Advanced analytics help to reduce failures and improve yields and quality

21 May 2013

The need to reduce costs and improve product safety and sustainability, has resulted in powerful tools, traditionally used by analytical groups, being transferred to the plant floor, in a bid to reduce failures and product variability and to increase yields and product quality.

CAMO Software offers multivariate data analysis software and solutions for manufacturers in the pharmaceuticals, medical devices, food and beverage, chemicals and animal feed industries.

Multivariate data analysis (MVA) is the investigation of many variables simultaneously, to understand the relationships that may exist between them. Until recently the technology has been primarily used in laboratories and technical groups, rarely being applied to production processes.

Today, this is changing as manufacturers looking for a competitive advantage seek to make use of the data collected during production operations to offer greater insight into product development and process performance. Because manufacturing processes are typically highly multivariate in nature they require multiple measurements to fully understand them.
Yet most Statistical Process Control (SPC) systems rely on Univariate methods which only look at single variables, one at a time. Univariate statistics tend to fail when analysing complex systems because they cannot detect relationships between the variables, often the cause of process upsets. Multivariate analysis tools allow manufacturers to better understand process behaviour and implement more robust control strategies. This enables them to run processes closer to limits, use lower cost components, reduce energy use, reduce cycle time, minimise waste and rework.

Manufacturers who have adopted these tools can quickly see improvements in their operations and on their bottom line. Brad Swarbrick, vice president of Business Development at CAMO Software, explains further: “We worked with a food manufacturer who had a quality problem which they could not identify the cause of using their SPC tools. After analysing the data with multivariate methods, they worked out what the issue was and adjusted their process accordingly, saving them around €1 million per year in scrap, rework and energy costs alone.

“Another client in the pharmaceutical sector applied multivariate analysis together with Near Infrared (NIR) spectroscopy to monitor their fluid bed dryers to achieve consistent product quality using timeline and process trends.” CAMO’s solutions allow multivariate models to be developed by Technical Service groups or CAMO’s consultants, and then applied to real-time production processes. The solutions can be used standalone to analyse off-line data, connected to databases or scientific instruments such as spectrometers, or integrated with control systems for use by process operators.


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