Balanced Scorecard KPI Predictive Dashboard Examples

The following balanced scorecard KPI predictive dashboard examples provide a description of a methodology for reporting that can eliminate much organizational firefighting. The need for the described predictive metrics is supported by the article Gartner Says Organizations Using Predictive Business Performance Metrics Will Increase Their Profitability 20 Percent by 2017 . Organizations benefit when they integrate predictive performance metrics into their balanced scorecard dashboard or balanced scorecard KPI.

Balanced Scorecard KPI Dashboard Reporting Example

The following key performance indicator (KPI) is currently being reported in an organization. This data from this dashboard will later be used to illustrate how typical balanced scorecard dashboard reporting methodologies can be enhanced.


Balanced Scorecard KPI Predictive Dashboard Examples - Service Reliability Traditional Report-out
Balanced Scorecard KPI Predictive Dashboard Examples – Service Reliability Traditional Report-out


What actions or not actions would result from this form of KPI presentation reporting, which could be a part of an organizational balanced scorecard reporting methodology? It is hard to say. What often happens, if someone were to present this type of chart, is that the presenter would often try to provide an explanation for the ups and downs of each plotted time period.

With this type of balanced scorecard KPI dashboard reporting, typically there is no mention whether the process is stable or not and if the process were stable, no estimation is made about current process performance capability. In addition, this form of performance reporting makes no statement about expected future process performance. If such a futuristic statement could be made, then it should be understood that process improvements would be needed, if the process’ response performance is unsatisfactory.

Balanced Scorecard KPI Predictive Dashboard Examples

A dashboard reporting methodology that provides a predictive statement, when appropriate, is called 30,000-foot-level reporting . Presenting data from the above process using this predictive dashboard reporting technique for a balanced scorecard KPI yields the following:


Balanced Scorecard KPI Predictive Dashboard Examples -Service Non-availability Predictive Reporting
Balanced Scorecard KPI Predictive Dashboard Examples -Service Non-availability Predictive Reporting


Several points could be made from this chart; i.e., the first illustration of several balanced scorecard KPI predictive dashboard examples which will be later referenced:

  • The above KPI report-out included a longer period of time than the initial chart shown for this balanced scorecard KPI; hence, more knowledge was gained from a process point of view. This is valuable to determine if something changed or not.
  • The upper and lower control limits (UCL and LCL) are used to separate common-cause variability from special cause for this balanced scorecard KPI. That is, regular variability (common) from a process from unusual events (special).
  • From this graph, there were three points that had were considered to be special cause for this balanced scorecard KPI. These points should be under consideration for investigated for causal reasons; however, the other points for this balanced scorecard KPI are considered sources of common-cause variability from the process.
  • For those familiar with statistical process control (SPC) charting, one might infer that 30,000-foot-level reporting is the same as this form of traditional manufacturing reporting. This is not the case. For one thing, the 30,000-foot-level reporting format uses an individuals chart rather than a p-chart, as described in the article, issues and resolution to p-charting .
  • Plotting an attribute rate for this balanced scorecard KPI that is bounded by 100% for this has difficulties since the individuals chart is not robust to non-normality of data. Data of this type is often non-normal. Because of this, data values were subtracted from 100% to create a non-availability metric. Data was given a log-normal transformation, which makes sense for this type of data, which is bounded by zero.
  • The process is basically stable with a 3.2 percent non-availability rate (or 96.8% reliability).
  • Because there is probability relative to occurrences, we expect that some weeks will be higher and other weeks will be lower than the average of 3.2 percent. We should not react to individual points in the control limits trying to determine specific reasons for their departure from the nominal.
  • The goal set for this process was a service reliability of 98% or 2% non-availability. Since our mean value of 3.2% is larger than 2%, we are not meeting our objective. When there is common-cause variability and the response is undesirable, process improvement efforts are needed. Even thought the goal of 2% for this balanced scorecard KPI might have been established for some time, this second charting predictive dashboard methodology demonstrates how nothing has been demonstrated to improve in the process.
  • Improvement efforts for this process could involve a Lean Six Sigma project, kaizen event, Deming’s plan-do-check act, or another type of process improvement effort. The confirmation that an improvement was made to the process is when the 30,000-foot-level individuals chart transitions to an enhanced level of performance.
  • One approach for determining what might be done differently is to build statistical hypothesis test for the region of stability. For example, do we experience differences by machine type, people, or days of the week that affect the response? Factors found significant could provide insight to what might be done differently.

The 30,000-foot-level predictive performance methodology for reporting dashboard measurements in an organization often completely changes the behavior of an organization for their reacting to data. Use of 30,000-foot-level reporting can significantly reduce the amount of firefight in an organization where common-cause variability incidents are reacted upon as thought they were special cause.

Dashboards with Predictive Performance Metrics

The above 30,000-foot-level reporting can be extended throughout an organization through the use of an Integrated Enterprise Excellence (IEE) value chain. An advantage of this approach is for performance dashboard reporting is:

Additional Balanced Scorecard KPI Predictive Dashboard Examples

The following KPI scorecard examples were converted to predictive performance metric reporting. These balanced scorecard key performance indicators examples illustrate what could be done to formulate a balanced scorecard template for various situations.

A summary of KPI conversions to predictive performance reporting illustrations are available in the article Transitioning Traditional Dashboards to Predictive 30,000-foot-level Metric Reporting Examples.

Application of Balanced Scorecard KPI Predictive Dashboard Examples

Additional information about 30,000-foot-level balanced scorecard key performance indicators predictive dashboard examples can be found through the following:

More information about the Integrated Enterprise Excellence balanced scorecard KPI predictive dashboard examples system that integrates with organizational strategy development and improvement efforts so enterprise financials see the benefit:

To address how 30,000-foot-level ″Balanced Scorecard KPI Predictive Dashboard Examples″ or IEE business intelligence system applies to specific situations, contact me at:

Please let me know what you think about the concepts in this blog by entering your comments.

Entering your social media inputs about this article through the Google+, Facebook and/or LinkedIn icons would also be great.

To reiterate, contact me to discuss any application possibilities.