Organizations benefit from scorecard template procedure predictive scorecards, which provides additional insight for improved decision making. This balanced organizational scorecard template provides a procedure for creating predictive statements for performance scorecards. The described approach addresses the shortcomings of many traditional performance metrics report-outs.
Shortcomings of Traditional Scorecard and Performance Reporting
- Traditional metric reporting often has a table of numbers, pie charts, or stacked bar charts, illustrated in Figure 1. These charts are not predictive and have a similar decision-making view as to driving a car by looking only at the rear view mirror; i.e., an unhealthy behavior.
- Variance to metric goals can lead to playing games with the numbers and behaviors that can be destructive for the business as a whole.
- Red-yellow-green scorecards can lead to resource draining firefighting, where resolutions are often not long-lasting.
- The balanced scorecard decision to choose metrics after selecting strategies can lead to very subjective and short-term measurements, which are a function of leadership and economic-environmental changes.
Traditional Scorecard Methodologies
The Integrated Enterprise Excellence (IEE) system, with its predictive scorecard and other attributes, resolves these issues and gets organizations out of the firefighting mode, where common-cause issues are often reacted to as though they were special cause. Provided is a scorecard template procedure predictive scorecards.
Scorecard Template Procedure Predictive Scorecards: Comparing Predictive Performance Measures with a Stoplight Scorecard
The red-yellow-green scorecard shown at the bottom of Figure 1 is from a corporation’s actual scorecard system. Let’s now further examine this scorecard in Figure 2 where it is compared to an IEE 30,000-foot-level balanced organizational scorecard system reporting; i.e., where 30,000-foot-level is to represent a high-level-metric view, like an airplane view of the earth.
Comparing Stoplight Scorecard to IEE 30,000-foot-level metric Reporting
The IEE metric system has two steps for a scorecard template procedure predictive scorecards. The first step of this process is to analyze for predictability. The second step is the formulation of a prediction statement, when the process is considered predictable.
To determine predictability, the process is assessed for statistical stability using a 30,000-foot-level individuals control chart, which can detect if the process response has changed over time and is stable. The second step is the formulation of a prediction statement, when the process is considered predictable.
To determine predictability, the process is assessed for statistical stability using a 30,000-foot-level individuals control chart, which can detect if the process response has changed over time and is stable. A process could have multiple regions of stability, where the latest region of stability could have been for the last three weeks, three months, or three years.
When there is a current region of stability, data from this last region can be considered a random sample of the future. For this example, note how the 30,000-foot-level control chart in Figure 2 indicates that nothing has changed, even though a traditional red-yellow-green scorecard showed the metric frequently transitioned among red, yellow, and green. For the traditional scorecard, the performance level was red 5 out of the 13 recorded times.
Included in this figure is a probability plot that can be used to make a prediction statement. Much can be learned about a process through a probability plot. Let’s next examine some of these probability-plot-benefit characteristics.
The x-axis in this probability plot is the magnitude of a process response over the region of stability, while the y-axis is percent less than. A very important advantage of probability plotting is that data do not need to be normally distributed for a prediction statement to be made. The y-axis scale is dependent upon the distribution type; e.g., normal or log-normal distribution.
If the data on a probability closely follows a straight line, we act as though the data are from the distribution that is represented by the probability plot coordinate system. Estimated population percentages below a specification limit can be made by simply examining the y-axis percentage value, as shown in Figure 2.For this case, we estimate that about 33% of the time, now and in the future, we will be below our 2.2 specified criterion or goal.
There is a certain amount of technical training needed to create 30,000-foot-level metrics; however, the interpretation of the chart is quite simple. In IEE, a box should be included below the chart that makes a statement about the process. For this chart, we can say that the process is predictable with an approximate non-conformance rate of 32.8%. That is, using the current process, the metric response will be below the goal of 2.2 about 1/3 of the time.
As a business-management policy, red-yellow-green versus IEE reporting can lead to very different behaviors. For this example, a red-yellow-green reporting policy would lead to fighting fires about 33% of the time because every time the metric turned red, management would ask the questions, “What just occurred? Why is our performance level now red?” Red-yellow-green scorecards can result in counter-productive initiatives, 24/7 firefighting, the blame game, and proliferation of fanciful stories about why goals were not met. In addition, these scorecards convey nothing about the future.
With IEE-performance-metric reporting, we gain the understanding that the variation in this example is from common-cause process variability and that the only way to improve performance is through improving the process itself. With IEE, someone would be assigned to work on improving the process that is associated with this metric. This assumes that this metric improvement need is where efforts should be made to improve business performance as a whole.
In organizations, the IEE value chain functions and metrics maintain basic continuity through acquisitions and leadership change. The IEE value chain with its 30,000-foot-level metric reporting can become the long-lasting front end of Jim Collins’ Level-Five System and a baseline assessment from which strategies can be created and improvements made.
IEE addresses the issues with traditional scorecards and organizational improvements that are described in a 1-minute video:
The above was taken from the Integrated Enterprise Excellence, Volume II – Business Deployment: A Leaders’ Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Forrest W. Breyfogle III Citius Publishing, copyright 2008.
Details on creating 30,000-foot-level metrics for various types of data is described in 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 Citius Publishing, copyright 2008.
More Information about Scorecard Template Procedure Predictive Scorecards
For additional information about Integrated Enterprise Excellence (IEE) scorecard template, see:
- Scorecard template procedure predictive scorecards information: Articles, Videos, Books
- Scorecard template that addresses an executive challenge
- Scorecard template for transitioning traditional scorecards to predictive reports (10 illustrations)
- Predictive performance metrics within an enhanced business management system
- Scorecard template that integrates with business process management (BPM)
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 scorecard template procedures for predictive scorecards.