How to Create Predictive Performance Metrics Examples

Answering the question “how to create predictive performance metrics″ provides much benefit to a business. When there are good predictive performance metrics throughout a business, organizations are able to determine where to focus improvement efforts so that they can optimize their overall enterprise financial benefit. One estimate for the benefits from this enhanced metrics approach is: Gartner Says Organizations Using Predictive Business Performance Metrics Will Increase Their Profitability 20 Percent by 2017 .

Business Performance Measurement Improvement Opportunity

Organizations may present their performance measurement or performance metrics as a table of numbers, time-series run chart, or stoplight goal-setting scorecard. However, each of these forms of reporting provides only a historical data statement and does not offer a predicting performance assessment. In addition, this form of reporting can lead to no big-picture benefits and the possibility of undertaking unhealthy, if not destructive, behaviors.

When organizations give focus to addressing the question of how to create predictive performance metrics, their performance measurement system will help them move toward achieving the 3 Rs of business; i.e., everyone doing the Right things, and doing them Right, at the Right time.

This blog will transition three business dashboard reporting methodologies to predictive performance metrics. Each of these metrics, as a business reporting improvement opportunity, will reference another blog for the details of how to make this metric-reporting transition. The dashboard performance measures that will be addressed in this blog are:

  • Table of numbers business performance metrics reporting
  • Red-yellow-green scorecard business performance metrics
  • Time series business performance measurement dashboard

Reference will also be made to

  • An Integrated Enterprise Excellence (IEE) value chain for integrating the display of predictive performance metrics (with an opportunity for automatic updates) and alignment of these predictive metrics with the processes that created the metrics.
  • An IEE system for identifying improvement opportunities that have whole-enterprise benefits.

How to Create Predictive Performance Metrics from a Table of Numbers Business Metrics Reporting

Often organizations report their executive business metrics as a table of numbers. Consider the following table of numbers that an organization has reported. What actions or non-actions would this type of reporting generate? More often than not, with this report-out format no specific actions or non-actions would be generated. Rather, ″stories″ might be stated by a presenter to describe specific up and down movements that occurred. These statements may or may not be true, but they typically don’t provide much value.


Predicting Performance Transition from Table of numbers performance metrics reporting
Predicting Performance Transition from Table of numbers performance metrics reporting


This executive dashboard format does not encourage process thinking. The output of a process is a function of its inputs and process steps. If an enhancement is desired in a process’ response, the inputs or process steps need to be improved.

A 30,000-foot-level predictive performance reporting alternative to the above table of numbers addresses these issues. This form of reporting is described in a Predictive Performance Reporting Alternative to a Table of Numbers Reporting Blog.

This article describes how to transform four of the metrics from the above table into a predictive performance metric reporting alternative.

How to Create Predictive Performance Metrics from Red-Yellow-Green Scorecard Performance Business Metrics

Another common form of organizational performance metric reporting is red-yellow-green scorecards. An example of this reporting type is:


Predicting Performance Transition from Red-yellow-green Scorecard Performance Metrics
Predicting Performance Transition from Red-Yellow-Green Scorecard Performance Metrics


With this form of reporting, goals are established for the metrics that are tracked. Whenever the metric goal during a time interval is achieved, the metric report-out color is green. However, if the color is red, then action should be taken to determine what should be done to transition the metric’s color to green. The problem with this form of reporting is that one can be reacting to normal process variation as though it were an unusual event that should be understood and resolved. Among other things, this type of reporting can lead to much firefighting or playing games with the numbers. The result can be unhealthy, if not destructive, behaviors.

A 30,000-foot-level predictive performance reporting methodology provides an alternative that addresses these stoplight scorecard issues. How to create predictive performance metrics from red-yellow-green scorecard performance metrics is described in a Predictive Performance Reporting Alternative to a Red-Yellow-Green Reporting blog.

In this blog, six of the above red-yellow-green metric report-outs are compared to 30,000-foot-level predictive performance reporting. These illustrations provide a dramatic illustration of how the 30,000-foot-level form of futuristic reporting leads to more productive and beneficial behaviors than stoplight scorecards.

How to Create Predictive Performance Metrics from a Time Series Performance Measurement Business Dashboard

The final performance metric report-out illustration that will be covered in this blog is a time-series plot. An example of this form of reporting is:


Predicting Performance Transition from a Time series performance measurement dashboard
Predicting Performance Transition from a Time series performance measurement dashboard


Again, what actions or non-actions should be taken given this reporting for four measurements? It is difficult to say. What might happen is that one person undertakes one action while another thinks no action should be taken or does something different.

A 30,000-foot-level predictive performance reporting methodology provides an alternative that addresses these time-series-plot issues. How to create predictive performance metrics from this form of performance dashboard reporting is described in a Predictive Performance Reporting Alternative to Time Series Plotting with a Table of Numbers.

This blog illustrates that one would have a much better perspective of how a process is performing with a 30,000-foot-level report-out. Illustrated are how this process view point leads to a much better undertaking for an organization and what actions should be undertaken.

Predicting Performance Dashboard Reporting that can have Automatic Updates

The next question that one might ask is: how could predictive performance measures have a systematic reporting throughout a business? This dashboard-report objective can be accomplished using an IEE value chain.

In addition to an orchestration of metrics, an IEE value chain also integrates 30,000-foot-level performance metrics with the processes that generated the reported measurements. Organizations benefit when they use software for automatically updating these metric report-outs.  The implementation of this software, which provides up-to-date clickable access to the 30,000-foot-level reporting, can save much time and resources. With this system, people throughout an organization can make more appropriate actions or non-actions within their process.

Additional conversions from traditional dashboards to predictive reporting illustrations are available in the article Traditional Dashboards’ Transitions to Predictive Metric Reporting Examples.

Predicting Performance Metrics in a System that highlights where Improvement Efforts should focus so that there are Whole Enterprise Benefits

Organizations often set enhancement goals for many, if not all, of their performance measurements. This may initially appear to be a good thing to undertake; however, to have a long-lasting process-response enhancement from typical everyday up and down variability can be very difficult and involve much resource. When one multiplies this effort by all the processes that an organizational management is stating should be improved, it is quickly concluded that this all-encompassing-improvement objective is not realistic. There are not enough resources to accomplish the task of making a statistical significant change to ″all″ processes.

The good news is that, for an organization to have a big-picture benefit, not all processes need improvement. When focus is given strategically to determining improvement efforts, the bottle neck or constraint of the organization receives the most attention for enhancement. A methodology for accomplishing this big-picture-objective for improvement is the IEE system. In the IEE system, predictive performance measurements (as described above) are orchestrated with analytically/innovatively determined targeted strategies which lead to improvement efforts that benefit the enterprise as a whole.

Integrated Enterprise Excellence (IEE) Business Management System with its Predictive Scorecard Reporting

Integrated Enterprise Excellence (IEE) addresses the business scorecard and process improvement issues that are described in a 1-minute video:


How To Create Predictive Performance Metrics Video

An overview of the IEE Business Management System and its benefits is provided in the article “Positive Metric Performance, Poor Business Performance: How Does this Happen.”


How To Create Predictive Performance Metrics Article


Next Steps to address the question: ″ How to Create Predictive Performance Metrics? ″

Organizations which would like to consider the benefits of creating predictive performance metrics could:


Contact Us to set up a time to discuss with Forrest Breyfogle how your organization might gain much from an Integrated Enterprise Excellence (IEE) Business Process Management System and its 30,000-foot-level reporting methodology.

3 thoughts on “How to Create Predictive Performance Metrics Examples”

  1. This is indeed value add opportunities
    – to reduce complexity and dimensions, and
    – to focus on the few critical to quality or delivery or cost etc. predictive metrics.

    It is not at all easy although, as in reality there are often correlations between inputs or and-or process characteristics, i.e. so-called multi-collinearity, This makes predictions vulnerable and sensitive to very small deviations in inputs and/or in processes.

    Knowing how estimate stable and robust predictive metrics and how to present them in a simple and easy to understand and to make consequent decisions way – is a definite value for any business process manager.

  2. @Elena, thanks for your comment. It is important to note that 30,000-foot-level reporting is not the same as traditional control charting and process capability.

    With 30,000-foot-level reporting, there is an infrequent sub-grouping/sampling approach. This approach breaks up much of the auto correlation issues.

    This and much more is described in a 15 page peer-reviewed article on 30,000-foot-level reporting that was published Feb. 2014. You can download a copy from the link

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