There is a strong demand for devices such as mobile phones, tablets and large screen TVs all over the globe. The business is competitive, which puts pressure on prices. At the same time, production costs are fairly high due to complex production processes. Consequently, a high yield becomes paramount for good profit margins. Multivariate data analysis (MVDA) is being employed by an increasing number of manufacturing companies to increase yield, and the electronics industry is no exception. This article provides examples of where and how real-time data analytics can be used in the electronics industry.
Over the last several years, the use of artificial intelligence (AI) in the pharma and biomedical industry has gone from science fiction to science fact. Increasingly, pharma and biotech companies are adopting more efficient, automated processes that incorporate data-driven decisions and use predictive analytics tools. The next evolution of this approach to advanced data analytics incorporates artificial intelligence and machine learning.
The key to being able to innovate, improve and streamline your processes often lies in gaining as many insights as you can from a variety data sources scattered throughout your operations. Making sense of all that data can be difficult. But it's not an impossible dream.
Most biopharma manufacturing companies are keen to adopt new methods that would streamline production, reduce errors and ensure product quality. That was the goal of Bristol-Myers Squibb when they implemented a complex real-time process monitoring system that involved integrating data from a number of different technologies, systems and vendors to gain greater control over complex batch processes.
In many manufacturing industries, variability in raw materials can lead to unexpected and undesirable changes in the final products. In regulated industries such as pharmaceuticals, this is especially problematic due to the need to maintain carefully controlled processes that stay within approved regulatory parameters for drug development and production. Embracing a total company-wide digital transformation enabled Amgen to align data across multiple systems to not only control, but also predict unacceptable deviations in time to make necessary adjustments. Read on to find out how they used data analytics to implement real-time process control.
Several trends in the food and beverage industry are leading to challenges for manufacturers that can be best addressed with data analytics. With growing digitalization, more companies have access to the kinds of data that can transform their processes to meet the latest consumer demands as well as to shorten time to market, reduce costs, and shrink health and safety risks.
In the last few years, many pharmaceutical companies have started investing in continuous production, and some have already succeeded in filing new pharmaceuticals using a continuous flow manufacturing process. This article summarizes a study at GlaxoSmithKline, GSK, where real-time multivariate monitoring added value to the development of a continuous production process of an active pharmaceutical ingredient (API).
The pharmaceutical industry, including R&D, manufacturing and also product sales and use, creates a lot of data. The question is, what can we do to understand our data better, get more out of it, and unlock its potential in the most rational way possible to get to the knowledge we need? And how can we gain control over our research, or the processes needed to generate a stable, reliable product that consistently meets regulatory requirements? The answer is Multivariate Data Analysis.
In agrochemical, pharmaceutical and other industries that manufacture complex chemicals, finding ways to reduce waste and improve inefficiencies often hinges on selecting the right chemical compounds. Data analytics can help manufacturers find alternative compounds that meet complex requirements, decrease raw material usage or enable more cost-effective, sustainable processes.
A challenge for the regenerative medicine industry is to develop cell culture processes that can be scaled up for high volume production. Finding a better way to scale up commonly used research cells like HEK293T (used for protein expression and the production of recombinant retroviruses or lentiviral vectors) would be beneficial for biologists in many fields of medicine. Dr. Franziska Bollmann, virus scientist at Sartorius Stedim Biotech in Germany, recently conducted two experiments to find out if micro bioreactor systems can help facilitate the transition from the traditional shake flask process to a more improved method optimizing process control.