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 industries ranging from biopharmaceuticals to chemicals, executives in today’s manufacturing marketplace face ever-increasing pressures to grow profit margins, reduce time to market and optimize processes across all aspects of their business. Everything from constraints in the supply of raw materials to multiple steps in a manufacturing process can affect productivity—making process optimization an amorphous target.
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.
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 a manufacturing setting where consistent quality matters, variability in how individual technicians and operators perform their jobs can be frustrating for managers. Companies need a way to achieve consistent quality, without reducing the capacity for innovation and improvement.
You may have heard the term Six Sigma used in conjunction with lean manufacturing, a Kaizen approach or continuous quality improvement. Perhaps you thought Six Sigma only applied to large-scale business operations, or that newer philosophies had overtaken Six Sigma as the most updated approach to quality management? But if you're looking for a way to improve your production processes or solve a problem you’re having with quality, Six Sigma might be the answer. Are you and your team familiar with these concepts? Here's an overview.
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.
In manufacturing and other industries that have complex processes, knowing which variables have the most impact on quality and at what point, or knowing which combination of variables to change in order to improve your process, can have a huge impact on the overall quality or profitability of your manufacturing process. But without making expensive and time-consuming changes in the physical processes in order to test all possible scenarios, how can you identify and predict the variables that have the most significant impact on your outputs?
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.