen in the future and go beyond knowing what has happened.

It can quickly integrate and analyze massive amounts of data using predictive analytics with embedded AI (Artificial Intelligence).

Predictive Analytics can control processes using predictive modeling techniques such as neural networking, regression analysis, and clustering.

Real-time Decision support

Leverage IoT and streaming data to generate actionable alerts to improve quality.

Use predictive maintenance to maximize equipment performance and optimize throughput and yield.

Real-time Decision Support System Decision support system in pharmaceutical companies plays an important role in dealing with critical patient situations.

Also, effective and efficient real-time monitoring must be provided.

Benefits Of Ai Clinical Decision Support Systems

• Enhancing diagnostic accuracy

• Making more informed decisions