Performance Measurements

Organizations often use a performance metrics system that only provides point to point comparisons. For example, the currently monthly profitability of a business might be compared to last month. Any movement that is not desirable often leads to a search for what happened.

This what-happened investigation can result in the treatment of common cause variability as though it were special cause. Wasteful firefighting often is a result of traditional metric report-outs and nothing positive happens relative to making a metric improve over time.

A 30,000-foot-level predictive reporting methodology addresses this traditional metric reporting format shortcoming. With 30,000-foot-level metric reporting, a predictive statement assessment is made. If a process output performance is stable and the futuristic response is undesirable, this metric desirable shortcoming pulls for the creation of a process improvement effort that enhances the measurement’s response.

Enterprise Performance Reporting System (EPRS) software provides a means to easily create 30,000-foot-level report outs. A statistical shift in the time-series portion of a 30,000-foot-level metric is an indicator that the process output changed, either to the betterment or degradation. When this happens a new predictive process statement can then be made using this software.

Process Capability Analysis – A Better Way

Performance Measurements, Process Metrics /

For a given process, do you think everyone would create a similar looking control chart and make a comparable statement relative to its control and capability? Not necessarily. Process statements are not only a function of procedural characteristics and sampling chance differences but can also be very dependent upon sampling approach. The implication of this is that one person could describe a process as being out of control, which would lead to activities that immediately address process perturbations as abnormalities, while another person could describe the process as being in control. For this second interpretation, the perturbations are perceived as fluctuations typically expected within the process, where any long-lasting improvement effort involves looking at the whole process. During this session, issues with traditional control charting techniques (e.g., x-bar and R charts) and process capability indices statements (e.g., Cp, Cpk, Pp, and Ppk) will be discussed. An enhanced alternative predictive performance measurement system will then be described that not only provides resolution to these issues but can also provide a predictive statement, which everyone can understand.

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Process Behavior Charts for Non-normal Data: A 30,000-foot-level Perspective

30000-foot-level Predictive Metrics, Performance Measurements /

When non-normally distributed data are tracked over time at the 30,000-foot-level, process stability is to be assessed and a predictive statement provided, when appropriate. However, to make these assessments, a transformation that makes physical sense for this assessment may be needed. This article describes the use of an appropriate transformation from a physical point of view when deciding which actions or non-actions are most appropriate.

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Process Improvement Demonstration: Staged 30,000-Foot-Level Individuals Chart

30000-foot-level Predictive Metrics, Performance Measurements /

30,000-foot-level charting consists of two steps. The first step is to determine if the process has a recent region of stability, where this stability region could be six days, six weeks, six months, or six years. If the process does have a recent region of stability, the second step is to describe how the process is performing.

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Enhanced Attribute Data Control Chart with Process Capability Statement

Performance Measurements, Process Metrics /

Attribute, pass/fail proportion data, can be monitored over time for stability and then, when a process is stable, provide a prediction statement. When a process has a recent region of stability, it can also be said to be predictable. When this occurs, we can use historical data to make a statement about what we might expect in the future, assuming things stay the same; e.g., the center line of the chart if no transformations are needed to create the 30,000-foot-level chart, and the subgroup sizes are approximately the same.

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