Issue link: https://www.e-digitaleditions.com/i/1468049
Pharmaceutical Technology TRENDS IN MANUFACTURING 2022 eBOOK 61 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 de- grees 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 ef- fect 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 man- ufacturing processes become automated, the num- ber 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 pro- duction as well. According to a World Economic Forum and McKinsey & Company case study, Novo Nordisk boosted output at one facility by optimiz- ing production through the use of big data, ad- vanced analytics, and AI (1). The manufacturing site used digital scheduling and work-management applications plus automated overall equipment ef- fectiveness 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, al- lowing them to allocate resources, order supplies, and identify problems before they become severe. The technology also allowed managers to set real- istic benchmarks regardless of work shifts or op- erators. 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 allow- ing 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 expe- rience, these systems become more efficient as they work with more data and on more projects. Pharma- ceutical 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 com- pany time and effort and gives it the best chance of finding innovative compounds. All this newly usable data can reveal some inter- esting insights. To go back to the Novo Nordisk ex- ample from earlier, data insights will allow you to see the full carbon footprint of your operation, even though it involves everything from individual ma- chines to distribution (2). 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