Key Performance Indicators KPI Dashboard Examples

A performance management best practice is the use of predictive KPI dashboard reporting. The following six examples illustrate the benefit of this performance management best practice approach for predictive KPI dashboard reporting.

Businesses can benefit from this structured approach to reporting their performance measurements as part of implementing an Operational Excellence system. The described techniques address the metric reporting needs described in a 1-minute business-needs video.

Key Performance Indicators KPI Dashboard Examples

A key performance indicator for a company had the following KPI dashboard

 

Key performance indicators KPI dashboard examples

Key performance indicators KPI dashboard examples

 

When examining this common approach to reporting KPI metrics within a KPI performance system, the following questions come to find:

  • Why not examine the metrics over a longer period of time, which can provide some additional information for KPI analytics?
  • Have there been any statistical changes in the KPI metrics response over time?
  • What behaviors relative to actions or non-actions would this form of report out for KPI performance management provide?

Let’s next individually examine each of these KPI reports.

KPI reporting example Report 1

An enlargement of the time series plot from the original KPI reporting for report 1 is:

 

Traditional KPI reporting example 1

Traditional KPI reporting example 1

 

When one looks at this KPI metric reporting, which is one of this article’s six described key performance indicators KPI dashboard examples, what action should be taken or not taken? One might be to set a goal and get the process owner to react whenever an individual point does not meet expectation; however, this can cause problem, as described in Stoplight Scorecards Issues and Resolution.

A better approach would be to track the metric using an Integrated Enterprise Excellence (IEE) 30,000-foot-level KPI performance indicator tracking methodology. This form of predictive KPI metric system reporting for this situation would be:

 

IEE 30,000-foot-level KPI dashboard reporting example 1

IEE 30,000-foot-level KPI dashboard reporting example 1

 

The above 30,000-foot-level control chart on the left assesses whether the process is stable. For individual control charts, the upper control limit (UCL) and lower control limit (LCL) are mathematically determined from between subgroup variability. For this KPI reporting, the process is not considered stable because of the one point that is beyond the control limits.

As part of a KPI performance management system, the reason for this special cause point beyond the limit could be investigated for causal reasons. One should not try to understand the reasons for each of the other common cause data point fluctuations; i.e., individual data points within the control limits. The process is not considered predictable for this situation since there was a data point beyond the upper or lower control limit.

Notice how the reaction to a 30,000-foot-level KPI reporting is quite different from the earlier described traditional KPI management performance metric reporting. A fifteen page peer-review article about 30,000-foot-level performance reporting describes the enhancement of this methodology to traditional control charting methods.

KPI Example Report 2

The original KPI reporting graphic enlargement for report 2 of the six key performance indicators KPI dashboard examples is

 

Traditional KPI reporting example 2

Traditional KPI reporting example 2

 

What conclusions might one make from this KPI reporting example response? It is difficult to say. Let’s now examine the data set using a 30,000-foot-level key performance indicator report.

 

IEE 30,000-foot-level KPI dashboard reporting example 2

IEE 30,000-foot-level KPI dashboard reporting example 2

 

From the individuals plot on the left, there is not enough information to state that the process is not stable. Because of this one presumes the process is stable. When this form of KPI metrics reporting has a recent region of stability, we can state that the process is predictable. The next obvious question is: what do we predict?

To answer this KPI metric prediction question, one considers that the data from the recent region of stability is a random sample of the future. The probability plot on the right side of the above IEE KPI dashboard report out provides a best estimate prediction statement.

For this predictive KPI reporting aspect, with an IEE report-out one would make a median and 80% frequency of occurrence statement from the graph’s information. With 30,000-foot-level metric reporting, a statement would also be made at the bottom of the pair of graphs about the process’ performance.

If this common cause process response is undesirable, improvement efforts are required to the process. Proof that the later improvement effort was effective is that the 30,000-foot-level report transitioned to a new, improved level of stability.

Predictive 30,000-foot-level reporting throughout the organization can help a company move toward achievement of the 3Rs of business; i.e., everyone doing the Right things, and doing them Right, at the Right time.

KPI Example Report 3

Enlarging the original key process indicator reporting graphic for report 3, of the six key performance indicators KPI dashboard examples that have been presented, result in

 

Traditional KPI reporting example 3

Traditional KPI reporting example 3

 

It appears that the process changed but we really don’t have any statistical evidence to support this theory. Also, how is the process performing now relative to what is desired? This type of KPI reporting does not provide this level of insight. The following 30,000-foot-level predictable KPI metrics report out addresses these shortcomings by providing the potential KPI analytics that is basically included in the report-out.

 

IEE 30,000-foot-level KPI dashboard reporting example 3

IEE 30,000-foot-level KPI dashboard reporting example 3

 

From this report out, one concludes that the process changed and now has the stated, predictive level of performance. Again, if the current level of performance is not satisfactory, this KPI improvement need would be pulling for the creation of a process improvement effort. This process enhancement effort might be a Lean Six Sigma project, kaizen event, just to it effort, or some other approach. The owner of the process would be anxious for the project work to be completed since he/she understands that this work is important to improve their metric. This urgency is underscored if the metric needs to be improved from a strategic point of view.

When undertaking a process improvement effort, someone could create hypotheses that they statistically evaluate. Data from the recent region of stability could be used to test theories. For example, are their statistical differences between people, machines, time of day, day of the week, and so on? Information from hypotheses tests can show significance, which is often useful to determine where improvement efforts should be given focus.

KPI Example Report 4

An enlargement of the key process indicator report 4 that was shown initially as part of the six key performance indicators KPI dashboard examples is

 

Traditional KPI reporting example 4

Traditional KPI reporting example 4

What does this KPI reporting indicate? Is there a downward trend? One person might say there is a decline in the response, while another person might disagree. Should any action be taken, or should there be no action at this point in time? It is hard to say. The following 30,000-foot-level KPI metric report clarifies this decision making process.

 

IEE 30,000-foot-level KPI dashboard reporting example 4

IEE 30,000-foot-level KPI dashboard reporting example 4

 

From a 30,000-foot-level KPI reporting, we currently do not have enough information to indicate that the process has changed to a lower level of performance. Because of this, at this point in time one would consider that the process is stable. If the reported predictable level of performance is not satisfactory, the process will need to be given process improvement efforts.

KPI Example Report 5

An enlargement of the report 5 presentation of one of the six key performance indicators KPI dashboard examples original displayed is

 

Traditional KPI reporting example 5

Traditional KPI reporting example 5

 

This KPI report out has similar issues to the previous KPI dashboard reports. What should be done, if anything, about this KPI? Is there improvement needs? What is the plot telling us? When answering this question, there can be very differing opinions from what one person thinks to another.

An IEE 30,000-foot-level metric KPI report out takes away this uncertainty, as shown below.

 

IEE 30,000-foot-level KPI dashboard reporting example 5

IEE 30,000-foot-level KPI dashboard reporting example 5

 

This report out indicates process stability and predictability; however, there seems to be two distributions. Notice the knee in the probability plot, which indicates a bi-modal distribution. No specification was given for this process response; hence, one would not be sure if any process improvement effort should be undertaken to improve this KPI or not.

If one were to do some KPI analytics for this metric, one of the first things that should be done is to try to determine what input condition changed for the two apparent distributions. Maybe one distribution was from one machine or department and the other from another machine or department. When this type of process understanding is gained, improvement efforts are enhanced.

KPI Example Report 6

Below is report 6 of the six key performance indicators KPI dashboard examples presented, enlarged from the original presentation.

Traditional KPI reporting example 6

Traditional KPI reporting example 6

From this report out one might conclude that the process response has moved to a new lower level of performance. However, can be sure that is change has occurred? No. What is needed is a statistical assessment.

However, an examination of the following IEE 30,000-foot-level reporting indicates that there is not enough current information to conclude that the lower response has just occurred by chance from normal common-cause variability.

 

IEE 30,000-foot-level KPI dashboard reporting example 6

IEE 30,000-foot-level KPI dashboard reporting example 6

From this plot even though we could not identify a special cause condition, we still may have an undesirable common-cause response that might should to be address through a process improvement effort.

For all of the above KPI reports, if we had a specification, we could have then estimated the percentage of the time we could expect that the process had a non-conforming condition.

Additional dashboard conversion examples to predictive performance reporting are available in the article Dashboard Transitions to Predictive Metric Reporting.

KPI Performance Management

The next question that one might ask is how could one apply the above six key performance indicators KPI dashboard examples reporting in a business?

A Key Performance Indicator article shows how to address this desire. This article provides insight into an Integrated Enterprise Excellence (IEE) performance management best practice for

  • KPI selection
  • KPI dashboard creation
  • Tracking of KPIs
  • Software for key process indicator tracking
  • Analytics for improvement of performance indicators

There is also a six-book set that describes aspects of the Integrated Enterprise Excellence (IEE) system and its benefits.

Implementing Operational Excellence and Next Steps

Wikipedia states that Operational Excellence should align performance scorecard needs with improvement efforts.  The described performance reporting methodology provides a KPI report-out methodology that benefits organizations in the implementation of Operational Excellence.

If the above IEE KPI metric reporting system is something that you believe could be beneficial to your organization, contact us so that we can work together to see whether (and how) these techniques might benefit your business/function.  Contract information: +1 512.918-0280 or info@smartersolutions.com.

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