How to Report Performance Metrics – Continuous Response

The example provided in this video shows the use of a free metrics KPI dashboard report-out that gives direction for the most appropriate action to occur for a variety of measurement situations.
There is no value having metric reports of company performance if management and operations teams are not able to turn the data into the most appropriate actions or non-actions. Wisely determining key performance indicators (KPIs) can provide meaning to what actions or non-actions are most relevant. Reports need to be in a format that is easy to understand so that at a glance, managers or executives know what action to take.
However, organizations need to take care in how they create and then examine their KPI reports. Often organizations react to the ups and downs of measurements as though these metric changes are special events, where, in reality, the changes are noise from the overall system. The output of most processes will have variability, and KPI reporting should include this variability structurally. If there is no inclusion of variability in the reporting, the most appropriate decision may not occur. The inclusion of variability in KPI reporting is a critical need to address when an organization wants to determine how to report metrics and build a great performance culture.
Why is predictive KPI performance reporting important to achieve the most appropriate action or non-action for a given situation? The common-place tracking of KPI performance against organizational goals can lead to inappropriate reactions. An example of this occurrence is reacting to common-cause variability as though a measured response (that did not meet a target) were a special-cause event and should receive special attention.
This video shows the application of free metrics software that separates special-cause events from the common-cause-response-output noise of the system. When only common-cause variability exists, the software will report a prediction statement. If the prediction statement response provided by this software is undesirable (e.g., defective rate is too high), this metric enhancement need pulls for the creation of process-improvement work.
″Proof″ that process-improvement work was beneficial to enhancing a metric’s performance is when the software KPI predictive statement transitions to an improved performance level (e.g., defective rate transitions to an acceptable rate).
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