By Emily Newton, www.revolutionized.com
Artificial intelligence (AI) is growing across all industries, but some have more to gain than others. Pharmaceutical manufacturing is in the position to see substantial improvements from this technology. Part of that is because AI in pharmaceutical processing has several unique use cases.
AI is ideal for data-heavy, repetitive tasks and complex decision making. Many processes under the pharma processing umbrella fall into those categories, making AI an exciting innovation for the sector. Here are seven of these game-changing use cases.
1. Drug Discovery
AI’s potential in pharmaceuticals begins before production even starts. It takes 10 to 12 years on average to bring a new drug to market with conventional means, largely due to long research and trial times. Machine learning could dramatically shorten these timelines.
Machine learning can simulate interactions between molecules to determine new drugs’ efficacy effectively without expensive, time-consuming, real-world experiments. These simulations are faster and more accurate than conventional trial and error. Even though additional testing is still necessary afterward, AI’s efficiency gives pharma manufacturers a significant headstart.
Reducing drug discovery timelines — even by a relatively slim margin — can result in substantial savings over time. Consequently, manufacturers have more room in the budget to spend on other process improvements to optimise production further and drive down costs.
2. Workflow Optimisation
When it’s time to manufacture the drug, AI in pharmaceutical processing can refine production lines to boost efficiency. It starts with using data from Internet of Things (IoT) devices to create digital twins — virtual recreations of the real-world workflow. AI then analyses these twins to find inefficiencies or error-prone processes.
Just as machine learning simulates drug interactions, it can simulate multiple production scenarios. These virtual tests reveal bottlenecks or areas where mistakes are likely to occur, and recommend more reliable alternatives.
These insights reveal the most effective way to manufacture a drug before manufacturers learn from costly mistakes. By minimising the need for trial and error, AI saves pharma companies time, money and resources on the way to streamlining their production lines.
3. Real-Time Equipment Adjustments
AI can also improve pharmaceutical processing by automating equipment controls. For currently manual processes, this means higher precision and efficiency. For conventional automated equipment, AI functionality means adaptability, resulting in fewer errors.
Operating manufacturing equipment is repetitive, unengaging work, which makes it prone to error with human operators. Automation helps, but automated equipment typically can’t account for variability, leading to mistakes when conditions aren’t perfectly consistent. AI helps by monitoring products and adjusting machines in real time to account for inconsistencies.
This kind of real-time analysis and adjustment can improve error compensation rates by 77% in some cases. In turn, these accuracy improvements result in fewer manufacturing defects, resulting in higher throughput and less waste. Those savings are crucial in an industry as cost intensive and complex as pharmaceutical processing.
4. Making Production More Sustainable
A less commonly advertised but important benefit of AI in pharmaceuticals is it makes manufacturing more eco-friendly. Historically, pharma companies aren’t famous for their sustainability, but AI can help through energy insights and real-time adjustments.
AI can analyse production facilities for environmental shortcomings the same way they scan for operational inefficiencies. These insights are significant because seemingly small changes can produce substantial differences. Wet scrubbers can remove half of airborne pollutants at the minimum or as much as 95%, depending on their application. AI can reveal how best to implement them so manufacturers can land on the latter side of the spectrum.
It can also adjust equipment while monitoring their power consumption to balance productivity and energy efficiency. As a result, pharma production tools can maintain throughput goals while using as little energy as possible.
5. Predictive Maintenance
Maintenance is another ideal use case for AI in pharmaceutical processing. Pharmaceuticals involve more complex machinery than many other manufacturing sectors, so breakdowns are a more pressing concern. AI can prevent them through predictive maintenance (PdM).
PdM uses predictive analytics to judge when equipment will require repairs as soon as signs of wear emerge. Because AI is better at drawing these kinds of conclusions from data than humans, it can detect maintenance issues before they’re noticeable to employees. Consequently, manufacturers can address them sooner, saving time and money.
By addressing issues before they grow more severe, PdM can reduce unplanned downtime by 60% and minimise repair costs. Pharmaceutical equipment will also remain in better condition for longer, reducing the chances of waste-producing equipment errors.
6. Supply Chain Optimisation
AI’s impact on pharmaceutical manufacturing extends beyond the production facility. It can also help pharma companies optimise their supply chains to lower costs, reduce lead times, prevent disruption and ensure drugs reach their destinations safely.
Machine learning algorithms can compare logistics partners and suppliers to find the best options for supply chain resiliency and costs. They can also simulate disruptive events in digital twin of the supply chain to ensure it can withstand unexpected scenarios.
When it comes time to ship pharmaceuticals, AI can find the optimal route to ensure fast deliveries. Many drugs also require refrigerated shipment, introducing the risk of equipment errors in transit. AI can analyse data in refrigerated vehicles in real time to ensure they remain in good condition, preventing spoilage.
7. Ongoing Improvements
Across all these applications, AI offers more than simple one-time adjustments. Supervised machine learning — the most common type in use today — gets more accurate over time as it receives new data. This ongoing learning enables continuous improvements.
As pharma companies continue to use AI, the adjustments it suggests will become more reliable, leading to greater cost savings. This increased accuracy will also help adapt to changes in buyer trends or new technologies. That way, pharmaceutical manufacturers can always remain at the forefront of the industry with minimal disruption.
Markets are becoming increasingly volatile, so quick adaptations are crucial. The only way to learn of these shifts and react accordingly is to embrace AI-driven continuous improvement.
AI in Pharmaceutical Processing Will Change the Industry
Manual workflows and intuition are too slow and unreliable for an industry as high-precision as pharmaceuticals to rely on. AI is the ideal solution across many processes within the sector.
AI in pharmaceutical processing enables manufacturers to get all they can from their resources. With a potential that significant, this technology will undoubtedly revolutionise the industry.
