Leading manufacturers are significantly improving product development and process performance with powerful analytical tools.
Multivariate data analysis is the investigation of many variables simultaneously, to understand the relationships that may exist between them. Because manufacturing processes are typically highly multivariate in nature – i.e. a large number of variables which are usually interactive – they require multiple measurements to fully understand them.
Multivariate analysis software helps manufacturers understand process behaviour better and implement more robust control strategies, resulting in a wide range of operational benefits directly impacting the bottom line.
Benefits for manufacturers
Increase yields: Identify which combination of process variables produce the highest yield, and improve process understanding generally to find the optimum settings.
Improve quality: The root cause of quality problems can often be difficult to identify. Multivariate analysis gives deeper insights and can pinpoint the variables or their interactions causing problems. When combined with spectroscopy, it can enable cost effective 100% quality testing.
Improve product safety and sustainability: Quickly and affordably determine authenticity of products, detect contamination and adulteration when combined with spectroscopy. Reduce the use of hazardous chemicals, minimize scrap and optimize processes for more sustainable products.
Reduce process failures: Identify issues in a process before they become problems causing the process to fail, and use powerful multivariate diagnostics to drill-down into the cause of the problem.
Reduce variability: Maintain consistent end product quality more effectively by early detection of drifts in process using multivariate predictive models.
Cheaper product development: Experimental Design lets you develop products faster and more cost effectively by reducing the amount of test and experiments needed, as well as less use of destructive testing by replacing with spectroscopy.
Faster time to market: Move products from the pilot plant to full production scale faster. Multivariate analysis provides the product and process insights needed to help make scale up smoother to get to market faster.
Client applications of multivariate analysis
Pharmaceuticals: A pharmaceutical company applied multivariate analysis together with Near Infrared (NIR) spectroscopy to monitor their fluid bed dryers, enabling them to achieve consistent final product quality using timeline and process trends.
Injection molding: An industrial manufacturer used Multivariate Statistical Process Control (MSPC) for real-time analysis and control of an injection molding process. The company collected data comprised of several measurements from transducers and sensors that were interfaced to an injection molding process which was used to predict final product quality. By monitoring real-time measurements and predictions of product quality, the manufacturer was able to make the necessary process adjustments in real-time to maintain quality, as well reducing the dependence on off-line testing and minimize scrap, waste, energy and rework.
Industrial Manufacturing: An industrial manufacturer used multivariate models to predict failure in their rotating machinery. Each piece of equipment was instrumented with multiple sensors, collecting a large amount of data. However, traditional Statistical Process Control (SPC) was unable to accurately predict which equipment would break down or which combination of variables would lead to failure. Using the same data but applying multivariate predictive models, they were able to predict with 100% accuracy which specific equipment would fail.
Food & Beverage: A food manufacturer was experiencing major product quality issues. Despite having masses of data, their univariate analysis and traditional SPC could not identify the problem. The client began using multivariate analysis which quickly let them identify and resolve the quality issue, saving millions of dollars per year on one product line alone.
Pulp & Paper: A paper manufacturer used multivariate analysis to measure a range of paper types and determine which quality parameters influence print-through. Multivariate analysis was also used in conjunction with spectroscopy to improve paper strength and thereby reduce the rate of roll tear, enabling them to make significant savings through less waste, scrap and energy.
Food & Beverage: A beverage manufacturer used multivariate data analysis to get deeper insights for market segmentation. Their analysis uncovered underlying relationships between consumer preferences in relation to product attributes, leading to the successful launch of a new product.
Food & Beverage: A manufacturer of common potato based snack foods used multivariate analysis to develop a market leading product in a very homogenous category. They analyzed the results from sensory panels, consumer preference data, demographics and socioeconomic factors, allowing them to refine their product and marketing offering.
How CAMO Software can help
CAMO Software specializes in Multivariate Data Analysis solutions, working with the world’s leading manufacturers. Established in Norway in 1984 and with offices in the US, Japan, Australia and India, over 25,000 people in 3,000 organizations use our solutions to explore large, complex data sets and better understand processes in R&D, engineering, production and quality control.
For more information download our free e-book Multivariate Data Analysis for Dummies at www.camo.com/par/MVA-for-Dummies
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