Apply AI to Optimize
Pharmaceutical Process
We focus on quality process like deviation management process. Every deviation has a potential cost impact. Cost of Deviation(CoD) rises exponentially when deviations are found at later stage of products lifecycle. As the risks involved with scrappage, inventory loss, reputation rises exponentially. Inefficiencies in your deviation management process increases CoD. Delays in execution of your process will result in pushing the problem to the later stages of product lifecycle.
We employ process science and data science techniques to solve in real time process optimization problem. Optimizing deviation management process to reduce risk from CoD is a complex problem. Our cloud based platform is aiming to solve this dynamic problem by prioritizing deviations based on real time risk impact.
What We Offer
Our innovative platform can monitor and analyse the dynamics of your process in real time and identify the weakness, loop holes and inefficiencies automatically. Additionally, give you the possibility to optimize your process for cost and efficiency in real time. Process mining technology is the main backbone of our platform.
Visualize Process
Visualize how deviation management process is running in real time.
Identify Inefficiencies
Point out inefficiencies like CAPA Without RCA, RCA Re-Assessment, Delayed RCA,.etc
Mitigate
issues
Trigger automation actions immediately when a particular inefficiencies is recorded.
Optimize continues
Assess the effectiveness of your automation and optimize it real time in a closed loop fashion.
Risk Patterns
Our AI platform analyses thousands of process events from various data silos and identifies in real time risky patterns in execution of deviation management process which can lead to following risks
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Risk of Repeating Investigation
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Risk of Repeating Deviation
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Risk of Long Execution Time
Process Mining in
Pharmaceuticals
Process Mining in Quality Department
German CDMO was able to reduce their end to end execution time of deviation process to 30 days. They reduced the cycle time by 15%. Considering that 1,500 of 5,000 employees are involved in the Trackwise process, this is a breakthrough. They had identified 1M+ variants of the deviation process which was not shown by QMS. Process mining technology was able to show the hidden inefficiencies which helped to focus on the
Process Mining in Regulatory Department
Global Pharmaceutical Manufacturer has products in 66 countries. The regulatory department has the huge task to satisfy various health authorities amid ever changing regulatory requirements. Regulatory Submission Process was used so often that they ended by submitting every 15 mins. Any inefficiencies in this process would burden the whole regulatory department. Process mining gave the regulatory department an idea where the inefficiencies are and how they can fix it real time.
Top 3 problems of inefficiencies in Deviation Process
Cost of Repeating Investigation
According to a 2018 survey among pharmaceutical Quality Managers, a simple failure investigation averaged $8,000 to $12,000 per incident.
Climet, 2018
Cost of Repeating
Deviation
Industry average deviation costs generally range from $25,000 to $55,000, yet they can top $1,000,000 per deviation if product loss is involved. Investigating one deviation alone can cost a company thousands of dollars.
CAI, 2021
Cost of Delaying
Investigation
Poor quality is measured in seven figures, or more. Label-related recalls alone can cost pharma companies more than $100 million a year. A warning letter can set companies back approximately $200 million, and a consent decree could cost upwards of $1 billion
Shafer, 2019
Explore Our Solution
Every deviation in your deviation process has a certain cost associated with it depending on the risk it carries. Inefficiencies in processing these deviations would increase the risk. As QA manager you would like to know at any given point in time which deviations are critical and needs to be prioritized. Our platform sorts this out by recalculating cost of deviation and number of days left for drug to be released. Also, it tries to optimize your process in real time to minimize the risks due to high cost of deviation. So you can actively avoid or minimize those loopholes which block the performance of your process
