## Process Capability

This is a long article, but I thought it was crucial to keep Cp and Cpk together. Cpk is addressed first, then Cp. There are besides crib notes on what the equations mean in a real performance common sense, what you should be able to tell about a process depending on Cp and Cpk values and more. If you are not finding what you are looking for, please let me know in the notes below .

## Before We Begin!

This article was written to help Six Sigma Green Belt and Black Belt candidates prepare for and pass their exams. If that ’ mho you, leave me a gossip below or contact me and let me know which administration and belt out you ’ rhenium studying for. This will help me make the article even better for you.

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If you ’ re studying for something else, please leave a remark or contact me and let me know what you are studying for. Again, this will help me make the article better for you and everyone else. Thanks, Ted .

## What is the Difference between Cp, Cpk and Pp, PPk?

Cp Cpk vs Pp Ppk Cp and Cpk are called Process Capability. Pp and Ppk are called Process Performance. In both cases we want to try to verify if the process can meet to meet Customer CTQs ( requirements ). Cp, and Cpk are used for Process Capability. Generally you use this when a process is under statistical control. This frequently happens with a suppurate process that has been around for a while. Process capability uses the process sigma value determined from either the Moving Range, Range or Sigma operate charts Pp and PPk are used for Process Performance. Generally you use this when a process is besides newly to determine if it is under statistical dominance. Ex. there is a short pre-production run or you are piloting a new procedure. Because there is not a lot of historical data we take large samples from the process to account for magnetic declination. process Performance broadly uses sample sigma in its calculation. In hypothesis Cpk will constantly be greater than or equal to Ppk. There are anomalies seen when the sample size is belittled and the data represents a short sum of meter where estimating using R will overstate standard deviation and make Cpk smaller than Ppk. It is not real, there can never be less variation in the long term since the long term is using all of the data not precisely two pieces of data from every subgroup. Evaluating process capability with Cp & Cpk mirror what is done ( and why it is done ) when following the Pp & Ppk approach. The main difference is that you use Cp & Cpk after a process has reached constancy or statistical control .

## Cpk vs Ppk

Ppk tells us how a process has performed in the past and you can not use it predict the future because the march is not in a state of control .

### If a process is in statistical control;

The values for Cpk and Ppk will converge to about the lapp value because sigma and the sample distribution standard deviation will be identical ( use an F examination to determine ). In early words, if Cpk == Ppk, the process is probably in statistical control .

### If a process is NOT in statistical control;

Cpk and Ppk values will be distinctly unlike, possibly by a very wide margin .

## What is the Difference Between Cp and Cpk?

### Cp vs Cpk

Cp and Cpk measure how consistent you are to around your average performance. The ‘ thousand ’ stands for ‘ centralizing factor. ’ The index takes into consideration the fact that your data is possibly not centered. Cpk tells us what a process is adequate to of doing in future, assuming it remains in a state of statistical control .

### The Shooting at a Target Analogy

In a absolutely centered data stage set, there will be no dispute between Cp and Cpk. Think of throwing darts at a flit board and having the center of the bull ’ sulfur eye be the 0,0 on a cartesian plane and the edges being out 3 units from that center point ( we will use the edge of the dart board or 3 and -3 as our USL and LSL ). In a perfectly centered sample of darts, your average distance from the center, or Mu, will be 0. A little algebra will show us that that your Cpk and Cp numbers come out the same. Min ( ( 0- -3 ) /3s, ( 3-0 ) /3s ) = ( 3- -3 ) /6s = 1s. Things get a little harasser when the darts move up, say to be centered at an average of 2 units above center. now you end up with a Cpk of ( 3-2 ) /3s = 1/3s, but your Cp is still the like 1s as earlier. It is authoritative to note that because Cpk uses the minimum function, it will always be equal to or smaller than the Cp for the lapp set of data .

## What is Cpk?

### The Parking a Car in the Garage Analogy

think of the walls of your garage – where you have to fit your cable car in – they become the customer specification limits. If you go past those limits, you will crash, and the customer will not be glad ! When your process has a distribute of variation that means the action average is all over the seat. not good for parking a cable car, and not dear for any other action. To give your park process the best gamble of success you should work on reducing mutant and focus on. If the car is excessively broad for the garage, nothing you do to center the process will help. You have to change the distribution of the process ( make the car smaller. ) If the cable car is a lot smaller than the garage, it doesn ’ t matter if you park it precisely in the center ; it will fit and you have plenty of board on either side. That ’ randomness one of the reasons the six sigma philosophy focuses on removing pas seul in a process. If you have a process that is in manipulate and with short variation, you should be able to park the car easily within the garage and therefore meet customer requirements. Cpk tells you the kinship between the size of the car, the size of the garage and how far away from the middle of the garage you parked the cable car. ”

## How to Calculate Cpk

Cpk is a measure to show how many standard deviations the specification limits are from the plaza of the process. On some processes you can do this visually. Others require an equality. To find Cpk you need to calculate a Z score for the upper specification limit ( called Z USL ) and a Z score for the lower specification limit ( called Z LSL ). Since we are trying to measure how many standard deviations fit between the center line and the specification terminus ad quem you should not be surprised that the prize of those limits, the serve mean, and the standard deviation are all components of the Z calculation. Cp is an abbreviation. There are very two parts ; the upper and the lower denote Cpu and Cpl respectively. Their equations are : Cpl = ( Process Mean – LSL ) / ( 3*Standard Deviation )

Cpu = ( USL – Process Mean ) / ( 3*Standard Deviation ) Cpk is merely the smallest value of the Cpl or Cpu denoted : Cpk= Min ( Cpl, Cpu )

### Why are we dividing by 3 to find Cpk?

We know that any specification limit has an upper bound and a lower tie down. Because you know that 6 sigma – or 6 standard deviations account for closely all eventualities on a process ( assuming normal distribution ) you shouldn ’ t be surprised to see the “ / 3 ” because we are looking at only one side of the distribution .

### Calculating Cpk using a Z Value

If you have a Z prize, the equation is very easy ; Cpk can be determined by dividing the Z score by three. A z score is the like as a standard score ; the number of standard deviations above the beggarly. Z = x – average of the population / standard deviation .

## Notes and Characteristics of Cpk

### Cpk and Centered Processes

If a work is perfectly centered, it has a Cp of 1. That would indicate that bastardly was 3 standard deviations away from the upper limit and the lower limit. A perfectly centered process – a process who has a average precisely in between the 2 specification limits ( meaning halfway between the two will have a Cpk of 1. How is this potential ? Let ’ s check the mathematics. If a process is absolutely centered, then we know that the ( USL – Process mean ) equals the like thing as the ( Process Mean – LSL ). Let ’ s call that A. Z USL = USL – Process Mean / Standard Deviation. then becomes Z USL = A/ Standard Deviation Z LSL = Process Mean – LSL / Standard Deviation then becomes Z LSL = A / Standard Deviation. The claim lapp thing.

## Notes on Cpk

- Cpk measures how close a process is performing compared to its specification limits and accounting for the natural variability of the process.
- Larger is better. The larger Cpk is, the less likely it is that any item will be outside the specification limits.
- When Cpk is negative it means that a process will produce output that is outside the customer specification limits.
- When the mean of the process is outside the customer specification limits the value of Cpk will be Negative
- We generally want a Cpk of at least 1.33 [4 sigma] or higher to satisfy most customers.
- Cpk can have an upper and lower value reported.
- If the upper value is 2 and the lower is 1, we say it has been shifted to the left.
- This tells us nothing about if the process is stable or not.
- We must report the lower of the 2 values.

That was poorly centered!

### What are Good Values for Cpk?

Remember the Car parking in the garage analogy ? Cpk = Negative number : Your work will regularly crash the car into the wall. Cpk =0.5 : You have a estimable luck hitting the wall on entry. Cpk =1 : Your car may be barely touching the nearest edge of the entrance. Cpk =2 : bang-up ! You have great clearance. You could double the width of your car before you hit the side of the garage. Cpk =3 : excellent ! You have excellent clearance. You could triple the width of your car before you hit the side of the garage .

## How to Calculate Cp

good as you use Cp & Cpk when a serve is stable and Pp & Ppk when a action is new, the way you calculate each are a bite different, besides. Let ’ s revisit Pp Pp = ( USL – LSL ) / 6* randomness In Pp, sulfur is the criterion diversion, or the ‘ fatness ’ or dispersion of the bell bend. In Cp, we replace sulfur with and estimate of σ we call σr. To do that we leverage the Moving Range concept from a Moving R Bar chart or an XMR Chart. so, σr = [ R Bar / d2 ] R Bar comes from the Moving stove. D2 reflects values derived from integrating the area under the normal curve. We often use a postpone which gives a d2 value based on how many subgroups were in the sample .d2 subgroup values Cp does not account for concentrate. Cp = ( USL – LSL ) / ( 6* σr ) Cp = ( USL – LSL ) / ( 6* R Bar / d2 )

#### Cp for Process Mean close to USL

If your serve Mean ( central tendency ) is closer to the USL, use : [ USL – x ( prevention ) ] / [ 3 * R Bar / d2 ], where ten ( measure ) is the serve Mean .

#### Cp for Process Mean close to LSL

If your serve Mean ( central tendency ) is closer to the LSL, manipulation : [ ten ( legal profession ) – LSL ] / [ 3 * R Bar / d2 ], where ten ( measure ) is the process Mean .

### Capability Index

How do Cp, Z values, DPMO, Specification Limits, Standard Deviation, and Capability all relate ? besides see Z values and process capability .Capability Index

### Notes on Cp Values

- If the ratio is greater than one, then the Engineering Tolerance is greater than the Process Spread so the process has the “potential” to be capable (depending on process centering).
- If, however, the Process Spread is greater than the Engineering tolerance, then the process variation will not “fit” within the tolerance and the process will not be capable (even if the process is centered appropriately).

## Capability Ratio Cr

The capability ration is the inverse of Cp Cr = 1/ Cp = ( 6* σr ) / ( USL – LSL ) If Cr < 0.75, the process is able. If Cr = 0.75 – 1.00, the process is capable with tight control. If Cr > 1, the process is not capable .

## Notes on Relating Cp And Cpk

- If Cp == Cpk, then the process is perfectly centered. If perfectly centered, Cp == Cpk.
- Because Cpk accounts for centering (where Cp does not), Cpk can never be larger than Cp.
- Both assume a stable process.

## Process Capability Videos

### Cpk Videos

Great, clear, concise television on this subject. “ If you were producing a Cpk equal to 1, than you could expect to produce **at least** 99.73 % good parts. ”

### Lecture on Process Capability and SPC

## ASQ Six Sigma Black Belt Certification Process Capability Questions:

**Question: ** Data being used in the initial set-up of a serve are assumed to have a normal distribution. If the nominative ( prey ) is set at the center of the distribution, and the specification limits are set at ±3s from the center, then the Cpk is equal to : ( A ) –0.25

( B ) 1.00

( C ) 1.33

( D ) 1.67 **Answer:**

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## ASQ Six Sigma Green Belt Certification Process Capability Questions:

**Question: ** When calculating the Cp exponent, what does the standard deviation present in the formula Cp = ( USL – LSL ) / 6σ ? ( A ) The tolerance interval

( B ) The confidence interval for the consequence

( C ) The range of the process

( D ) The variation of the index **Answer:**

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