Why & How Cpm came into existence? Weren’t Cp & Cpk enough to trouble us?

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In the earlier post (see earlier post “what is Taguchi Loss function?”) we end up the discussion stating that Cp need to be penalized for the deviation of the process mean from the specification mean.

If you are producing goods near to LSL or USL hence, the chances of rejection increases which in turn increases the chances of reprocessing and rework thereby increasing the cost. Even if you manage to pass the quality on borderline then your customer has to adjust his process accordingly to accommodate your product thereby, increasing his set-up time and cost involved in readjusting his process. Moreover, the variance from your product and the variance from the customer’s process just get adds up to given final product with more variance (remember! Variance has an additive property).

It’s fine that we need to produce goods and services at the center of the specification, which means that we should know the position of process mean with respect to the center of the customer’s specifications. Hence another index was created called as Cpm was introduced which compensates for the deviation of process mean from the specification mean.

For calculating Cpm, the Cp formula is modified where the total variance of the system becomes

Picture24

Where μ = process mean & T = specification mean or target specification

Hence, Cp formula

Picture25

 

is modified to

Picture26

 

This is necessary because if I can keep the process mean and the specification mean near to each other, the chances of touching the specification limits would be less which in turn would reduce the chances of reprocessing and we can control the process in a better way.

If μ = T, then Cpm = Cpk = Cp

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What’s the big deal, let’s rebuild the garage to fit the bigger car!

How the garage/car example and the six-sigma (6σ) process are related?

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What do we mean by garage’s width = 12σ and car’s width = 6σ?

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What Taguchi Loss Function has to do with Cpm?

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The traditional way of quality control can be called as “GOAL-POST” approach where, the possible out-come is goal or no-goal. Similarly, QA used to focus only on the end product’s quality with two possible outcomes, pass or fail.

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Later on Taguchi gave the concept of producing products with quality targeted at the center of the customer’s specifications. He stated that as we move away from the center of the specification, we incur cost either at the producer’s end or at the consumer’s end in the form of re-work and re-processing. Holistically, it’s a loss to the society.

Picture23

For example;

Buying a readymade suit, it is very difficult to find a suit that perfectly matches your body’s contour, hence you end up going for alterations. This incurs cost. Whereas, if you get a suit stitched by a tailor that fits your body contour (specification), it would not incur any extra cost in rework.

Let’s revise what we learned in “car parking” example (see links below). The Cp only focuses on how far the process control limits (UCL & LCL) are from the customer’s specification limits (USL & LSL) …. it doesn’t take into the account the deviation of process mean from the specification mean. Hence, we  require another index which can penalize the Cp for the above deviation and this new index is called as Cpm.

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How the garage/car example and the six-sigma (6σ) process are related?

Now Let’s start talking about 6sigma

What do we mean by garage’s width = 12σ and car’s width = 6σ?

Kindly provide feedback for our continuous journey

Six Sigma Video lectures from IIT Karagpur by Prof. Bagachi

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This is a collection of 40 excellent video lectures from Prof. Bagachi which would be helpful in understanding the concepts.

6sigma-Video Lectures by Prof. Bagachi

But How Six-Sigma Tools Compresses the Variation?

picture56

In order to understand this, let’s take the following equation

Picture19
Now if I ask you the value of ‘y’ for x1 = 3 and x2 = 7. The value of ‘y’ would be 38.

Point to be noted here is that “you were in a position to calculate the value of ‘y’ because you have a mathematical equation describing the relationship between ‘y’ and x1 & x2.

Similarly in six-sigma we find out the variables (x) that impact the response (y) and then we find a quantitative relationship between them. In six-sigma language we describe it as “y is a function of x1, x2,….”

Picture20

For example

Time taken to reach office (y) is a function of following variables

  1. When he slept last night?                                      (x1)
  2. Did he had drinks last night?                                (x2)
  3. When he woke-up?                                                (x2)
  4. When he started from the home?                         (x4)
  5. How was the traffic in the morning?                    (x5)
  6. How fast he was driving?                                      (x6)
  7. Which route he took?                                             (x7)

Let’s assume for the time being that x2, x4, x5 and x7 were found to be important during investigation using six-sigma[1] and the relationship between the time taken to reach office and all of the 4 factors can be described arbitrary for the time being as

Picture21

Using the above equation, the response (time taken to reach office) could be optimized.

[1] This investigation is usually done using a famous methodology called as DIMAC. I hope everyone is acquainted with it. Followed by ANOVA and regression to get the equation.

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What do we mean by garage’s width = 12σ and car’s width = 6σ?

 

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Right now we are not in a position of going into the details of the standard normal distribution hence, for the time being let’s assume that my manufacturing process is stabilized, which is represented by a symmetrical curve shown below

Picture16

The main characteristic of this curve is that the 99.7% of the product would be between LCL & UCL or within ±3σ distance from the mean (μ). Only 0.3% or 3000ppm products would be beyond ±3σ or defective products. So width of the car is equivalent to the width of the process = UCL-LCL = voice of the process = VOP = 6σ = ±3σ.

Second point is that the curves never touches the x-axis à means that there will always be some probability of failure even if you move to infinity from the mean (probability can be negligible but will be there).

Now let’s overlap the above process curve with the customer’s specifications (=12σ = ±6σ) or the garage’s specifications.

Picture17

We can see that there is a safety margin of 3σ on both side of the process control limits (LCL & UCL). In layman words, in order to produce a defective product, my process has to deviate by another 3σ, which has very remote possibility. Statistically ± (position of LSL & USL) from the mean would account for only ~3.4 ppm failure (don’t bother about the calculation right now, just understand the concept). For this has to happen, someone has to disturb the process deliberately. Compare this failure of 3.4 ppm at ±6σ level with 3000ppm at ±3σ level!

Even if the mean of the process deviate by ±1.5σ, there is enough margin of safety and it will not impact the quality and in regular production, this deviation of ±1.5σ is quite common.

Picture18

Car Parking & Six-Sigma

What’s the big deal, let’s rebuild the garage to fit the bigger car!

How the garage/car example and the six-sigma (6σ) process are related?

Now Let’s start talking about 6sigma

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Now it’s important to understand the concept of sigma or the standard deviation

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We have seen that we need to restrict the width of the car for a given width of the garage. This is analogous to the with of the process (voice of customer, VOP) Vs the width of the customer’s specification (voice of the customer or VOC).
The width of the process is measured in terms of standard deviation denoted by σ (sigma).

The target of the 6sigma methodology is to reduce this variance (width of the car) to such an extent that even by mistake it should not cross the customer’s specification (or should not hit the wall of the garage).

Before we work towards reducing the σ, we should know about this monster very well as we will be encountering him at every step during the 6sigma journey.

There are two very important characteristics of any data set

Location and the spread of the data set.

Location represents the point in the data set where there is maximum clustering of the data –> Mean and median.

Spread represents the variability in the data set, there will be some observations that will be above the mean and there will be some that will be below the mean. Standard deviation σ measures the average spread of the data from the mean in either direction of the mean.

Office arrival time for last 5 days with average time are given below, deviation of each observation from the mean is also captured.

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Let’s calculate the average deviation

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Note that sum of all positive deviations = sum of all negative deviations which indicates that the mean divided the data in two equal halves.

Sum of all deviation itself becomes zero, hence we need some other way to calculate this average deviation about the mean.

 In order to circumvent the issue, a very simple idea was used

Square of negative number → positive number → square root of this number → ±parent number

Hence square of all the deviations are calculated and summed-up to give sum of squares (simply SS) [1]. This SS is then divided by total number of observations to give average variance around the mean.[2] The square root of this variance gives standard deviation s, the most common measure of variability.

Picture4What it typically means that “on an average data is 7.42 units (= 1 standard deviation ±1σ) in either direction of the mean in the given data set. Mean of the data set is at ZERO standard deviation.

If process a stabilized and normally distributed then following holds true

 Picture16

i.e. 99.7 % of the observation in the data set would be between ±3σ.

Now we can understand whey we have taken 12σ as the width of the garage and 6σ as the width of the car!

The concept of ‘σ’ is the most important concept in understanding 6sigma. If we can understand it, downstream we wouldn’t be having any problem in understanding other topics. At this moment one important point to be noted here is that the calculation of σ depend on the type of data or data distribution we are handling.

Calculation of mean and σ would be different depending on whether we are dealing with normal distribution, binomial distribution, Poisson distribution etc. The importance of this would be realized when we would be studying the various types of control charts. At that time we just have to remember that “we must calculate mean and σ according to the distribution”.


[1] Popularly known as sum of squares, this most widely term used in ANOVA and Regression analysis

[2] SS divided by its degree of freedom → mean sum of squares or MSE, these concepts would appear in ANOVA & Regression analysis.

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How the garage/car example and the six-sigma (6σ) process are related?

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Let the width of the garage (D) and that of the car (d) is measured in some units called as sigma or σ, further

Width of the garage is sacrosanct = 12σ (assume for the time being), then following three cases can occur depending on the ratio of D/d = Cp.

Picture3

The process sigma level is when the Cp =2, this is represented by case ‘C’ given below. Point to be noted is that there is a margin of safety (=3σ) on both side of the car before car touches the garage. The ideal width of car is taken as 6σ (don’t ask why, right now!)

 Picture4Picture5

Process capability Cpk: is measured in the terms of the σ distance between the center of the of the car (C1) and the wall of the garage. Cpk tell us “how far is the car from left wall or the right wall of the garage (or customer’s specifications).

Hence, the are two Cpk vales,

     Picture14

Cpk is given by

Picture15

Car Parking & Six-Sigma

What’s the big deal, let’s rebuild the garage to fit the bigger car!

What do we mean by garage’s width = 12σ and car’s width = 6σ?

Now Let’s start talking about 6sigma

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What’s the big deal, let’s rebuild the garage to fit the bigger car!

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Picture2

You may argue that what’s the big deal if my existing car doesn’t fit into the garage, I will rebuild the garage that can fit my bigger car.

You can always do it with garage but not with customer’s specifications as you can’t dictate it or simply customer won’t change his processes to fit your defective products. The only way is to improve your existing process or else lose your business to your competitors.

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Car Parking & Six-Sigma

How the garage/car example and the six-sigma (6σ) process are related?

What do we mean by garage’s width = 12σ and car’s width = 6σ?

Now Let’s start talking about 6sigma

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