7QC Tools: Case Study on Interpreting the Control Charts

    Amrendra Roy

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    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

    picture109 In order to understand the process, I-MR control chart was plotted (for simplicity, R-chart is not captured).

    picture110

    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.

    picture112

    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.

    picture114When 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

    picture115

    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.

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    Comments

    7 thoughts on “7QC Tools: Case Study on Interpreting the Control Charts”

    1. Thanks for these articles on six sigma which explains the concept in a way easy to learn and understand. It really taught me how to identify actionable from data and I used histogram today in my ppt and it made my day. Thank you!

    2. thanks for your experience and information which help me to understand the variation more and more deeper
      can we see case study for hypthese test and how we use it in actual life

      1. Thanks for the appreciation
        OK I will try to make a case study on hypothesis testing.
        Kindly recommend this site to others

        1. Dr. Roy,
          It is an interesting case you have shared. Thanks for the same.

          Since there are two crystallizer’s are involved, I am of the opinion evaluation of the process being in control or not is to be carried out separately for each Crystaller.

          I find the run chart of Yield for the Crystallizer no..1 is showing special cause variation. The same is confirmed when we draw I-MR chart (individual values Chart going out of Control).

          In case of the Crystallizer 2, we do not have any special cause variation thrown up by run chart of Yield and the same is confirmed by Individual Values chart.

          I think one needs to work on the Process followed at Crystallier 1 for removing special causes and then move forward.

          Regarding the Histograms, It is advisable to draw Histogram for each process/ equipment/ shift/ operator. Whenever we combine the data from more than one source, we are likely to end up with multi modal histograms.

          Hope the group finds these views interesting and logical. I am open to different ways of looking at the data.

          Happy Deepavali

          Prasad

    3. Hi, Its great that a lot of drill down was done.
      I have one query in the Yield to be 101 in the first data set.
      Also suggest if we can also have a Null Hypothesis testing to find the significant contributor.

      1. These are the yields in Kgs
        Yes we can use hypothesis testing (in the form of t-test) to find the significant contributor or you can also use regression analysis

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