The first post in the Analytics in Life Sciences blog series covered New Product Development. Today, we will look at how analytics can be employed in manufacturing and quality operations. It is no new revelation that the life sciences industry needs to augment characteristic strengths in research and development by transforming the way products are manufactured. With drug shortages making headlines and manufacturing quality issues cited as the largest contributing factor, how quickly you move from reactive problem solving to proactive process management can determine your competitive advantage. Let’s see how analytics can help transform operations to a robust state with increased yields, reduced defects and improved compliance.

Asset Management and Maintenance

For one, equipment maintenance can move beyond time-based preventative maintenance cycles to predictive maintenance. Imagine a bioreactor with sensors for online monitoring and built-in analytics that leverage asset failure patterns to allow for early prediction of anomalies. This combination helps optimally determine a cost-effective maintenance schedule while maximizing system reliability and equipment uptime. That is, you can now have maintenance teams address potential performance issues during scheduled outages and before they ever escalate. Increased uptime at the constraint resource now amounts to a positive impact on product throughput.

Manufacturing Process Control

Likewise, process sensors configured with anomaly recognition algorithms can notify manufacturing personnel of unusual conditions and prompt timely interventions to return processes to steady state. Tools, like TrakSYS^TM, alert plant personnel of peculiar activity via email and text message; this provides the ability to quickly respond to atypical conditions, eliminating defects and reducing associated production waste. Adding multivariate statistical process control as well, companies can maintain the desired production state and realize the next round of cost savings!

Workflow Leveling

To further reduce wasteful practices and effectively manage resources, companies are adopting the concept of the Rhythm Wheel, a repeatable changeover-optimal sequence of products to be manufactured. Optimal production changeover sequences result in increased available capacity and also enable learning effects, reducing changeover times further and improving production asset yields. This can be further enhanced with iterative machine learning. And, the implementation and associated benefits can be realized for any lean process design. Pharmaceutical companies have even adopted this approach in the Quality Control Lab, reviewing demand rate and resource availability to facilitate workflow leveling and to proactively design balanced and productive roles.

Compliance and Quality Control

Minimizing the time and effort in similar compliance activities is necessary to extend operational cost reduction beyond the production floor. You can modernize your production processes to seamlessly integrate quality verifications and regulatory reporting via 21 CFR Part 11-compliant electronic batch records and record maintenance. Doing so also facilitates the integration of production data with business and compliance data to drive new insights and improvements to the production process. Laboratory Information Management Systems are already used to collect material and product testing results. A fairly novel approach is to integrate this to a data warehouse and leverage predictive analytics for more profound trending and defect analysis, such that inconspicuous changes in Certificate of Analysis elements can actually highlight vendor sourcing or process changes that may require further due diligence. Think about these examples and your company. How and where would improved analytical capabilities benefit your organization? Join us again next time to discuss analytics in Supply Chain Management.