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.
The natural variability of botanical material often makes it difficult to ensure a consistent quality process for pharmaceuticals made from plant-based products. In addition, botanical drug products (BDPs) are often produced using a series of separate batch processes, which adds even more variability into the manufacturing process.
Advancements in cell and gene therapy hold promise for the future of personalized medicine, especially for cancer treatments. However, bioprocessing methods for autologous cellular therapies, and CAR-T in particular, often present unique challenges in manufacturing due to the variability of the starting material and unique nature of each batch. Is there a way to create more efficient processes in order to bring down costs and make personalized medicine a viable option for more patients?
In production, your media will pass several different refinement steps. To really understand and be assured about a good progression and state of the production, all of these processing steps need to be monitored continuously. With SIMCA® and SIMCA®-online, both part of the Umetrics® Suite of Data Analytics Solutions, you can confidently monitor and control every step of your process. The web clients allow you to access manufacturing data anytime, anywhere.
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.
Using real-time data analytics monitoring has become the accepted way to monitor processes in several industries. The goal is to detect and diagnose issues as they happen, which is a great leap forward compared to traditional analysis conducted in retrospect. This has been highlighted in a previous blog post.
Using advanced data analytics models in real time opens up a whole new world of possibilities for improving your production processes. Not only does real-time process monitoring provide a level of confidence in your process performance, it can also help improve the overall quality of your production output.
The key to process manufacturing success is a mixture of knowledge and experience supported by mastery of data. A presentation I recently attended put this into sharp focus. A major paper manufacturer was faced with the challenge of maintaining paper smoothness during production. They approached this problem in a way that gave them enormous insight into their process, the ability to control it in real time, and ultimately lead to cost savings and maintained quality. There were also a few added benefits, including the ability to spot, diagnose and solve problems in real time.