Business metrics reporting enhancements are achieved when organizations use 30,000-foot-level reporting throughout their organization.
Pictorially, 30,000-foot-level reporting is not unlike someone looking out of an airplane in flight at the terrain below. An airplane-in-flight viewer has only a high-level view of the terrain below and does not see all the up-and-down changes in the ground below the airplane. Similarily, 30,000-foot-level reports provide a high-level view of a process-output or key performance indicator (KPI) response.
Business Metrics Reporting Enhancement: Why 30,000-foot-level Performance Metric Reporting is Different and Better than Traditional Scorecard Reports
Traditional scorecard reports give focus to the achievement of measurable goals at some point in time, where these metric objectives are often arbitrarily set. Organizations need a business metrics reporting enhancement methodology.
In IEE, this form of management is called Y-management. The practice of Y-goal-setting management does not structurally give focus to addressing the reality that a Y-process-response is the result of the Xs executed in the processes; that is, Y=f(X). The achievement of long-lasting Y-enhancements is through improving the processes’ Xs.
Y-Management can lead to very unfavorable, if not destructive, organizational behaviors, including playing games with the numbers to achieve a next reporting period Y-response goal and making things appear better than they are.
Performance metrics typically have over-time variation, which could be large or small. Traditional scorecard reports do not typically include this variation in their reporting, for example, in red-yellow-green scorecards or table-of-numbers reporting. The exclusion of process-response variation in performance metric reporting is a big deal!
The achievement of long-lasting Y-response improvements is through the enhancement of the processes that deliver the Y-response; that is, the Xs. Because of the Y=f(X) relationship, it is better to provide a process-output reporting that includes measurement variation. The inclusion of variation in performance-metric reporting can provide a more accurate representation for current and future expectations from the process-output response, with the understanding that, if this response-statement is undesirable, there is a need for enhancing the process; that is, the Xs.
The 30,000-foot-level performance metric reporting methodology fulfills the requirement for inclusion of process variation in reported statements. Besides, 30,000-foot-level reports can often provide a futuristic statement that, when unacceptable, pulls for the creation of a process improvement project.
Business Metrics Reporting Enhancement: Why 30,000-foot-level Performance Metric Reporting is Different and Better than Traditional Lean Six Sigma Process-output Response Reports
In Lean Six Sigma (LSS), the time-series, process-output tracking methodology typically utilized is a statistical process control (SPC) control chart. In an LSS project and elsewhere, when a control chart has not identified any out-of-control condition, a process capability statement can be determined, which states how the process is performing relative to customer specifications.
For a continuous process output response, process capability indices describe how a process is performing relative to specifications, for example, Cp, Cpk, Pp, and Ppk. For a process response that has an attribute pass/fail response, an estimated non-conformance rate determined from the control chart’s centerline describes process capability. Control charts and process capability reporting are two separate analyses and reports.
Traditional control charts used to assess whether a process has any out-of-control signals are dependent upon the selection process of samples from an operation. For example, someone may choose to select five daily samples and then create an and R control chart, while someone else may decide to randomly select only one daily sample and then create an XmR control chart. The reporting differences between these two charting options, relative to the identification of special-cause incidents, can be considerable because of the mathematics associated with calculating UCL and LCL values for each of the two situations (Reference Appendix B, Web page 11 of Leadership System 2.0: Implementing Integrated Enterprise Excellence.
The reason for this difference is that an individuals chart creates upper and lower control limits as a function of variation between subgroups. This UCL and LCL limit calculation input is different for X-bar and R charts, p-charts, and u-charts. For these three control charting methodologies, the variability between adjacent time-series intervals has no impact upon the calculation of UCL and LCL control chart limits.
The purpose of determining UCL and LCL control-chart plotting values is the separation of special-cause events from common-cause variation. For deciding which charting methodology to use, the question one needs to reflect upon is whether there should be consideration of the differences between adjacent time-series process response values as a source from common- or special-cause variation. A response to this question can differ depending upon whether the control chart is tracking an X-process-input response or a Y-process-output response.
Consider the tracking of hold time in a company’s call center for determining the most appropriate Y-process-output charting approach when deciding whether differing process response values are originating from common- or special-cause variation. For this situation, the question to address is: Should any difference between hold times as a function of time-of-day or day-of-week have consideration that these up-and-down values originated from common-cause or special-cause variation?
Since there will always be multiple times in a day and several days in a week, any variation from these weekly time sources should be considered a source of common-cause Y-response variation. For this 30,000-foot-level process-response perspective, an individuals chart of both a calculated mean and standard deviation of weekly hold time values would be most appropriate for determining whether special-cause occurrences were identified or not. When there are no identified special-cause conditions in these two individuals charts, the process response is considered stable and predictable.
Any important time-of-day and day-of-week determined differences should be addressed as X-input differences and managed accordingly to improve an overall Y-process-output hold-time response. An example of this X-management is adding more attendants during analytically-determined times-of-day or day-of-week where call-in demand is the highest.
Organizations need a business metrics reporting enhancement methodology. In the Integrated Enterprise Excellence (IEE) system, 30,000-foot-level reporting always utilizes the mathematics of an individuals chart to determine process stability for both continuous and attribute data. When not following this charting practice, many false special-cause signals can appear in a chart and then wrongfully reacted to because of common-cause time-of-day and/or day-of-week variations.
When a process is considered stable, the next question to address is how well the process is performing relative to customer specifications for a process response. A traditional Lean Six Sigma approach for providing a process-capability statement has many issues that a 30,000-foot-level report overcomes (Reference Appendix B, Web page 11 of Leadership System 2.0: Implementing Integrated Enterprise Excellence.
In 30,000-foot-level performance metric reporting, both process stability and capability information are reported – in one chart!
A free app is available to create 30,000-foot-level reports.
Origination of this Post’s Content
This post’s content was from Section 11.3 of the book Leadership System 2.0: Implementing Integrated Enterprise Excellence, which is available as an audiobook, in addition to paperback and ebook formats.
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