The Lighthouse Effect: Using Technology to Improve Life Sciences Manufacturing

Prior to the COVID-19 pandemic, the bio/pharma manufacturing industry had been relatively slow to embrace the ongoing digital revolution. But the pandemic forced the life sciences industry and drug developers to rethink their hesitation toward digitization to quickly produce and distribute new vaccines under unprecedented conditions. Now, technology is being used as a solution to improve efficiency and sustainability in life sciences manufacturing.

Those who have led the way are said to be creating the “lighthouse effect.” Just as actual lighthouses help sailors navigate dangerous waters, lighthouse companies serve as guiding lights for charting the course of an entire industry. For the life sciences manufacturing industry, becoming one of these luminaries relies on a mix of advanced technical capabilities and coming up with innovative applications for the technology. In the age of Big Data, artificial intelligence (AI) and digitization will play lead roles in achieving lighthouse effects.

Lighthouse manufacturing techniques utilize technology and automation to streamline production. Bio/pharmaceutical manufacturers can employ systems that optimize the allocation of resources to boost output, track productivity, and improve sustainability by using energy-efficient processes. But what does this mean from a facilities standpoint? Let’s talk about three key areas where lighthouse effects are enacting great change in the life sciences manufacturing industry.

More efficient buildings and products

AI is increasingly being used by lighthouse corporations to find and eliminate inefficiencies in building operations. For example, smart building technology can monitor the temperature of the water being used in a building. It can then crunch the numbers to make determinations, such as raising the temperature of the boiler by five degrees will be more efficient overall because it will take less time and energy to heat the water to the average-use temperature. This sort of technology is called a smart building management system (SBMS), and it’s a crucial component of the lighthouse effect in the life sciences manufacturing industry. It also makes it far easier to ensure a building qualifies for leadership in energy and environmental design (LEED) or the WELL building certification.

AI also powers automation efforts. As more manufacturing processes become automated, the number of human employees needed in the building gets lower. The fewer people in a space, the less square footage these buildings will need. This makes it easier for companies to upgrade existing buildings rather than build new ones, as they can repurpose space that is no longer needed.

Lighthouse techniques apply to life science production as well. According to a World Economic Forum and McKinsey & Company case study, Novo Nordisk boosted output at one facility by optimizing production through the use of big data, advanced analytics, and AI (1). The manufacturing site used digital scheduling and work-management applications plus automated overall equipment effectiveness monitoring and digital performance surveillance to boost production capacity without expanding the facility. Managers could see the real-time status of production lines and equipment, allowing them to allocate resources, order supplies, and identify problems before they become severe. The technology also allowed managers to set realistic benchmarks regardless of work shifts or operators. The result was increased people efficiencies and a significant reduction in downtime.

Better data evaluations

The magic of AI comes from its ability to make sense of huge quantities of data, revealing patterns that might otherwise go unnoticed and allowing companies to process data in bulk much faster than by hand. This allows for the implementation of risk-based qualification to accelerate the process of getting facilities and manufacturing techniques approved by FDA. Life sciences manufacturers can conduct trials and studies at scale and format that data for submission to FDA.

Automation plays an important role in all of this as well, speeding up the process of working with all that data and getting it into an acceptable state. Because leading AI has the ability to learn from its own experience, these systems become more efficient as they work with more data and on more projects. Pharmaceutical giant Novartis is just one company using AI to help speed up the development of new drugs and therapies (2). Using algorithms, the AI scans images of cells treated with untested molecules to see what might be worth further research. This saves the company time and effort and gives it the best chance of finding innovative compounds.

All this newly usable data can reveal some interesting insights. To go back to the Novo Nordisk example from earlier, data insights will allow you to see the full carbon footprint of your operation, even though it involves everything from individual machines to distribution (1). It’s all data, and that means AI can make sense of it altogether.

A key to this is the edge data center, which gets its name from its smaller footprint that enables it to be built on the “edge” of urban centers—sometimes even within the same building as the facility using the data center. This proximity reduces the latency in sending data back and forth between the life sciences manufacturing facility and the data center, which makes a difference in the perceived performance of these sophisticated AI applications.

Faster production schedules

Incorporating AI and analytics lets life sciences manufacturers build facilities and products faster. Much of this has to do with eliminating inefficiencies in every step of the manufacturing process. Having increased visibility into the supply chain makes it easier to coordinate phases of a construction project and helps achieve connections to other manufacturing facilities to improve supply chain efficiency. Again, this grants unprecedented visibility into considerations, like the overall energy use or carbon footprint of the production process.

Lighthouse techniques reduce supply chain issues by allowing bio/pharma manufacturers to see things at a network level and identify where things either work or don’t work. This allows manufacturers to act proactively rather than reactively to ensure production schedules stay on track and to account for shortages or overages before they happen. AI can utilize data in other ways. It can enhance quality controls by searching for anomalies in production, allowing issues to be corrected early in a batch. It can predict which machines in the facility may fail, allowing for predictive maintenance and virtually eliminating unscheduled downtime.

Every industry has leaders who show others the way via the lighthouse effect. But companies who have yet to implement the lighthouse effects could begin the process today, including utilizing AI and digitalization. All it takes is the proper investment in technology and the right vision to see how that technology can create meaningful change. Thanks to the increasing availability and affordability of AI solutions, including off-the-shelf AI systems that can perform SBMS tasks, execute mass data evaluations, and better manage supply chains and production schedules, there is a low barrier to entry when it comes to creating a lighthouse effect.

References:

1. World Economic Forum and McKinsey & Company, “Global Lighthouse Network: Reimagining Operations for Growth,” Whitepaper, 2021.
2. C. Schmidt, “Machine Learning Poised to Accelerate Drug Discovery,” Novartis, May 7, 2018.

About the author

Gul Dusi is a director for Linesight.

Article details

BioPharm International
Volume 35, Number 2
February 2022
Pages: 24–25

Citation

When referring to this article, please cite it as G. Dusi, “The Lighthouse Effect: Using Technology to Improve Life Sciences Manufacturing,” BioPharm International 35 (2) (2022).