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KPIs: Avoiding the Threshold McGuffins

Published
  • April 01 2005, 1:00am EST
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Completion of the key performance indicator (KPI) rationalization and definition phases allows us to focus on the next important step in KPI development - setting KPI targets and threshold levels. This first column will discuss how special and common causes contribute to process variation which, subsequently, impacts KPI metrics.

Once KPIs have been identified, defined and formulized, you may feel that the KPI battle is won. Please be advised that the arbitrary and inappropriate selection of KPI targets and thresholds can still doom a BPM implementation. When targets are set badly, people tend to achieve them by gaming on the numbers to ensure compliance and the associated bonus compensation. Gaming can be as subtle as setting easily achievable results or as blatant as definition reinterpretation. Where possible, KPI targets must be based on concrete data and non-manipulative formulas supported by a closed-loop data sourcing system.

The selection of appropriate threshold levels is also critical to the success of BPM implementations. Keep in mind that all the green, yellow and red stoplight and beacon visuals are only as effective as the threshold values driving them. Selection of thresholds that are too tight can cause an overreaction to KPI values that are within acceptable limits. Management will then spend more time reacting to inconsequential KPI metric changes that are actually within the normal variability for a given process. Alternatively, thresholds that are too broad will not be sensitive enough to detect significant KPI values that are outside acceptable limits. The bottom line is to avoid the McGuffins (i.e., those "misleads" often found in Alfred Hitchcock films).

We will select another statistical method - control charts - to facilitate development of KPI targets and thresholds. Control charts have been around since the 1930s and provide tools and methodologies that allow management to effectively track process trends and outliers via an assortment of x-bar, range and run charts. As a prelude to the design and construction of control charts, we will first discuss one of the key building blocks - variation. Variation, of course, is inherent in any process, and is also incorporated in the resultant KPIs that measure that process. The real challenge in variation analysis is to separate the common causes of variation from the special causes of variation. Common causes originate from the process itself and are repetitive from day to day. They are inherent in design, implementation and operation of process (e.g., poor design, inappropriate procedures, measurement error). Special causes are not part of the process itself and are sporadic in nature. Their occurrence is caused by outside forces (e.g., operator skill level, machine malfunction, raw material defects).

Figure 1, the KPI Variation Road Map, illustrates the relationship of KPIs to process, variation and source causes. From the diagram, it is clear that a change in the process is necessary to reduce common cause variation, while improved control of the existing process is the way to reduce special cause variation. Because the majority of causes are common causes (75 to 85 percent historically) and emanate from the process itself, constructing control charts will allow us to develop KPI targets and KPI thresholds that reflect past experience and historical data points. This is certainly a preferred alternative to "guesstimates." Next month, we will explore the steps required to implement KPI control charts.

Figure 1: KPI Variation Road Map

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