Red-yellow-green scorecards or stoplight scorecards, which assess functional performance relative to established goals, can lead to firefighting and not the best behavior.
Stoplight scorecards assess how well a function is performing relative to established goals, where, in a tracking-to-goal system, a red-colored metric is a signal for attention since a performance goal is not being met, while a green-colored measurement indicates that a goal objective is being achieved.
Stoplight Scorecards Issues
Stoplight scorecard organizational tracking can initially appear to be an attractive approach for business management; however, red-yellow-green scorecards can lead to much firefighting. Often, when using red-yellow-green scorecards, common-cause variability is reacted to as though it were special cause, as described in the article Control Charting Issues: 30,000-foot-level Chart Resolution). Reacting to common-cause issues as though they were special cause can lead to much frustration, wasted resources, firefighting, and often playing games with the numbers.
Stoplight Scorecards Issues Resolution
Note: Content of this webpage is from Chapter 3 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.
The example, which is presented later in this article, describes how organizations can benefit from a Statistical Business Performance Charting (SBPC) scorecard or dashboard system, which can guide them to the most appropriate performance-measurement system actions or non-actions in both manufacturing and transactional processes.
An SBPC system can provide predictive 30,000-foot-level Reports with Predictive Measurements throughout an organization. When this occurs, futuristic 30,000-foot-level functional reporting assessments can be utilized collectively to determine which performance metrics need improvement so that the enterprise as a whole benefits. The metrics that need enhancements will require improvement efforts within the processes that impact these metrics. It should be noted how this approach is quite different from a typical stop-light scorecard approach, where goals are set throughout the organization with no specific linked process-improvement activities for accomplishing these improve-performance measurement goals. The simple setting of metrics goals without an improvement plan is not effective and can cause unhealthy, if not destructive, behaviors.
Predictive Performance Reporting and Improvement
Dr. Lloyd S. Nelson in Deming’s Out of the Crisis1 book stated, “If you can improve productivity, or sales, or quality, or anything else, by 5 percent next year without a rational plan for improvement, then why were you not doing it last year?” To me, this addresses a fundamental problem with the implementation of red-yellow-green goal-setting scorecards, which can lead to much firefighting and management by hope.
When setting goals, it is important to remember that the output of a process (Y) is a function of its inputs (Xs) and the step sequence, which can be expressed as Y=f(X).
What is needed is an honest assessment of how well an overall process-managed system is addressing the overall needs of customers and the business as a whole. The reason for doing this is to determine which of the following actions or non-actions, as presented in Table 1, is most appropriate for any given situation.
30,000-foot-level reporting of functional performance metrics provides a high-level view of how processes are performing so that organizations can move toward achievement of the 3 Rs of business; i.e., everyone doing the Right things, and doing them Right, at the Right time.
We are not attempting through 30,000-foot-level performance reporting to manage the process in real time. With this performance measurement system, we consider differences between sites, working shifts, hours of the day, and days of the week to be a source of common-cause input variability to the overall process.
Predictive Performance Reporting and Improvements in lieu of Stop Light Scorecards
The 30,000-foot-level predictive performance reporting system has two steps in its reporting. The first step is to evaluate the process for stability. This is accomplished using an individuals chart where there is an infrequent subgrouping time interval so that input variability occurs between subgroups. For example, if we think that Friday’s checkout time in a popular grocery store could be longer than the other days of the week because of increased demand, a weekly subgrouping frequency could be most appropriate when tracking the checkout time process.
The second step in the 30,000-foot-level charting system is to determine a region of stability. If there is a recent region of stability, a predictive statement can then be made by considering data from the most recent stable-process-performance timeframe to be a random sample of the future. If data are continuous, this prediction statement can be presented using a probability plot, where normal and log-normal are the most common types of distribution plots.
If the prediction from a stable process is undesirable, process enhancements are needed to transition the performance report-out to a new, improved level of performance. This improvement effort could be undertaken through a Lean Six Sigma project, kaizen event, plan-do-check-act efforts, or some other approach.
Real-data Example: Comparing Red-Yellow-Green Scorecards to 30,000-foot-level Charting
Figure 1 illustrates an actual organizational red-yellow-green scorecard. This form of scorecard reporting is often appealing to managers in that they can examine the colors in the latest timeframe to determine who needs to be working on improving goal-driven metrics. However, does this management approach move organizations toward achievement of the three Rs of business? Let’s assess the effectiveness of this practice by examining one of these metrics in detail.
Figure 1: Red-yellow-green tabular scorecard example.
Figure 2 evaluates in further details one of the metrics from the scorecard shown in Figure 1. For this real application example, there were red indicators five of the thirteen reporting periods. The individuals control chart in the 30,000-foot-level report-out indicates that no process improvements were made, even though the color often changed from red to green. This 30,000-foot-level probability plot report-out indicates that there is a common-cause non-conformance rate of about 33%.
From an examination of the 30,000-foot-level report-out portion of Figure 2, we would need to work on step number three in Table 1, assuming that 33% level of non-conformance is a current business-priority-improvement need when considering other organizational measurement performances. A successful implementation of this process-improvement option would be demonstrated by the 30,000-foot-level individuals chart shift to a new, stable improved level of performance.
Figure 2: Comparison of a red-yellow-green scorecard to 30,000-foot-level predictive measurement reporting
(Histogram included for illustrative purposes only).
Comparing Red-Yellow-Green Scorecards to 30,000-foot-level Charting
As noted earlier, when an organization utilizes a red-yellow-green scorecard system, it takes action whenever the color is red; however, the 30,000-foot-level reporting indicates that in this example all the red-colored events are common-cause variability. That is, the perceived improvements when changing from red to green were simply common-cause variability occurrences. From the 30,000-foot-level reporting, one would conclude that no process improvements were in fact made, even though the color changed from red to green several times.
When undertaking process improvement effort (Table 1, item number 3), assignable causes that negatively impact process performance from a common-cause point of view can determined by examining data in the latest region of stability. Collected data in this most recent stable region can be used to test hypotheses that assess differences between such factors as machines, operators, day-of-the-week, raw material lots, and so forth. This investigation can provide insight to where focus should be given to determine what might be done or what further investigations be made to improve the process. This is a more efficient analytical discovery approach than reacting to a red signal and expending effort trying to determine why the negative-to-goal signal occurred.
Improvement to the system would be demonstrated by the 30,000-foot-level individuals control chart shifting to an improved level of stable performance.
Organizational Predictive Performance Reports
In many organizational business systems, there is a need for providing predictive measures. 30,000-foot-level reporting provides a charting system that addresses this need.
With this form of reporting, when there is a recent region of stability, we can consider data from this timeframe to be a random sample of the future. With this statistical business performance charting approach, we might be able to report that our process has been stable for the last three days, three weeks, three months, or three years with a prediction that 20% of the incoming calls to a call center are on hold longer than 40 seconds.
30,000-foot-level charting technique has many applications, as described in:
- 30,000-foot-level Performance Reporting Applications.
- Transitioning ten traditional scorecards/dashboards to predictive performance measurement reporting.
Organizations gain much when they integrate 30,000-foot-level metrics within their business functional process map or Integrated Enterprise Excellence (IEE) value chain, where the organizational chart is subordinate to this enterprise-view process map. When the enterprise is analyzed as a whole with a blending of analytics, goals can be set for 30,000-foot-level functional metrics so that the business as a whole benefits; i.e., avoiding silo process improvement efforts that might sound good but are not as beneficial as they initially seem relative to the big financial picture.
Organizations benefit when they automatically update and report-out their 30,000-foot-level predictive performance metrics in a clickable format that is tied to the business’ IEE value chain using Enterprise Performance Reporting System (EPRS) Software.
Business System Application
The above described reporting methodology addresses one of the challenges that executives have, as described in the article Executive Challenges and Resolution.
Organizations benefit when they use a predictive performance metric system within an enhanced business management system implementation. One aspect of this IEE implementation is transitioning historical reported dashboard and scorecards to predictive reports. Ten illustrations and benefits of this transition are shown in the article predictive performance metrics.
The Integrated Enterprise Excellence (IEE) business management system and its enhanced scorecard reporting approach addresses the issues described in a 1-minute video:
- W. Edwards Deming, Out of the Crisis, MIT Press, 1986.
- Forrest W. Breyfogle III, Integrated Enterprise Excellence Volume II – Business Deployment: A Leaders’ Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Bridgeway Books/Citius Publishing, 2008
- 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
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 that addresses the issues with red-yellow-green stoplight scorecards.