In the midst of a global crisis, many industrial manufacturing operations— including those in the chemical industry— are faced with shortages of supplies and equipment, or staff reductions, and finding it difficult to keep operations working as normal. Are there process improvements or tools that can be used to manage production more efficiently during this time of COVID-19 (and moving forward)?
A team at Glaxo Smith Kline, GSK, has carried out several studies using Raman spectroscopy and multivariate data analytics in order to monitor upstream cell culture processes. The studies show that Raman spectroscopy is a valuable analytical tool for enabling real-time monitoring and control of production bioreactors and that improved control can lead to improved product quality. Below is a summary of the studies.
Keeping your pharmaceutical manufacturing processes under control is important not only to ensure a quality product, but also for regulatory compliance. Process or raw material deviations can affect the downstream quality of a product and could mean tossing out an entire batch or end product if process corrections aren’t made soon enough — or if you can’t document that a correction was made before it affected your critical quality attributes.
For pharmaceutical manufacturers, a process deviation may not only mean a bad batch that affects a downstream process, it can also risk a regulatory violation that leads to fines or expensive market setback, or worse, it could endanger the health of the patient.
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.
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 2019 Umetrics User Meeting drew more than 102 engineers, operations managers, process experts, researchers, and data scientists in industries ranging from biopharma to food and beverage to chemicals who gathered to share ideas and insights into new methods for streamlining their processes, reducing waste and cost of goods sold.
Injection molding is the most important production method for manufacturing plastic components used in products ranging from cars to medical devices. Although the plastic components themselves are often inexpensive to produce, any defect can lead to expensive errors that can affect the performance or safety of the finished product. Creating a system of early fault detection and continuous process improvement can mean big payoffs for manufacturers.
In industries that depend on bioprocessing, achieving the highest possible yields in the shortest time frame, while keeping costs down and product quality high is often challenging. Meeting these goals requires having a well-designed, well-defined and well-controlled process. And at the core of any effective process control is a set of effective process modeling tools.
Consumers expect a certain consistency in quality and taste from the food and beverage brands they love. But many factors can influence the way a product tastes when it reaches the consumer – ranging from the manufacturing process to seasonality of ingredients to storage temperatures. Similarly, a number of other factors may influence the overall quality attributes that matter, such as alcohol content of beer or stability of the whiskey aging process.
In bioprocessing today, a shift is happening that takes the ability to monitor, optimize and control processes to the next level. Whereas in the past manufacturers aspired to measure data in order to find out why a bioprocess action happened (using descriptive and diagnostic analytics), today we are able to use predictive analytics to determine what will happen in a bioprocess based on specific process data measured in real-time. This migration “up the food chain” to a higher level of data analytics requires automation, ongoing process monitoring and the ability to make adjustments in real-time.
For manufacturing companies, process control is essential— even for those producing low-cost items such as small plastic parts. That’s because even when units are small and inexpensive, the cost of defects becomes exponentially higher when they reach the next manufacturing step at another plant.