Pharmaceutical manufacturing generates ever-growing volumes of data. This can include various sensors, equipment, quality systems, production processes.
The task is to turn this information into actionable insights.
Here's when the combination of industrial Internet of things (IIoT), artificial intelligence (AI) and advanced analytics can come in handy. Recent studies have outlined several use cases in pharmaceutical manufacturing, including real-time process monitoring, anomaly detection, predictive maintenance, and automated inspection.
What Does Data Mean for Pharma Manufacturing?
Managers in pharmaceutical manufacturing require visibility into various parameters such as:
- Temperature and humidity levels,
- Equipment performance,
- Pressure and process conditions,
- Manufacturing parameters,
- Quality parameters,
- Energy consumption.
Traditionally, this information has been accessed separately, using different systems or reports.
Connected technologies can offer a continuous overview of manufacturing processes.
What is Industrial IoT
IoT integrates sensors, machines and other physical assets with digital solutions.
Typically, this architecture includes the following:
Sensors → Data collection → Analytics → Insights → Human action
For instance, sensors can continuously monitor equipment or environmental conditions, providing information to the central system.
Such setup allows identifying changes faster than in the case of manual monitoring at fixed intervals.
Where Artificial Intelligence Can Help
Industrial IoT provides the data. But AI helps in its interpretation.
Predictive Maintenance
Machine learning algorithms can analyze equipment data and detect abnormal patterns indicating maintenance issues.
It allows detecting maintenance issues in advance before they trigger unplanned equipment downtime.
Anomaly detection
Machine learning models can compare current working conditions with the expected patterns and detect anomalies.
Quality monitoring
AI can help analyze manufacturing and inspection data and detect possible quality problems.
Process optimization
Advanced analytics can process historical and real-time data, understanding the performance of processes and possible ways to optimize it.
Data Quality First
AI cannot solve the problem of poor data.
Accuracy of sensors, calibration, completeness, traceability and system integration are important aspects to consider while implementing AI into pharmaceutical manufacturing environment. Data quality, integrity, security and proper oversight have also been listed by FDA as the key aspects to consider while using AI in pharma manufacturing environment. ([U.S. Food and Drug Administration][2])
It means that companies have to build a proper data infrastructure first before starting scaling AI into manufacturing.
Why Data Quality Comes First
It is impossible for AI to overcome problems with poor data quality.
Such aspects as sensor accuracy, calibration, data completeness, traceability, and systems integration are important to consider during the implementation of AI technologies in manufacturing processes. The FDA also highlighted data quality, integrity, security, and appropriate oversight as important factors for the application of AI in pharmaceutical manufacturing. ([U.S. Food and Drug Administration][2])
That is why companies are recommended to have their data strategy solid first before going further.
AI Doesn't Replace Manufacturing Expertise
Human oversight is one of the key factors to consider.
Although AI can detect patterns and provide important insights, manufacturing engineers, operators, quality control staff, and maintenance engineers are supposed to analyze those findings considering their specific operational and quality needs.
Thus, the best approach involves combining technology and domain expertise.
Starting from Use Cases
It doesn't mean that pharmaceutical manufacturers are supposed to change their whole facility right away.
Instead, it may be easier to identify the problems they face and then focus on a particular solution, for example:
- Equipment often breaks down;
- Environmental conditions are hard to monitor;
- It is difficult to analyze quality data on a regular basis;
- It takes a lot of time to investigate deviations;
- There is no real-time visibility of the operations performed;
Having achieved some results, organizations can implement the same approach to other use cases.
Connected Pharma: The Next Step
The combination of AI + IIoT + Analytics helps pharmaceutical manufacturing become more proactive and data-driven.
The more developed those technologies are, the more chances there will be to combine them with automation, computer vision, digital twins, and process monitoring in order to create connected manufacturing facilities.
To find out more about AI, Industrial IoT, and connected pharmaceutical manufacturing, visit PharmaFlux AI.
Final Words
Data collection alone is not the main goal of pharmaceutical manufacturing.
Connected sensors can help with real-time visibility, AI can help with identifying patterns and possible threats. Reliable data, proper governance, and human expertise can help to maintain equipment better, monitor the processes, and make appropriate decisions.
If you are Dev.to reader and interested in AI, IoT, and regulated manufacturing, pharmaceutical manufacturing can offer you a unique opportunity to see how technology delivers value while meeting quality standards and regulatory requirements.
