In an environment where drug speed-to-market, efficiency, and compliance are key to success in pharmaceuticals, conventional production approaches are insufficient. Pharmaceutical producers are facing increased pressure than ever before, including compressed timelines for drug development, supply chain disruptions, and navigating more complex regulatory obligations.
To meet these pressures, pharma manufacturers are leaning on AI (artificial intelligence) and emerging connected technologies, including IoT (internet of things), big data, and cloud-based technologies. These connected technologies are revolutionizing the way drugs are developed, produced, and delivered. They are introducing unimaginable levels of agility, safety, and scalability.
In this blog post, we examine how AI and connected technology are reimagining pharmaceutical production and what that means for the future of medicine.
The Current Challenges in Pharmaceutical Manufacturing
Before getting into the technology, it is important to appreciate that the constraints driving changes in the industry are:
- High R&D costs and long development timelines
- Regulatory frameworks that require compliance
- Manual data entry and decentralized data
- Supply chain variability and quality issues
- Demand for more personalized medicines
These constraints all lead to slow time-to-market, increased operating costs, and increased risk of human error – all of which AI and smart systems can mitigate.
How AI is Changing Pharmaceutical Manufacturing
- Predictive Analytics in Drug Manufacturing
Predictive Analytics can utilize AI to build models using historical production data with environmental variables and the properties of raw materials used to make drugs to optimize the quality parameter and consistency of the batch. These models can augment the detection of potentially undesirable anomalies and help alleviate batch failure.
- Smart Scheduling and Resource Management
AI can automate the production scheduling process based on variables like shift changes in workforce member availability, equipment availability to run the batch, maintenance requirements, etc.
The prediction and scheduling, combined with time slots for meetings or downtime, provide a new performance baseline for productivity and have maximum output whilst minimizing downtime.
This adaptive scheduling provides just-in-time (JIT) plant utilization, and companies can minimize the size of overdosing buffer stock and drastically reduce wastes across the supply chain.
- Improved quality assurance through computer vision
AI and computer vision technologies can examine products on a very small scale, spotting visual defects or contaminants much more clearly than any human would be able to do.
For instance, blister pack inspection, verifying tablet coating uniformity, and checking vials for correct labeling can all be accomplished through machine-learning-trained vision models.
- AI-based compliance and reporting
AI can generate automated compliance reports, validate records, and identify potential regulatory compliance breaches prior to audits, saving significant time and human capital as it relates to regulatory compliance.
Compliance with regulatory requirements is a particularly useful use case for AI in highly regulated environments, such as those bound by GxP standards, where data integrity and traceability are particularly important.
Connected Technology in Pharmaceuticals: IoT and Beyond
- Real-time monitoring and the uses of IoT
IoT is typically sensors embedded in physical infrastructure or equipment, and combines various sources of information in real-time to monitor something.
This type of sensor can look at continuously monitor the environment of things like cold storage, and indicate if any temperature, humidity, or pressure has gone outside of allowable values.
This especially applies to biopharmaceuticals, vaccines, or other temperature-constrained products that require cold chain management.
- Digital twins in pharmaceutical operations
A digital twin acts as a real-time virtual representation of physical equipment or processes. In pharmaceuticals, digital twins serve to model and optimize:
- Tablet press activity
- Airflow in clean rooms
- Conditions in bioreactors
Through digital twins, manufacturers can explore many more scenarios and data points in the virtual realm without putting any equipment at risk or waiting for equipment to reset, or ‘cool down’ before testing again.
- Cloud-Based Manufacturing Execution Systems (MES)
Cloud-enabled MES allows manufacturers to view real-time visibility into manufacturing KPIs across different locations, aligning with centralized decision-making for departments.
Cloud systems also offer remote visibility, which is essential in a hybrid-work world post-COVID.
- Blockchain for Supply Chain Traceability
Blockchain effectively adds a secure and immutable layer of transparency across the pharmaceutical supply chain. It can be used as a secure way to:
- Track raw material provenance
- Authenticate shipments
- Keep counterfeit drugs off the market
This inspires trust, transparency, and regulatory confidence, especially for global distribution.
How to Get Started: Steps to Implement AI + Connected Technologies
Here’s a simple roadmap to begin the implementation of digital transformation in pharmaceutical manufacturing:
Step 1. Audit Existing Systems.
Identify where you may have data silos, inefficient manual processes, etc.
Step 2. Focus Initial Efforts on High-Value Areas.
Common starting points: predictive maintenance, visual quality control, and production scheduling.
Step 3. Deploy IoT Sensors.
Start with environmental monitoring and equipment status.
Step 4. Select a Single Common Platform.
With a specific focus on selecting MES or ERP with the ability to accept AI/ML plugins and IoT integration.
Step 5. Train/Up-Skill the Workplace.
Provide your team training to use digital tools and to interpret the outputs available from AI.
Conclusion
The confluence of AI and connected technologies is moving pharmaceutical production from slow, bureaucratic systems tied to (manual) errors into a more responsive, intelligent ecosystem. From predictive quality control to real-time monitoring and automated compliance, these technologies enable organizations to deliver better medicines more quickly and more safely than ever before.
For those pharmaceutical companies who want to continue to be competitive, now is the time to embrace the offerings of smart manufacturing technologies. Start smaller and ramp up to digital transformation or jump into a complete digital transformation; the tools are available, and the future is connected.
To stay ahead in a competitive market, pharmaceutical companies must embrace smart manufacturing, and there’s no better time to start. Tecnolynx offers AI and IoT-powered solutions, purpose-built for regulated industries like pharma and healthcare. Explore our manufacturing tech services and begin your digital transformation with confidence.
