Process Capability (Cp) is a statistical measurement of a process’s ability to produce parts within specified limits on a consistent basis. To determine how our process is operating, we can calculate Cp (Process Capability), Cpk (Process Capability Index), or Pp (Preliminary Process Capability) and Ppk (Preliminary Process Capability Index), depending on the state of the process and the method of determining the standard deviation or sigma value.
The Cp and Cpk calculations use sample deviation or deviation mean within rational subgroups. The Pp and Ppk calculations use standard deviation based on studied data (whole population). The Cp and Cpk indices are used to evaluate existing, established processes in statistical control. The Pp and Ppk indices are used to evaluate a new process or one that is not in statistical control.
Process capability indices Cp and Cpk evaluate the output of a process in comparison to the specification limits determined by the target value and the tolerance range. Cp tells you if your process is capable of making parts within specifications and Cpk tells you if your process is centered between the specification limits. When engineers are designing parts, they must consider the capability of the machine or process selected to produce the part.
To illustrate, let us use a real world example. Imagine that you are driving your vehicle over a bridge. The width of your vehicle is equivalent to the spread or range of the data. The guardrails on each side of the bridge are your specification limits. You must keep your vehicle on the bridge to reach the other side. The Cp value is equivalent to the distance your vehicle stays away from the guardrails and Cpk represents how well you are driving down the middle of the bridge. Obviously if the spread of your data is narrower (your car width is smaller), the more distance there is between the vehicle and the guardrails and the more likely you are to stay on the bridge.
The Cp index is a fundamental indication of process capability. The Cp value is calculated using the specification limits and the standard deviation of the process. Most companies require that the process Cp = 1.33 or greater.
The Cpk index of process center goes a step further by examining how close a process is performing to the specification limits considering the common process variation. The larger the Cpk value the closer the mean of the data is to the target value. Cpk is calculated using the specification limits, standard deviation or sigma, and the mean value. The Cpk value should be between 1 and 3. If the value is lower than 1 the process is in need of improvement.
The Cp and Cpk indices are only as good as the data used. Accurate process capability studies are dependent upon three basic assumptions regarding the data:
- There are no special causes of variation in the process and it is in a state of statistical control. Any special causes must be discovered and resolved.
- The data fits a Normal distribution, exhibiting a bell shaped curve and can be calculated to plus or minus three sigma. There are cases when the data does not fit a normal distribution.
- The sample data is representative of the population. The data should be randomly collected from a large production run. Many companies require at least 25 to preferably 50 sample measurements be collected.
In manufacturing and many other types of businesses, reduction of waste and providing a quality product are imperative if they are to survive and thrive in today’s marketplace. Waste exists in many forms in a process. When we look at the bigger picture, process capability is more than just measuring Cp and Cpk values. Process capability is just one tool in the Statistical Process Control (SPC) toolbox. Implementing SPC involves collecting and analyzing data to understand the statistical performance of the process and identifying the causes of variation within. Important knowledge is obtained through focusing on the capability of process. Monitoring process capability allows the manufacturing process performance to be evaluated and adjusted as needed to assure products meet the design or customer’s requirements. When used effectively this information can reduce scrap, improve product quality and consistency and lower the cost to manufacture and the cost of poor quality.
A process has been defined as a sequence of interdependent procedures, operations or steps that consume resources and convert the inputs into outputs. Each operation or step adds to the next to achieve a goal or desired result. In every process, there exists a certain amount of variation. Variation in a process cannot be eliminated, but it can be measured, monitored, reduced and controlled. If we look at a simple example of making a cup of coffee, we can identify the inputs, steps, equipment and output of the process. Some of the inputs are coffee and water.
The steps include turning on the coffee maker, measuring and adding the coffee and water and the output is a pot or cup of coffee. The variation can occur in the amount of coffee or water introduced in the process and the performance of the coffee maker itself. Not every cup of coffee is exactly the same but in most cases, if the measurements are controlled and reasonably consistent, it tastes the same.
By utilizing process controls, taking measurements and using reliable, well-maintained equipment, variation in a process can have less effect on the quality of the output. The process can be capable of producing acceptable product on a consistent basis. We can maintain Process Capability.
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