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KPIs with Control Charts

by | Feb 7, 2024

W. Edwards Deming popularized the use of Statistical Process Control as a means to improve quality. This method transformed Japanese industry into an industrial power after being completely destroyed after World War 2. opsZero implements Statistical Process Control and we use Control Charts to improve our processes as Quality is a part of our Principles.

We have certain goals with our KPIs (Key Performance Indicators):

1. Conservative. We want our numbers to reflect reality.

2. Consistent. We should have all the KPIs charts look the same with a 1 sigma control on both the Upper and Lower Limits. We also want our charts to have a goal of up and to the right for any goal we setup. This makes it easy to see at a glance if we are attaining our goal.

To achieve this, we will use Control Charts. A Control Chart tracks a process over time. We monitor the process average, the Upper Control Limit, and the Lower Control Limit, which are a certain number of standard deviations from the average.

Control Limits are based on the standard deviation. The aim is to ensure that the process stays within its control limits with the goal of gradually tighten those limits over time. Have a 3 sigma upper and lower limit may not be effective, but as we reduce variance getting the average within 1 sigma or even 0.5 sigma may be ideal. Again this is based on the needs of the process we are optimizing.

The nice thing about Control Charts is that they can be used for revenue goals, process goals, and basically any goal that can be measured over time. This makes it a useful chart across functions.

Control Charts are used for all at a glance KPIs at opsZero to find patterns for how we are moving towards our target. In addition we use Pareto Charts to build products that reduce our Support and Sales issues which we will cover in a future post.

Here is a way to generate control charts in Python.

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