Concept of Quality — We Must Understand this before Learning 6sigma!

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Before we try to understand the 6sigma concept, we need to define the term “quality”.

 What is Quality?

The term “quality” has many interpretations, but this by the ISO definition, quality is defined as: “The totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs”.

If we read between the lines, then the definition varies with the reference frame we use to define the “quality”. The reference frame that we are using here are the manufacturers (who is supplying the product) and the customer (who is using the product). Hence the definition of quality with respect to above two reference frame can be defined as

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This “goal post” approach to quality is graphically presented below, where a product is deemed pass or fail. It didn’t matter even if the quality is on the borderline (football just missed the goalpost and luckily a goal was scored).

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This definition was applicable till the time there was a monopoly for the manufacturers or having a limited competition in the market. The manufacturers were not worried about the failures as they can easily pass on the cost to the customer. Having no choice, customer has to bear the cost. This is because of the traditional definition of profit shown below.

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Coming to current business scenario, the manufacturers doesn’t have luxury to define the selling price, now the market is very competitive and the price of goods and services are dictated by the market, hence it is called as market price instead of selling price. This lead to the change in the perception of quality, now quality was defined as producing goods and services meeting customer’s specification at the right price. The manufacturers are now forced to sell their goods and services at the market rate. As a result the profit is now defined as the difference of market rate and cost of goods sold (COGS).

In current scenario if a manufacturer wants to make a profit, the only option he has is to reduce COGS. In order to do so, one has to understand the components that makes up COGS. The COGS in has many components as shown below. The COGS consist of genuine cost of COGS and the cost of quality. The genuine COGS will always be same (nearly) for all manufacturers, but the real differentiator would be the cost of quality. The manufacturer with lowest cost of quality would enjoy highest profit and can influence the market price to keep the competition at bay. But in order to keep cost of quality at its lowest possible level, the manufacturer has to hit the football, right at the center of the goalpost every time!

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The cost of quality involves the cost incurred to monitor and ensure the quality (cost of conformance) and the cost of non-conformance or cost of poor quality (COPQ). The cost of conformance is a necessary evil whereas the COPQ is a waste or opportunity lost.

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Coming to the present scenario, with increasing demand of goods and services, manufacturers required to fulfill their delivery commitment on time otherwise their customers would lose market share to the competitors. The manufacturers has realized that their business depends on the business prospects of their customers hence, timely supply of products and services is very important. This can be understood in a much better way using pharmaceutical industry

Sole responsibility of any Regulator (say FDA) towards its country is to ensure not only the acceptable (quality, safety and efficacy) and affordable medicines but they also need to ensure its availability (no shortage) in their country all the time. Even that is not enough for them; those medicines must be easily accessible to patients at their local pharmacies. These may be called as 4A’s and are the KRA of any Regulatory body. If they miss any one of the above ‘4As’, they will be held accountable by their Government for endangering the life of the patients. The point that need to be emphasized here is the importance of TIMELY SUPPLY of the medicines besides other parameters like quality and price.

 Hence, the definition of quality again got modified as “producing goods and services in desired quantity which is delivered on time meeting all customer’s specification of quality and price.” A term used in operational excellence called as OTIF is acronym for “on time in full” meaning delivering goods and services meeting customer’s specification on time and in full quantity.

Coming once again to the definition of profit in present day scenario

Profit=MP-COGS

We have seen that the selling price is driven by the market and hence manufacturer can’t control it beyond an extent. So what he can do to increase his margin or profit? The only option he has is to reduce his COGS. We have seen that COGS has two components, genuine GOGS and COPQ. The manufacturers have little scope to reduce the genuine COGS as it is a necessary evil to produce goods and services. We will see latter in LEAN manufacturing how this genuine COGS can be reduced to some extent (wait till then!) e.g. if we can increase the throughput, we can bring down genuine COGS (if throughput or the yield of the process is improved, which results in less scrap would decrease the RM cost per unit of the goods produced).

But the real culprit for the high COGS is the unwarranted high COPQ.


The main reasons for high COPQ are

  1. Low throughput or yield
  2. More out of specifications (OOS) products which required to be either
    1. Reprocessed
    2. Reworked or
    3. Has to be scraped
  3. Inconsistent quality leading to more after sales& service and warranty costs
  4. Biggest of all loses would be the customer’s confidence in you, which is intangible.

If we look at the outcomes of COPQ (discussed above), we can conclude one thing and that is “the process is not robust enough to meet customer’s specifications” and because of this manufacturers faces the problem of COPQ. All these wastages are called as “mudas” in Lean terminology hence, would be dealt in detail latter. But the important

What causes COPQ?

Before we can answer this important question, we need to understand the concept of variance. Let’s take a simple example, say you start from the home for office on exactly the same time every day, do you reach the office daily on exactly same time? Answer will be a big no or a better answer would be, it will take anywhere between 40-45 minutes to react the office if I start exactly at 7:30 AM. This variation in office arrival time can be attributed to many reasons like variation in starting time itself (I just can start exactly at 7:30 every day), variation in traffic conditions etc. There will always be a variation in any process and we need to control that variation. Even in the manufacturing atmosphere there are sources of variation like wear and tear of machine, change of operators etc. Because of this variation, there will always be a variation in the output (goods and services produced by the process). Hence, we will not get a product with a fixed quality attributes, but that quality attribute will have a range (called as process control limits) which need to be compared with the customer’s specification limits (goal post).

If my process control limits are towards the goal post (boundaries of the customer’s specification limits) represented by the goal post, then my failure rate would be quite high resulting in more failures, scrap, rework, warranty cost. This is nothing but COPQ.

Alternatively if my aim (process limits) are well within the goal posts (case-2), my success rate are much higher and I would be have less, scrap and rework thereby decreasing my COPQ.

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Taguchi Loss Function

A paradigm shift in the definition of quality was given by Taguchi, where he gave the concept of producing products with quality targeted at the center of the customer’s specifications (a mutually agreed target). 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. It states that even producing goods and services beyond customer’s specification is a loss to the society as customer will not be willing to pay for it. There is a sharp increase in the COGS as we try to improve the quality of goods and services beyond the specification.

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For example;

The purity of medicine I am producing is > 99.5 (say specification) and if I try to improve it to 99.8, it will decrease my throughput as we need to perform one extra purification that will result in yield loss and increased COGS.

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.

Six Sigma and COPQ

It is apparent from the above discussion that “variability in the process” is the single most culprit for the failures resulting in high cost of goods produced. This variability is the single most important concept in six sigma that required to be comprehended very well. We will encounter this monster (variability) everywhere when we will be dealing with six sigma tools like histogram, normal distribution, sampling distribution of mean, ANOVA, DoE, Regression analysis and most importantly the statistical process control (SPC).

Hence, a tool was required by the industry to study the variability and to find the ways to reduce it. The six sigma methodology was developed to fulfill this requirement. We will look into the detail why it is called as six sigma and not five or seven sigma latter on.

Before we go any further, we must understand one very important thing and must always remember this “any goods and services produced is an outcome of a process” also “there are many input that goes into the process, like raw materials, technical procedures, men etc”.

Hence, any variation in the input (x) to a given process will cause a variation in the output (y) quality.

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Another important aspect is that the variance has an additive property i.e. the variance from all input is added to give the variance in the output.

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How Six Sigma works?

Six sigma works by decreasing the variation coming from the different sources to reduce the overall variance in the system as shown below. It is a continuous improvement journey.

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Summary:
  1. Definition of Quality has changed drastically over the time, it’s no more “fit for purpose” but also include on time and in full (OTIF).
  2. In this world of globalization, market place determines the selling price and manufacturers either have to reduce their COPQ or perish.
  3. There is a customer specification and a process capability. The aim is to bring the process capability well within the customer’s specifications.
  4. Main culprit of out of specification product is the unstable process which in turn is because of variability in the process coming from different sources.
  5. Variance has an additive property.
  6. Lean is tool to eliminate the wastages in the system and six sigma is a tool to reduce the defects from the process.

References

  1.  In order to understand the consequences of a bad process, see red bead experiment designed by Deming on Youtube  https://www.youtube.com/watch?v=JeWTD-0BRS4
  2. For different definition of quality see http://www.qualitydigest.com/magazine/2001/nov/article/definition-quality.html#

 

7QC Tools — The Control Charts

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The Control Charts

This is the most important topic to be covered in the 7QC tools. But in order to understand it, just remember following point for the moment as right now we can’t go into the details

  1. Two things that we must understand beyond doubt are
    1. There is a customer’s specifications, LSL & USL (upper and lower specification limits)
    2. Similarly there is a process capability, LCL & UCL (upper and lower control limits)
    3. The Process capability and customer’s specifications are two independent things however, it is desired that UCL-LCL < USL-LSL. The only way we can achieve this relationship is by decreasing the variation in the process as we can’t do anything about the customer’s specifications (they are sacrosanct).
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  2. If a process is stable, will follow the bell shaped curve called as normal curve. It means that, if we plot all historical data obtained from a stable process – it will give a symmetrical curve as shown below. The σ represents the standard deviation (a measurement of variation)
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  3. The main characteristic of the above curve is shown below. Example, the area under ±2σ would contain 95% of the total data
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  4. Any process is affected by two types of input variables or factors. Input variables which can be controlled are called as assignable or special causes (e.g., person, material, unit operation, and machine), and factors which are uncontrollable are called noise factors or common causes (e.g., fluctuation in environmental factors such as temperature and humidity during the year).
  5. From the point number 2, we can conclude that, as long as the data is within ±3σ, the process is considered stable and whatever variation is there it is because of the common causes of variation. Any data point beyond ±3σ would represent an outlier indicating that the given process has deviated or there is an assignable or a special cause of variation which, needs immediate attention.
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  6. Measurement of mean (μ) and σ used for calculating control limits, depends on the type and the distribution of the data used for preparing control chart.

Having gone through the above points, let’s go back to the point number 2. In this graph, the entire data is plotted after all the data has been collected. But, these data were collected over a time! Now if we add a time-axis in this graph and try to plot all data with respect to time, then it would give a run-chart as shown below.

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The run-chart thus obtained is known as the control chart. It represents the data with respect to the time and ±3σ represents the upper and lower control limits of the process. We can also plot the customer’s specification limits (USL & LSL) if desired onto this graph. Now we can apply point number 3 and 4 in order to interpret the control chart or we can use Western Electric Rules if we want to interpret it in more detail.

The Control Charts and the Continuous Improvement

A given process can only be improved, if there are some tools available for timely detection of an abnormality due to any assignable causes. This timely and online signal of an abnormality (or an outlier) in the process could be achieved by plotting the process data points on an appropriate statistical control chart. But, these control charts can only tell that there is a problem in the process but cannot tell anything about its cause. Investigation and identification of the assignable causes associated with the abnormal signal allows timely corrective and preventive actions which, ultimately reduces the variability in the process and gradually takes the process to the next level of the improvement. This is an iterative process resulting in continuous improvement till abnormalities are no longer observed in the process and whatever variation is there, is because of the common causes only.

It is not necessarily true that all the deviations on control charts are bad (e.g. the trend of an impurity drifting towards LCL, reduced waiting time of patients, which is good for the process). Regardless of the fact that the deviation is goodor badfor the process, the outlier points must be investigated. Reasons for good deviation then must be incorporated into the process, and reasons for bad deviation needs to be eliminated from the process. This is an iterative process till the process comes under statistical control. Gradually, it would be observed that the natural control limits become much tighter than the customer’s specification, which is the ultimate aim of any process improvement program like 6sigma.

The significance of these control charts is evident by the fact that it was discovered in the 1920s by Walter A. Shewhart, since then it has been used extensively across the manufacturing industry and became an intrinsic part of the 6σ process.

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To conclude, the statistical control charts not only help in estimating these process control limits but also raises an alert when the process goes out of control. These alerts trigger the investigation through root cause analysis leading to the process improvements which in turn leads to the decreased variability in the process leading to a statistical controlled process.


Understanding 6sigma: Example-3 — Problem at a Soap Manufacturing Plant

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Commodities products like soaps, detergents, potato chips etc. faces lot of cost pressure. Manufacturer has to ensure right quantity of the product in each pack to ensure his margins (by avoiding packing more quantity) and avoids legal issues from consumer forum (in case if less quantity is found in the pack).

Let’s take this example

A company is in the business of making soaps with a specification of 50-55 Gms/cake. Anything less than 50 Gms may invite litigation from consumer forum and anything beyond 55 Gms would hit their bottom line. They started the manufacturing and found huge variation in the mean weight of the cakes week after week (see figure-1, January-February period). They were taking one batch per week and producing 250000 soap cakes per batch. From each batch they draw a random samples of 100 soaps for weight analysis. Average weight of 100 samples drawn per batch for the month of Jan-Feb is given below.

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In order to evaluate the performance of the process, a control chart is plotted with VOP & VOC (see below). Presently it represents the case-I scenario, Figure-6 where VOP is beyond VOC.

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They started continuous improvement program to reduce the variability in the process using DMAIC process. They were able to reduce the variability to some extent but still majority of the soap cakes were out of specifications (March-April period, Figure-3). They continued their endeavor and reduced the variability further and for the first time the control limits of the process was within the specification limits (May-June period, Figure-3). At this point their failure rates were reduced as 95% of the soaps would be meeting the specifications.

Continuous Process Improvement
Continuous Process Improvement

We can further reduce the variability to reach the 6 sigma level where the failure rates would be 3.4ppm. But now, we need do a cost benefit analysis as improvement beyond a limit would involve investment. If 5% failure rate is acceptable to the management then we would stop here.

Comments:

It is not always desirable to achieve 6 sigma level, a 3 sigma process is good enough. But there are cases where human life is involved like passenger aircraft, automobile brakes and airbags, medical devices etc. and in these cases it worth going to 6 sigma and beyond to ensure the safety of the human life.

Understanding 6sigma: Example-2 — Getting Late for the Office

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Our office timings is 8:30 AM to 5:00PM. Company requires us to reach the office between 8:00 and 8:30[1] otherwise it will lead to a pay loss of 1 hour if late for two consecutive days.

Following are the arrival time of my new colleague to the office for last 35 days.

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What is “Voice of Customer” (VOC) Or the Customer’s Specifications?

Here customer is the company to whom we are providing our services and in return we are getting the salary. Now customer’s requirement is that we should be in the office between 8:00 to 8:30 AM. This is called as voice of customer or customer’s specifications. It can be represented as

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USL → Upper specification limits

LSL→Lower specification limits

Customers have right to demand anything in this world, but it is most important for us to recognize the current process capability or what we can deliver?

What is Voice of Process (VOP) or Process Control Limits?

Now we need to understand that the time my colleague took to reach the office is independent of the customer’s requirement. It is a different process by itself but it is desired that the output of this process (arrival time at office) or VOP should comply with the customer’s specifications (VOC).

Now he want to understand the efficiency of his current process (arrival time at office). Simply he wants to know what his routine or the current process can offer if he makes 100 trips to the office?

The statistical calculation shows that on 95 occasions out of 100, he would land in the office between 7:41 AM and 8:54 AM.[2] This is called as lower control limit (LCL) and upper control limit (UCL) of the process respectively. This is also known as voice of the process (VOP).

Overlap of the process efficiency (VOP) and the customer’s specification (VOC) is shown below in figure-3. It is clear from the chart below that the current process is incapable to meet the customer’s specification.

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Now what we need to do is to analyze why his process is incapable of meeting customer’s requirement.

Where is the GAP?

It is evident from the figure-3 that the control limits of his process is way beyond the customer’s specifications. In other words there is a huge gap between customer’s expectation and his current process efficiency, it’s the time that he needs to improve his process by optimizing the following variables that can influence his arrival time to the office.

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

How to Reduce the GAP?

Above mentioned variables were studied and optimized using DMAIC process,this enables him to improve his process so that on 95% of the occasions he would be landing in the office between 8:02 and 8:26 AM, hence he would be complying with the customer’s specifications as shown in figure-4. It is also evident that the control limits of the process is inside the customer’s specifications. But still on 5% of the occasions he would be outside the specification limits. Hence process needs further improvements. If the process is improved to such an extent that there is only 3.4ppm failures then it is called as six sigma process which means that if he make one million trip to office, then I will be late only on 3.4 of the occasions.

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Arrival time after continuous improvement

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Understanding 6Sigma: Example-1 — Child’s Counter Argument

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We have seen the situation from parent’s angle (see earlier blog), now consider student as the customer and parent as supplier and student (or customer) is trying to convince his father by arguing “look dad, all universities with ranking 1 to 10 are same, it is just a statistical rating that is done for attracting students, it changes every year and the universities that you are talking about are best in science, but I want to study law for which a particular university with 6th rank is the best”. As an understanding father (supplier or vendor), he finds the argument too strong to be opposed any further and agrees to his son’s specification i.e. he modifies his current process (expectation). Here Student is setting the specifications (VOC) and father is accepting it (VOP). The process of convincing the father is six-sigma which bridges the gap between them.

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Let’s be little philosophical

All of us had a dream during college days that I want to be this, I want to be that. What we did is to provide ourselves with a specification about our future life or VOC. We were also aware about our current capability (VOP) but we never took pain of performing a gap analysis and as a result we couldn’t take appropriate steps to reduce the gap between our desire and our capability, ultimately landing somewhere else in our life. Our desires are still our desires only.

Can we apply six-sigma to build our career? Or at least help our children in doing so?

 

 Related Blog

DMAIC

Understanding 6Sigma: Example-1– Encouraging your Child for Admission in a Good University!

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As a parent you must have had an argument with your child that he should work hard in order to ensure a seat in top 3 universities.
Your child is also good at studies and he is aware of his current capability that he can easily get admission in universities with ranking from 4 to 6 but has to work hard to compete for top 3 universities.
If we consider parent as a customer, then what customer is demanding is the admission in top 3 universities. This is called as “voice of customer” (VOC).

If you consider your child as a supplier, then with his current efforts (current process) he can guarantee admission in the universities with ranking 4-6. This is called as voice of process” (VOP).

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There is a gap between what customer wants (VOC) and what your current process (VOP) can deliver. Both are independent processes but it is desired that VOP should match VOC.

Your child understood this and made a plan to fill the gap by making more efforts.

Above methodology of bridging the gap between customer’s specification (VOC) and the current process capability (VOP) is called as 6Sigma and the methodology used is DMAIC.

 Related Blog

DMAIC

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

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

Hence, Cp formula

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is modified to

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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

Related Posts

What Taguchi Loss Function has to do with Cpm?

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

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

Kindly provide feedback for our continuous journey

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.

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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.

Related Posts

Why & How Cpm came into existence? Isn’t Cpk was not enough to trouble us?

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

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

Kindly provide feedback for our continuous journey