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.
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 life science biopharma manufacturing, demonstrating consistent, repeatable processes is essential both for regulatory compliance and product quality. Being able to create data-driven, performance-based objectives, and aligning the process control strategies with compliance and business performance objectives, allows companies to take their data analysis to the next level: the level at which it becomes meaningful for the company’s bottom line.
When it comes to continuous quality improvement and removing defects from a process, Six Sigma continues to be the gold standard in manufacturing and process management. This structured, data-driven methodology for discovering problems relies on rigorous analysis of production and process data. For many companies, engaging in a Six Sigma process can be time consuming or even a bit daunting.
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.
[This blog was a favorite last year, so we thought you'd like to see it again. Send us your comments!].
Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.
Product development and innovation are important elements for the survival of many companies. Whether introducing a new food flavor or adding new product features, understanding consumer preferences can help guide both design and production decisions. The right decisions can make a product launch more successful, and ultimately more profitable.
Formative assessment has come into focus in recent years. In Sweden, the use of formative assessment is typically emphasized in the curriculum of upper secondary schools. However, scientific studies show both positive as well as no effects at all of formative assessment on student performance.
Furthermore, formative assessment has proved to be time consuming, which obviously is a problem if it has no effects on learning. A new thesis by Daniel Larsson at the Linnæus University, Sweden, shows that multivariate data analysis, MVDA, can be used to give some answers about the effectiveness of such teaching practices.
Multivariate data analysis (MVDA) is a statistical technique that can be used to analyze data with more than one variable in order to look for deviations and understand the relationships between the different data points. In practice, this can mean taking data from a number of different sources and turning it into meaningful information from which you can draw some conclusions.
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.