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Control charts are used to identify any changes or shifts in the process and to determine if the process is in a state of statistical control. By tracking process performance over time, control charts can help identify trends, and other sources of variation that may affect the required quality of the output. An Exponentially Weighted Moving Average (EWMA) control chart is a statistical process control tool/chart that is also used to monitor the quality of a process over time however it is a type of moving average control chart that assigns more weight to recent data points than to older ones. This allows the chart to detect changes in the process more quickly than traditional moving average charts. EWMA charts and traditional control charts are used to monitor processes and detect changes in the quality of output. However, there are some important differences between EWMA and traditional charts. Example of EWMA charts that they better at detecting small shifts in the process mean: Moving range (Tradition chart ) measurement of 250 samples, where the data seems within control in the below chart. For the same data when EWMA chart is used, it shows test has failed at point 161 which implies that the data is out of control limit. Test Failed at points: 161 Example: Suppose a manufacturer wants to monitor the thickness of a optical film produced by a machine. They decide to use an EWMA chart to monitor the process. The advantages and disadvantages of using an EWMA chart for this application are: Advantages: Sensitivity to small shifts: The EWMA chart will be more sensitive to small changes in thickness than a traditional control chart, allowing the manufacturer to detect changes in the process sooner. Flexibility: The weighting factor can be adjusted to give more or less weight to recent data points, depending on the specific process being monitored. Disadvantages: Complexity: The manufacturer will need to perform more complex calculations to determine the appropriate weighting factor and control limits for the EWMA chart. False alarms: If the process is stable, the EWMA chart may generate more false alarms due to its sensitivity to small changes. Limited detection of large shifts: The weighting factor used in the EWMA chart gives more weight to recent data, which may obscure longer-term trends in the process. In summary, EWMA charts are a variation of traditional control charts that are designed to be more sensitive to small shifts in the process mean and respond more quickly to changes. However, one needs to be vigilant of the fact that they require more complex calculations for control limits and can generate more false alarms.

We hear this term "Organizational transformation" being used by may organizations in recent times. In most cases that I have come across, it is heavily focused on 'Digital Transformation'. Companiies are trying to automate their processes by using RPA methods, Machine Learning and other advanced Data Analytical tools. However, recently I happend to attend a session, where I listened to the 'transformation journey' narrated by the chairman of a well known manufacuring company. Their approach to transformation was by winning the 'Deming Prize'. Most of the highlights explained by this industry leader was focussed on 'Cultural transformation'. The cultural transformation had to be brought out at all levels of the organization starting from the Chairman. Does the list of transformational approaches that we have identified so far, address this aspect directly or indirectly? We can discuss.