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Control Chart

5 minutes 5 Questions

Control Charts are used to study how a process changes over time. They are powerful tools for detecting whether a process is behaving consistently or whether it has problems. For scrum teams, the control chart measures the cycle time of your product workflow. The X-axis represents the time it takes for an issue to go from ‘in progress’ to ‘done’, and the Y-axis shows when each task was completed. This allows teams to identify patterns, find out if changes to the process are improving product delivery, and predict future performance.

Guide to Understanding and Answering Questions on Control Charts

Importance:
Control Charts are vital in Scrum as they allow teams to understand the variability of a process. They provide an upper and lower control limit, allowing teams to know when a process needs improvement or when it needs to be reevaluated altogether.

What is a Control Chart:
A Control Chart is a vital scrum performance metric that helps in tracking the performance of a team over time. It is a type of run chart that demonstrates the upper and lower control limits. The chart displays the mean, or average of the data points, and also displays control limits based on the expected range of variability.

How it Works:
The Control Chart works by plotting the cycle time or lead time for their workflows over a period. The y-axis on the chart represents the time taken to complete a task, and the x-axis represents the completion of tasks in chronological order. The mean is identified and control limits are set, usually at +/- three standard deviations. If data points fall within these control limits, it indicates that the process is in control. However, if data points fall outside of these limits, the process is then perceived as needing improvement.

Exam Tips: Answering Questions on Control Chart:
For exams, understand the fundamentals of a Control Chart, and remember key components like mean, upper control limit, and lower control limit. Practice reading and interpreting Control Charts. Questions may ask you to identify components of the chat or interpret provided data. Remember that data within the control limits indicates a process in control, and if points fall outside these limits, the process may require adjustments.

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CSM - Scrum Performance Metrics Example Questions

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Question 1

The project control chart shows seven consecutive points on one side of the mean. What may be your ideal next step as a ScrumMaster?

Correct Answer: Investigate whether there is a special cause operating.

A control chart is used to understand process variation and stability. When seven consecutive points fall on one side of the mean, it's typically a signal that there's a special cause of variation. As a ScrumMaster, the ideal next step would be to investigate this anomaly. This special cause could be any factor that is not part of the regular, random variation of the process and it could significantly impact the performance. The other options which suggest increasing the control limits, ignoring the problem, and adjusting the mean do not actually address the root cause and may result in ignoring or hiding critical issues and leading to false results.

Question 2

You're the Scrum Master and notice a few points outside the upper control limit on your team's control chart. How should you interpret this information?

Correct Answer: There might be a special cause variation and I should investigate to see what might have caused these outliers.

The best interpretation when points fall outside the upper control limit in a Scrum team's control chart is that there might be a special-cause variation. This indicates that something unusual has occurred which is not part of the normal process and could disrupt the stability of the project. It's crucial for the Scrum Master to investigate these outliers to identify and address the cause. This could involve assessing any changes to the team's working methods, environmental factors, or unexpected obstacles encountered by the teamAs for the other options, here's why they are incorrect:- 'The team is performing better than expected and I don't need to do anything': This response is incorrect because a point above the upper control limit doesn't necessarily show better performance. Rather, it signals inconsistency that needs to be investigated- 'I should ignore this information as it's probably a mistake': It's unwise to ignore data points that fall outside of the control limits. Ignoring these signals can lead to bigger problems or missed opportunities for improvements- 'Our control limits are incorrectly set and I should adjust them immediately': Simply adjusting the control limits whenever data falls outside them defeats the purpose of having limits. The control limits should be based on the process capability and should not be arbitrarily adjusted each time the data falls outside the limits.

Question 3

In your control chart, if upper and lower control limits are too close, what potential problem could you face?

Correct Answer: More chances of false alarms indicating a problem when there isn't one.

In a control chart, the upper and lower control limits are set to define the range of acceptable variation in a process. If these limits are too close together, it can result in an increased number of false alarms. This happens because even minor variations in the process, which are normal and not indicative of a problem, will appear to fall outside the control limits, thus triggering a false alarm. This can cause unnecessary corrective action, adding cost and potentially disrupting a normally functioning process. The other answers could be misleading. While the control chart's accuracy is not directly impacted by the closeness of control limits, interpretation might be difficult, leading to false interpretations (Answer 159571). The process mean would not shift because of close control limits; rather, it's the sensitivity to variations that increases (Answer 159573). Identifying special cause variations would not be directly impacted, but there might be an overreaction to common cause variations that are falsely determined as special cause variations (Answer 159576).

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