CAMO Software

Take your process understanding and control to a new level with CAMO Software

Understanding and managing processes is paramount in any manufacturing or engineering company. In many cases, those processes can be complex, with a large number of variables potentially affecting the outcome.

Yet most manufacturers still rely on traditional Statistical Process Control (SPC) software which uses ‘univariate’ statistical analysis such as mean, median, standard deviation etc. Unfortunately, univariate statistics often miss the underlying patterns in process data. This is where advanced Multivariate Statistical Process Control (MSPC) can give manufacturers and engineers an edge.

What is Multivariate Data Analysis?

Multivariate data analysis is an advanced statistical approach which identifies all of the critical variables and underlying patterns in a data set. Importantly, it also shows the relationships between variables and how they impact on each other – essential when trying to understand complex processes.

MVAvsUnivariate_for_Process_test_page

 

 

Multivariate Statistical Process Control (MSPC) applies these powerful methods to process and manufacturing data, giving you a better understanding and control over your processes.

Advantages of Multivariate Statistical Process Control over traditional SPC

Less control charts = less margin for error: MSPC simplifies the job of process operators by showing all process variables, including relationships which cannot be detected with univariate statistics, on just one or two control charts. This removes the need for control charts for every individual variable.

See the full picture and find underlying patterns in data: Multivariate analysis cuts through data to show which process parameters are interacting with each other and crucially, which are related to defects. MSPC also helps you identify which defects are related and which are diametrically opposed to each other.

Make process improvements based on deeper understanding of process behavior: Many manufacturers try new strategies to resolve or improve processes, only to find the adjustments create unforeseen problems elsewhere. MSPC helps you understand the interaction between variables, allowing you to model and predict the effect of a new strategy before implementing it.

Benefits of multivariate process monitoring and control for industry

Reduce problems during scale-up from R&D to production: Accelerate pilot plant activities and optimize testing during the development / engineering stage. Problems with raw materials or design flaws can be spotted faster and easier before they are scaled up from the lab to production.

Prevent process failures: Reduce production problems and failures by identifying drifts in a process before they become critical. MSPC enables process operators and managers to make informed decisions in real-time on the plant floor.

Improve product quality: Powerful diagnostics let you identify precisely where faults occur so you can improve product quality, thereby reducing warranty claims and even the risk of recall. On-line quality control helps minimize the need for expensive and time consuming off-line testing.

Reduce process costs: Less failures and higher quality invariably leads to lower costs through reduced waste and scrap, energy, rework, overtime etc. Even a small improvement in process efficiency can lead to a noticeable improvement on the bottom line.

Optimize processes: A better understanding of process behavior enables on-going process optimization programs, improved productivity and increased operational efficiency. MSPC gives you deeper insights and visibility so you can reduce variations and forecast more accurately.

Reduce time to market: Enhance process and quality improvement programs on individual production lines and across sites. Knowledge transfer and more efficient processes can lead to significant operational improvements, so you get your products to market faster and at lower cost.

 Increase overall equipment efficiency (OEE): Pinpoint anomalies in equipment performance to help increase uptime, reduce unscheduled maintenance, implement corrective and preventative action (CAPA) programs and get the maximum return on investment in plant and machinery.

Who should use Multivariate Statistical Process Control

MSPC can used by R&D teams, process engineers, production and manufacturing teams, quality control, maintenance teams and Six Sigma/Lean groups.

Industries

Multivariate data analysis and MSPC is already used in a wide range of industries including: aerospace & defense, agriculture, automotive, cement & glass, chemicals, electronics, energy, engineering, food & beverage, general manufacturing, medical devices, mining & metals, oil & gas, pulp & paper, pharmaceuticals & biotechnology, semi-conductors, steel, water & waste water.

Process Pulse: flexible, easy to use real-time process monitoring

The Unscrambler® X Process Pulse lets you identify critical variables and their impact on a process using powerful multivariate diagnostics. The software can be run out of the box and is ideal for pilot plants, test beds and small process lines. Process Pulse can be integrated with existing control systems, giving them the power of multivariate statistical process control.

Features and benefits of Process Pulse

– Affordable for even small process environments and delivers a fast return on investment

– Powerful real-time multivariate analysis and diagnostics enabling early fault detection

– Easy to set up and use for process operators without the need for specialized statistical knowledge

– Flexible to work with almost any type of data, including distributed control systems (DCS), SCADA etc.

– Scalable and can integrate with third party control systems through OPC and database connectivity (ODBC)

Click here to read more about Process Pulse

Click here to download a free trial of Process Pulse

Related topics:

Multivariate Analysis (MVA)

Smart Manufacturing

Process Analytical Technology (PAT) & Quality by Design (QbD)

PDF: What is Multivariate Analysis?

PDF: Monitoring chemical processes

For more information, please visit www.camo.com

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