Performance Reporting Issues and 30,000-foot-level Metric

Performance reporting issues and 30,000-foot-level metric resolution to these understood problems provides much value to organizations (less fires to fight). This article addresses these reporting issues and a predictive performance measurement reporting resolution.

Performance Reporting Issues and 30,000-foot-level Metric

Performance measurements in organizations can have issues. Consider how service and manufacturing organizations often present measurements and track performance using a table of number, pie charts, or stacked bar charts (See Performance Reporting (KPI Reports): Issues & Resolution article). In addition, organizations may be using stoplight scorecards throughout the organization as a means to check variance to performance goals for individuals or functions. In addition, customers may be demanding that suppliers meet quality levels using techniques such Acceptable Quality Level (i.e., AQL) (See Acceptable Quality Level (AQL) Issues and Resolution article or process capability indices such as Cp, Cpk, Pp and Ppk, (See Process Capability Cp, Cpk, Pp and Ppk Issues and Resolution article) which also can have shortcomings.

But, are these the best approaches for assessing quality, cost, and time metrics for an organization’s processes? This article will suggest that they are not and will describe an alternative 30,000-foot-level metric approach. The statement, “Tell me how you measure me, and I will tell you how I will behave,” has merit; however, what is not often addressed is how information is presented can make a big difference in leading to the most appropriate behavior or non-behavior. More discussion will be made about this point later, but let’s first talk about making improvements.

It has been stated that if you can improve productivity, or sales, or quality, or anything else, by (e.g.,) five percent next year without a rational plan for improvement, then why were you not doing it last year? To me this statement makes a lot of sense; however, this form of thinking challenges the typical way organizational metrics are presented and goals are set.1

Performance measures in an organization are the result of a process. A mathematical way of expressing this relationship is simply Y=f(X), where Y is the output of a process and X is inputs to the process and the process itself. With this relationship, if a Y response is not desirable, one needs to change either the process or inputs to the process – period. However, the presentation of data using pie charts, stacked bar charts, a table of numbers, and stoplight scorecards does not encourage this thought process. Often organizations manage to the Ys of processes, which could be called – management by hope. We want a better approach than hope management.

What is needed is another form of data presentation that is in alignment to this way of thinking. The alternative is 30,000-foot-level metric reporting, which addresses these needs. 30,000-foot-level metric reporting provides a high-level process output perspective that is not unlike an in-flight airplane view of earth’s terrain. With 30,000-foot-level metric reporting, one is not attempting to quantify the potential impact from typical up-and-down variability differences between process input variables such as people, machines, hours-of-the-day, and lots of raw material. 30,000-foot-level metric reporting2 provides a high-level view of overall process performance reporting.

30,000-foot-level performance can be used to track many, if not most, organizational metrics over time. Application examples include hold time in a call center, delivery time, non-conformance rates, a critical part’s dimension, and virtually any other response from both a transactional and manufacturing process point of view.

I will now give a quick overview of the thought process for creating 30,000-foot-level metric reports. Most process outputs tracked over time will have variability; however, the traditional forms of metric reporting, as discussed previously, do not formally include this component in their reporting. This is an important shortcoming that, when not addressed in a metric, can lead to firefighting and/or playing games with the numbers. The result is unhealthy, if not destructive, organizational behaviors.

What I am suggesting is that metrics need to be presented so that variability is included in performance reporting and the decision-making process. The next question we need to address is: How do we do make this inclusion? Before getting into the details of accomplishing this, we need first to discuss two general forms of variability. These classifications are common and special cause.

Why is this understanding important? Because the behavior someone undertakes when a process has common cause variability can be very different than if a process had special cause. I will illustrate this form of thinking using someone’s go-to-work process.


Common and Special Cause Variability Illustration

Consider the home-to-work commute process of an individual who travels by automobile. Do we expect that his/her commute time will be the same every day – to the second? Obviously this will not occur. Why? Between days travel time can differ because of delay differences from traffic conditions, stoplights, and how fast the vehicle was driven.

Let’s consider that it typically takes 25 to 35 minutes to commute. If it takes one hour longer than usual for a particular day, we can attempt to understand the reason for the individual special-cause instance. Perhaps there was a major traffic accident or a major inclement weather condition.

One should not try to create a causal explanation for all the up-and-down common-cause variability between 25 and 35 minutes; however, that is often what happens in business. For example, an improvement goal might be set not to have travel time longer than 32 minutes. With a red-yellow-green scorecard (i.e., stoplight scorecard), a 33-minute commute would trigger a red signal for causal investigation to resolve the problem, while a 30-minute commute might trigger a green, everything is okay, condition. However, this does not make sense since both these commute times are within the spread of common-cause variability for the process.

If a process response has a common-cause response that is unsatisfactory, process enhancements are needed so that the response from the process transitions to a new, improved level of performance.

Consider that the commuter, in order to reduce his/her commute time, decided to leave one-half hour earlier for the purpose of avoiding traffic. He/she then conducted an experiment by following this new process for some time and noted that the commute time was now 18 to 22 minutes. One could then conclude that this process not only reduced his/her mean commute time but also the variability in commute time as well.


30,000-foot-level Charting Process

30,000-foot-level metric reporting basically is a two-step process.

  • The first step is to determine if a process is stable from a high-level point of view. If a process is stable, then one other important statement can be made. This proclamation is – the process is predictable. Isn’t that exciting? Common-place traditional forms of metric reporting do not address this desired characteristic.
  • The second step for 30,000-foot-level metric reporting is to provide a prediction statement with the understanding that, if this futuristic statement is undesirable, then process improvement effort is needed.

30,000-foot-level reporting includes either one to three graphs that describe process stability from a high-level point of view, along with a prediction assessment and statement, when appropriate.



30,000-foot-level metric reporting provides a predictive measurement system that can be used to help organizations get out of the firefighting mode where they are addressing common-cause issues as though they are special cause. The integration of 30,000-foot-level metrics into an overall Operational Excellence (OE) system can be achieved through Integrated Enterprise Excellence (IEE) implementation. The IEE business management system helps organizations move toward achievement of the 3 Rs of business; i.e., everyone doing the Right things, and doing them Right, at the Right time.

To easily create a 30,000-foot-level report-out, one can download and use a no-charge add-in to Minitab.

Automatically updated 30,000-foot-level metrics that are in alignment with the processes that created them can be achieved through and IEE Enterprise Performance Reporting System (EPRS) software.

For more information see: 30,000-foot-level Reporting Examples with Predictive Measurements



  1. W. Edwards Deming, Out of the Crisis, MIT Press, 1986.
  2. Forrest W. Breyfogle III, Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Bridgeway Books/Citius Publishing, 2008

Content of this article is from Chapters 12 and 13 of Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Forrest W. Breyfogle III, Bridgeway Books/Citius Publishing, Austin, TX, 2008.

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30,000-foot-level training is available through:

30,000-foot-level charting can reduce the firefighting that can occur with traditional business scorecarding systems.

The Integrated Enterprise Excellence (IEE) business management system uses 30,000-foot-level charting to address these issues.

Contact us to discuss application of any Integrated Enterprise Excellence (IEE) technique; e.g., applying the IEE predictive performance metric system to a specific data set or functional