How to report performance metrics so that the most appropriate behavior occurs is provided in this video.
The video’s example shows the use of a free metrics key performance indicator dashboard or KPI dashboard report-out that gives direction for the most appropriate action to occur for a variety of measurement situations.
How to Report Performance Metrics so the Most Appropriate Actions Occur
If management and operations teams are not able to turn data into the most appropriate actions or non-actions for company performance, there is no value having the metric reports. The wise determination of 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 understandable at a glance so both managers and executives know what actions to take.
Organizations need to be careful in how they create and then examine their KPI reports. Organizations often react to the ups and downs of measurements as though these metric changes were special events, where the changes are in reality noise from the overall system. Output variation will be present from most processes; KPI reporting should structurally include this variation. If process output variability is not included in the reporting, the most appropriate decision may not occur. Including variation in KPI reporting is crucial when an organization wants to determine how to report performance metrics and build a great performance culture.
KPI predictive performance reporting is important for the achievement of the most appropriate action (or perhaps non-action) for situations? 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, which did not achieve a target, were a special-cause event and should receive special attention.
This video describes how-to-apply direction of free metrics software for the separation of special-cause events from the common cause output system noise through the application of 30,000-foot-level reporting techniques. When a metric report has only common-cause variation, the software will provide a prediction statement. When there is an undesirable prediction statement response from the software such as a defective rate being too high, this metric enrichment need requires the creation of process-improvement work.
Statistical evidence that process-improvement work benefited a metric’s performance is when the software KPI predictive statement transitions to an improved performance level; for example a defective rate transitions to an acceptable non-conformance rate.