7QC Tools: Why do we Require to Plot X-bar and R-charts Simultaneously

 Abstract: The main purpose of the control charts is to monitor the health of the process and this is done by monitoring both, accuracy and the precision of the process. The control charts is a tool that helps us in doing so by plotting following two control charts simultaneously for accuracy and precision. Control chart for mean (for accuracy of the process) Control chart of variability (for Precision of the process) E.g. X-bar  and R chart (also called averages and range chart) and X-bar  and s chart

The Accuracy and the precision

We all must be aware of the following diagram that explains the concept of precision and accuracy in that analytical development.

Case-1:

If you are hitting the target all the time at the bull’s eye is called as  accuracy and if all your shots are concentrated at the same point then it is called as Precision.

Figure-1: Accuracy and precision

Case-2:

You are off the target (inaccurate) all the time but your shots are concentrated at the same point i.e. there is not much variation (Precision)

Case-2:

It is an interesting case. Your shots are scattered around the bull’s eye but, on an average your shots are on the target (Accuracy), this is because of the average effect. But your shots are wide spread around the center (Imprecision).

Case-4

In this case all your shots are off target and precision is also lost.

Before we could correlate the above concept with the manufacturing process, we must have a look at the following diagram that explains the characteristics of a given manufacturing process.

Figure-2: Precision and Accuracy of a manufacturing process

The distance between the average of the process control limits and the target value (average of the specification limits) represents the accuracy of the process or how much the process mean is deviating from the target value.

Whereas the spread of the process i.e. the difference between LCL and UCL of the process represents the precision of the process or how much variation is there in the process.

Having understood the above two diagrams, it would be interesting to visualize the control chart patterns in all of the four cases discussed above. But, before that let’s have a look at the effect of time on a given process i.e. what happens to the process with respect to the time?

As the process continue to run, there will be wear and tear of machines, change of operators etc. and because of that there will be shift and drift in the process as represented by four scenarios described in the following diagram.

Figure-3: Process behavior in a long run

A shift in the process mean from the target value is the loss of accuracy and change in the process control limits is the loss of precision. A process shift of ±1.5σ is acceptable in the long run.

If we combine figure-1 and figure-3, we get the figure-4, which enable us to comprehend the control charts in a much better way. This gives picture of the manufacturing process in the form of control charts in four scenarios discussed above.

Figure-4: Control chart pattern in case of precision and accuracy issue

Above discussion is useful in understanding the reasons behind the importance of the control charts.

1. Most processes don’t run under statistical control for long time. There are drifts and shift in the process with respect to the time, hence process needs adjustment at regular interval.
2. Process deviation is caused by assignable and common factors/causes. Hence a monitoring tool is required to identify the assignable causes. This tool is called as control charts
3. These control charts helps in determining whether the abnormality in the process is due to assignable causes or due to common causes
4. It enables timely detection of abnormality prompt us to take timely corrective action
5. It provides an online test of hypothesis that the process is under control
1. Helps in taking decision whether to interfere with process or not.
1. H0: Process is under control (common causes)
2. Ha: Process is out of control (assignable causes)

6.  Helps in continuous improvement:

Figure-5: Control Charts provide an opportunity for continuous improvement

7QC Tools: Basis of Western Electric Rules of Control Charts

We all are aware of these famous rule, for beginners let’s understand the basis of these rule. All rules are applied to one half of the control chart. The probability of getting a reaction to the test is ~0.01.

1. A single point outside 3σ control limits or beyond zone A.
• Probability of finding a point in this region = 0.00135 if caused by the normal process. Anything in this region is a case of assignable cause.
2. Two out of three consecutive points in zone A (beyond 2σ).
• Probability of getting 2 consecutive points in zone A = 0.0227*0.0227 = 0.00052
• Probability of 2 out of 3 points in zone A = 0.0227*0.0227*0.9773*3 = 0.0015
3. Four out of 5 consecutive points in zone B (beyond 1σ)
• Probability of getting one points in zone B = 0.1587
• Probability of 4 points in zone B and 1 point in other part of the control chart = 0.1587*0.1587*0.1587*0.1587*0.8413*5 = 0.0027
4. Eight consecutive points on one side of the central line.
• Probability of getting one points in beyond central line = 0.5
• Probability of 8 points in in succession on one side of the central line = 8*0.5 = 0.0039

7QC Tools: Case Study on Interpreting the Control Charts

A process was running in a chemical plant. The final stage of the process was the crystallization, which gave the pure product. There were two crystallizer used for the purpose, each operated by a different individual. The SOP says that crystallizer has to be maintained between 30-40°C and for 110 to 140 minutes. The data for a month is captured below

In order to understand the process, I-MR control chart was plotted (for simplicity, R-chart is not captured).

As we have learned from the earlier blog, the alternate points above and below the central line represents some short of stratification (see the short connecting arms and the concentration of data points in zone B and C).

We plotted the histogram of the above data set and kept on increasing the number of classes. What we saw was the emergence of a bimodal distribution as we kept on increasing the number of classes.

So, one thing was sure, there were two processes running in the plant. Now question that was to be answered was “What is causing this stratification?”

We started with crystallizer, as soon as we plotted the simple run chart of the process with groups using Minitab®, we could see the difference. Crystallizer-2 was always giving better yield. This should not happen because both the crystallizer were identical and were connected to same utilities. Then we thought about the different operators might be the reason for this behavior, as this was the only factor that was different for both the crystallizer.

When we plotted the same run chart with grouping, but this time operator was used for the purpose of grouping. We got the same result as was found with the crystallizers, the operator-2 working on the crystallizer-2 was producing more quantity of the product. This run chart is not shown here.

We further grilled down to the operating procedure adopted by the two operators. We studied temperature and the maintenance time using scatter plot. The results are shown below

Finally, it was found that operator-2 was maintaining the crystallizer-2 at the lower end of the prescribed temperature and for longer duration. Hence, specification for temperature and the maintenance time was revised.

7QC Tools: Interpretation of Control Charts Made Easy

Visual Inspection of the Control Charts for Unnatural Patterns

Besides above famous rules, there are patterns on the control charts that needs to be understood by every quality professionals. Let’s understand these patterns using following examples. It would be easier to understand them if we can imagine the type of distribution of the data displayed on the control chart.

Case-1: Non-overlapping distribution

As a production-in-charge, I am using two different grades of raw material with different quality attributes (non-overlapping but at the edge of the specification limits) and I am assuming that the quality attributes of the final product will be normally distributed i.e. I am assuming that most of final product will hit the center of the process control limits.

If the quality of the raw material is detrimental to the quality of the final product then my assumption about the output is wrong. Because the distribution of the final product quality would take a bimodal shape with only few data at the junction of the distribution. Same information would be reflected onto the control chart with high concentration of data points near the control limits and fewer or no points near the center. Here is the control chart of the final product

In this completely non-overlapping distribution, there will be unusual long connecting arms in the control charts. There will be absence of points near the central line.

If we plot the histogram of this data set and go on increasing the number of classes, the two distribution would get separated.

So, whenever we see a control charts with the data points concentrated towards the control limits and no points at the center of the control charts, immediately we should assume that it is a mixture of two non-overlapping distribution. Remember long connecting arms and few data points at the center of the control chart.

Case-2: Partially overlapping distribution

Assume this scenario: A product is being produced in my facility in two shifts by two different operators. Each day I have two batches, one in each shift. There is a well written batch manufacturing record indicating that the temperature of the reactor should be between 50 to 60 °C. The control chart of a quality attribute of the product is represented by following control chart.

We can see that the data points on the control chart are arranged in an alternate fashion around the central line. The first batch (from the 1st shift) is below the central line and next batch (from the 2nd shift) is above the central line. This control chart shows that even we are following the same manufacturing process, there is a slight difference in the process. It was found that the 1st shift in-charge was operating towards 50 °C and the 2nd shift in-charge was operating towards 60 °C. This type of alternate arrangement is indication of stratification (due to operators, machines etc.) and is characterized by short connecting arms.

There are the cases of partially overlapping distribution resulting in a bimodal distribution, which means that there will be few points in the central region of the control charts but, majority of the data points would be distributed in zone C or B. In such cases, it would be appropriate to plot the histogram with groups (like operator, shift etc).

Case-3: Significant Overlapping distribution

If there is significant overlap between the two input distributions then it would be difficult to differentiate them in the final product and the combined distribution would give a picture of a single normal distribution. Suppose the operators in the above case-2 were performing the activity at 55 °C and 60 °C respectively. This would result in an overlapping distribution as shown below

Case-4: Mixture of unequal proportion

As a shift-in-charge, I am running short of the production target. What I did to meet the production target was to mix the current batch with some of the material produced earlier for some other customer with slightly different specification. I hoped that it wouldn’t be caught by the QA!. The final control chart of the process looked like

We can see from the control chart that if two distributions are mixed in an unequal proportions then the combined distribution would be an unsymmetrical distribution. In this case one-half of the control chart (in present case the lower half) would have maximum data points and other half would have less data points.

Case-5: Cyclic trends

If one observe a repetition of the trend on the control chart, then there is a cyclic effect like sales per month of the year. Sales in some of the specific months are higher than the sales in some other months.

Case-6: Gradual shift in the trend

A gradual change in the process is indicated by the change in the location of the data points on the control charts. This chart is most commonly encountered during the continuous improvement programs when we compare the process performance before and after the improvement program.

If it is observed that this shift is gradual on the control charts, then there must be a reason for the same, like wear and tear of machine, problem with the calibration of the gauges etc.

Case-7: Trend

If one observe that the data points on the control charts are gradually moving up or down, then it is a case of trend. This is usually cause by gradual shift in the operating procedure due to wear and tear of machines, gauges going out of calibration etc.

Summary of unnatural pattern on the control charts
 Unnatural pattern Pattern Description Symptom in control chart Large shift (strays, freaks) Sudden and high change Points near and or beyond control limits Smaller sustained shift Sustained smaller change Series of points on the same side of the central line Trends A continuous changes in one direction Steadily increasing or decreasing run of points Stratification Small differences between values in a long run, absence of points near the control limits A long run of points near the central line on the both sides Mixture Saw-tooth effect, absence of points near the central line A run of consecutive points on both sides of central line, all far from the central line Systematic Variation or stratification Regular alternation of high and low values A long run of consecutive points alternating up and down Cycle Recurring periodic movement Cyclic recurring patterns of points

For the case study see next blog

7QC Tools: My bitter experience with statistical Process Control (SPC)!

I just want to share my experience in SPC.

In general, I have seen that people are plotting the control chart of the final critical quality attribute of a product (or simply a CQA). But the information displayed by these control charts is historical in nature i.e. the entire process has already taken place. Hence, even if the control chart is showing a out of control point, I can’t do anything about it except for the reprocessing and rework. We often forget that these CQAs are affected by some critical process parameters (CPPs) and I can’t go back in time to correct that CPPs. The only thing we can do is to start a investigation.

HENCE PLOTTING CONTROL CHARTS IS LIKE DOING A POSTMORTEM OF A DEAD (FAILED) BATCH.

Instead, if we can plot the control chart of CPPs and if these control charts shows any out of control points, IMMEDIATLY WE CAN FORECAST THAT THIS BATCH IS GOING TO FAIL or WE CAN TAKE A CORRECTIVE ACTION THEN AND THERE ITSELF. This is because CPPs and CQA are highly correlated and if CPPs shows an out of control point on its control chart, then we are sure that that batch is going to fail.

Hence, the control charts of CPPs would help us in forecasting about the output quality (CQA) of the batch because, the CPP would fail first before a batch fails. This will also help us in saving the time that goes into the investigation. This is very important for the pharmaceutical industry as everyone in the pharmaceutical industry knows, how much time and resource goes into the investigation!

I feel that we need to plot the control chart of CPPs along with the control chart of CQA, with more focus on the control chart of CPPs. This will help us in taking timely corrective actions (if available) or we can scrap the batch, saving downstream time and resource (in case no corrective action available).

Another advantage of plotting the CPP is for looking for the evidence that a CPP is showing a trend and in near future it will cross the control limits as shown below, this will warrant a timely corrective action of process or machine.

CQA: Critical Quality attribute

CPP: Critical Process Parameter

OOS: out of specification

7QC Tools — The Control Charts

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).
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)
3. The main characteristic of the above curve is shown below. Example, the area under ±2σ would contain 95% of the total data
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.
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.

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.

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

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.

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.

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.

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.

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-1– Encouraging your Child for Admission in a Good University!

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).

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.

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