Understanding Process Capability Analysis and its measure set (Cp, Cpk, Pp and Ppk)
Do you know if the manufacturing processes within your organization have what is needed to be effectuated repeatedly? A critical process to uncover variability, meet performance goals, and consistently deliver exceptional results is the Process Capability Analysis (PCA).
Learn to measure process capability and carry out useful manufacturing analysis with this article. Harness this essential process analysis tool and improve efficiency in your organization.
The core principles of Process Capability Analysis
Overall, this evaluation uses data from an initial run of parts to predict the outcome of the sequence in terms of repeatability, to check whether a manufacturing process can produce parts that meet determined specifications over and over or not.
The thing is that this is a critical study to deliver to the organization’s customer as part of a PPAP, and on more occasions than you may think it’s not done. In a nutshell, a capability analysis measures consistency within the part production, according to the upper and lower limits. The goal is to determine how often a process meets its desired outcomes.
Here are its 5 core principles, according to UMass Amherst:
- Specification limits: Manufacturing processes must operate within defined upper and lower limits to meet industry quality standards.
- Centering and spread: The process should be well-centered and have minimal variability to consistently meet the required specifications.
- Capability indices: Metrics like Cp and Cpk are used to measure how well a process output can meet specifications —Cp focuses on the process’s potential capability, while Cpk accounts for both spread and centering.
- Root cause identification: This analysis helps uncover the causes of defects or deviations, enabling targeted improvements and preventive actions.
- Application across industries: While rooted in manufacturing, these principles are applicable in various fields, from healthcare to software development.
Cp and Cpk – How to measure and interpret them
Given the circumstances, two specific measures ought to be performed with relevant process data collected beforehand such as standard deviation and variability or spread:
- Cp (Process Capability): It accounts only for the spread or variation of the process, and is obtained through the next formula: Specification Width / Process Width.
- Cpk (Process Capability Index): It accounts for both the spread and centering of the process, and is obtained through the next formula: Distance from Mean to Nearest Spec Limit / Distance from Mean to Process Edge.
In general terms, when Cpk is higher it’s better. When Cpk is less than 1.0, it’s considered a poor result, and the analyzed process is tagged as not capable; a result between 1.0 and 1.33 is considered “barely capable”. In other words, a high Cpk value means that the organization and its process are expected to produce fewer defective parts, ultimately delivering better performance and fewer warranty complaints.
Seeking process stability overall is an inherent part of this objective. It’s considered as such when, on the different analysis runs, behavior remains equal in short and long terms, and average and standard deviation are constant over time.
Understanding Cp and Cpk
Cp and Cpk are closely related, but they don’t always have to be used together—they address distinct aspects of process capability in quality management.
- Cp measures the potential capability of a process by comparing the process spread (variability) to the specification limits, assuming the process is perfectly centered. It answers the question: Can this process potentially fit within the specified limits? However, it doesn’t account for whether the process is actually centered.
- Cpk goes a step further by evaluating both the process spread and how well the process is centered within the specification limits. It answers the question: How well is the process performing in reality, considering any shift or deviation from the center?
The difference between Cp and Cpk vs. Pp and Ppk
The Process Capability Analysis uses data to predict the outcome of the part production process —however, it must be distinguished from a Process Performance Analysis (Pp) and Process Performance Analysis Index (Ppk). These are employed to evaluate the manufacturing process and overall give a response to how the process is performed over a time interval. In other words, a historical analysis rather than a predictive one.
The valuable insight here is that, if the process is stable enough Cpk and Ppk will be uniform, indicating that the actual performance matched the predicted potential performance; but if its unstable Cpk will be greater than Ppk, implying shifts or drifts over time.
Additional recommendations when data measuring in manufacturing
Keep the next recommendations in mind when collecting data to perform this analysis:
- Gages should be calibrated and resolution should be at 10% of the specification.
- Production order is critical for part measurement and recording; if they are not recorded that way, trends can be missed.
- Measure data equally for parts that pass and for parts that fail.
- Check that all measurements are traceable, according to the 5Ms-1E Principle (Man, Machine, Method, Material Measurement System, and Environmental Conditions).
- Do not mix measurements made with two different types of equipment into single data-sets; everything must be homogeneous.
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