Process Capability Studies (PCS)

Fine-Tune Your Manufacturing Operation

Touchstone Measurement combines the most sophisticated dimensional-inspection technology with a human-element wisdom to conduct statistically precise and remarkably insightful process capability studies that support your manufacturing engineering processes. With Touchstone, you have a valued, objective technical authority that can serve as an extremely reliable, yet productively candid, partner and sounding board in your effort to fine-tune your production line and meet your customer specifications in the most efficient way possible.

Gain the Verifiable Confidence to Green Light Production

The output of any process is expected to have some degree of variability. The question is whether or not you can be confident knowing that variability will remain within your customer’s specification tolerances. Touchstone’s PCS services answer that question with pinpoint statistical accuracy, and shed light on where adjustments can be made, if necessary.

Touchstone conducts 2 different types of studies:

1. Process Capability Study : Determines Short-Term Stability

This study samples a continuous process, run in controlled ideal conditions, during short windows of time—usually between one and 24 hours—as a means to effectively:

  • Evaluate the process’ short-term, stable capability of meeting customer specifications.
  • Identify immediate opportunities for process improvement.
  • Predict the output of the process in the future.
2. Process Performance Study : Determines Long-Term Reliability

This study accrues sample data of a continuous process, under normal conditions, over an extended period of time—usually at least five days. In that time, a minimum of 100 samples, assembled in 20 subgroups of five, are statistically measured for variations both within the subgroup and against the other subgroups. This is a means to effectively:

  • Determine an estimate of process variations when a project is in its early stages and has only produced a few parts.
  • Identify how well a process, statistically speaking, can be expected to meet output specifications for the long term.