Organizations benefit when they use performance metrics software that can provide a predictive statement. Enterprise Performance Reporting System (EPRS) measurement software provides a means to accomplish this objective.
Predictive Performance Metrics Software Overview
Businesses can gain much when they use performance metrics software that:
- Reports performance measurements from a process-output perspective.
- Includes process variability in performance-metric report-outs, unlike red-yellow-green scorecards and a table-of-numbers.
- Provides a predictive statement, when a process is considered statistically stable. For a stable process, EPRS metrics software will provide a bottom-of-the-report-out statement, where this reporting provides an approximated non-conformance rate or expected mean/median response and 80% frequency of occurrence rate, when no specification exists.
- Delivers insight, when a futuristic statement is undesirable; i.e., an underlying process needs to be enhanced for the performance metric to be better.
- Offers a statistical visualization that shows when and how much a process change impacts a performance measurement response.
EPRS Predictive Performance Metrics Software
Process output data can have differing response types; e.g., continuous and attribute response. Also, for continuous response data, some situations need data to be in subgroups, while other situations do not. Performance metrics software needs to offer flexibility for handling various types of process-output responses.
The software that is to create and track predictive performance metrics need to offer high-level process-output data reporting from a 30,000-foot-level (operational) or satellite-level (financial) perspective. EPRS software provides this type of metric-response reporting. EPRS metric reporting format should not be confused with statistical process control (SPC) charting, which, unlike EPRS metric software, is to ″control″ processes.
EPRS 30,000-foot-level and satellite-level reporting format addresses various time-series process-output responses that can be encountered:
- Attribute
- Continuous response no-subgrouping
- Continuous response subgrouping
How to Downloading EPRS Software for High-level Process Metric Response Reporting
Example: EPRS Software Input and Created Output for Attribute Data
An EPRS Performance Metrics Software input screen shot for attribute process-output response data is the following, where column 2 (c2) contains the observed failure rate for a time-series of months, column 1 (c1):
For this EPRS Performance Metrics Software input, the created attribute process-output 30,000-foot-level response is shown in the following figure.
This process is considered stable since there were no data points beyond the UCL and LCL limits. Because of this stability determination, the up-and-down individual data point differences is considered a source of common-cause variability or noise for the output response; hence, a predictive statement is provided at the bottom of the report-out; i.e., an estimated 0.179 non-conformance rate. If this futuristic estimation is undesirable, the underlying process that impacts this metric need to be enhanced.
Example: EPRS Software Input and Created Output for Continuous Response of Individual Time-series Data
The next figure is an EPRS Performance Metrics Software input screen shot for creating a 30,000-foot-level report-out of continuous process individual data response values. This figure also illustrates how to stage the chart’s response at 11/01/2016; i.e., the point that a process-improvement response was made.
EPRS Performance Metrics Software provided the following 30,000-foot-level report out. A predictive statement is provided at the bottom of the report-out for this stable process response.
The staged individuals chart on the left indicates that an improvement was made. The probability plot on the right was created using the data from the recent region of stability to provide a best-estimate, bottom-of-the-report-out prediction statement.
Since there are no specifications for this process output response, an estimated mean and 80% frequency of occurrence is reported from the probability plotted data. If there were a specification, an estimated non-conformance rate would be shown at the bottom of the report-out.
Example: EPRS Software Input and Created Output for Continuous Subgrouped Data
An EPRS Performance Metrics Software input screen shot for continuous process-output data, where there are multiple subgroup responses is:
The circled area of the screen shot above shows that the dataset has seven value for each time-series subgroup date (column 1). For the creation of this dataset, one situational data point was randomly selected for each day of the seven-day subgrouping increments. The reason for selecting a weekly subgroup is that the hospital-patient ″length of stay″ response was thought to differ by day of the week when a person was discharged from the hospital at checkout and was a source of common-cause variability.
For the EPRS Performance Metrics Software input, the continuous process-output 30,000-foot-level response for the series of subgroup-values is shown in the following figure.
The individuals chart of the mean and standard deviation responses in this report-out indicates process stability, since no datum point was beyond the UCL and LCL limits. Because of this stability determination, a prediction statement is provided by the EPRS performance metrics software at the bottom of the report-out.
Data for the probability plot creation, from which the prediction rate is determined, are all the raw data from this recent region of stability.
Since there are no specifications, an estimated mean and 80% frequency of occurrence is reported; i.e., an estimated median length of stay of 109 hours where 80% of hospital stays are estimiated to be between 77 and 141 hours. If there were a specification, an estimated non-conformance rate would be shown at the bottom of the report-out instead of an 80% frequency of occurrence.
Additional Information
Integrated Enterprise Excellence (IEE) provides a means to systematically integrate an organization’s 30,000-foot-level metrics with the processes that created them and more.
Contact Us to set up a time for a discussion on how your organization might gain much from EPRS Predictive Performance Metric Software and/or an Integrated Enterprise Excellence Business Management system.