Control- SPC

Statistical Process Control (SPC) is a scientific, data-driven methodology for quality analysis and improvement.

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Statistical Process Control (SPC) is an industry-standard methodology for measuring and controlling quality during the manufacturing process. Quality data in the form of Product or Process measurements are obtained in real-time during manufacturing. This data is then plotted on a graph with pre-determined control limits. Control limits are determined by the capability of the process, whereas specification limits are determined by the client’s needs.

Control Limits on an XBar Range Chart


Data that falls within the control limits indicates that everything is operating as expected. Any variation within the control limits is likely due to a common cause—the natural variation that is expected as part of the process. If data falls outside of the control limits, this indicates that an assignable cause is likely the source of the product variation, and something within the process should be changed to fix the issue before defects occur.

With real-time SPC you can:

Dramatically reduce variability and scrap
Scientifically improve productivity
Reduce costs
Uncover hidden process personalities
Instantly react to process changes
Make real-time decisions on the shop floor
Visit our Case Studies page to learn how top manufacturers are using SPC.

Measuring the ROI of a Real-Time SPC Solution
To quantify the return on your SPC investment, start by identifying the main areas of waste and inefficiency at your facility. Common areas of waste include scrap, rework, over inspection, inefficient data collection, incapable machines and/or processes, paper-based quality systems and inefficient lines. You can start to quantify the value of an SPC solution by asking the following questions:

  • Are your quality costs really known?
  • Can current data be used to improve your processes, or is it just data for the sake of data?
  • Are the right kinds of data being collected in the right areas?
  • Are decisions being made based on true data?
  • Can you easily determine the cause of quality issues?
  • Do you know when to perform preventative maintenance on machines?
  • Can you accurately predict yields and output results?

For more detailed information about SPC and SPC software, read: