An enhanced business performance metrics management system is available through the Integrated Enterprise Excellence (IEE) business management system.
Enhanced Business Performance Metrics Management Read More »
Traditional management metrics often include tabular reporting and perhaps classic trend and bar charts. Management can also use a scorecard system to monitor and track both financial and non-financial areas of the business against measurement goals established for each of these metrics. With this scorecard approach, metric owners can be tracked against and are responsible for achieving the goals established for their respectively balanced scorecard metrics; i.e., financial, customer, internal business, and innovation and learning perspectives. Benefits can be achieved from these traditional performance measurement systems; however, if care is not exercised, many of these systems can lead to the wrong business activities and the sub-optimization of processes. However, In Integrated Enterprise Excellence (IEE) system cascading measurements can be created, which aligns metrics to the overall needs of the organization. The tracking of these measurements over time can then pull (using a Lean term) for the creation of IEE projects, which addresses common cause variability improvement needs for the process output.
One of these enhanced Design for Six Sigma examples illustrates an approach for conducting product mean time between failures (MTBF) in development.
A criticism of the Six Sigma approach was presented in the “Wall Street Journal and Chief Executive Magazine”. The article below rebuts the critic’s dubious statistics.
I am writing this response as author of the <ahref=”https://www.smartersolutions.com/articles.htm”>November 2006 3.4 per million article, which generated QP Mailbag feedback in the January and February issues of Quality Progress. My response to Tim Folkerts’ comments in the January 2007 issue had been given in the discussion board. In this response I stated: The point relative
30,000-foot-level Full of Problems OR Paradigm Shift? Read More »
This paper addresses 3-parameter log-normal distribution control charting. Statistical Process Control (SPC) has a primary purpose, which is to identify when special cause conditions occur for timely corrective actions. SPC textbooks and training state that an individuals control chart (X) or an individuals and moving range chart (XmR) control chart is appropriate and should be used when tracking individuals data. With an individuals chart, between subgroup variability affects the magnitude of the control limits from the overall response mean. This paper illustrates how the individuals control chart is not robust to non-normality. When data are not normally distributed (e.g., when there is a boundary condition) the data need a normalizing transformation that is appropriate for the process being evaluated. The log-normal distribution often is a good fit for these situations. However, the log-normal distribution cannot accept negative numbers, which are often needed to describe this type of process response. This paper describes the use of the 3-parameter log-normal distribution in 30,000-foot-level control charting. In this 30,000-foot-level metric reporting methodology, process response is evaluated for regions of stability. Within identified stable regions, a process capability non-conformance estimate can then be reported if a specification exists. If there is a recent region of stability, one can consider the data in this region to be a random sample of the future; hence, a prediction statement can be made. An enterprise can assess its value-chain metrics collectively – where each has 30,000-foot-level reporting – to determine where improvements can be made that positively impact the enterprise financials as a whole. Goals to these metrics would pull for a process improvement or design project creation that positively impacts these 30,000-foot-level metrics.
Control Chart for Non-Normal Data Example: Log-normal Distribution with Negative Values Read More »
These described enhancements to courses in lean Six Sigma address the lack of sustainability that often occurs with a traditional lean Six Sigma deployment.
This PDF article provides a predictive modeling example for assessing the stability of a process and then provide a prediction statement for non-normal response data.
This paper addresses infrequent failure data control charting. A primary purpose of Statistical Process Control (SPC) is to identify when special cause conditions occur for timely corrective actions. SPC textbooks and training state that a c-chart or u-chart should be used for count data tracking. With a c-chart, the tracked response is the number of counts, which has time-series identified rational sub-groups. A u-chart has a similar tracking but only occurrence rate is track. Described in this paper are technical issues that make the c-chart and u-chart ineffective, especially when the counts (e.g., safety incidents) are very low for the subgrouping (e.g., months). The technical reason for this occurrence is discussed along with an alternative 30,000-foot-level reporting system that addresses these issues. Traditional c-chart and u-chart control charts can lead to inappropriate activities, since the underlying assumptions for these charts are often not valid in the real world. In the 30,000-foot-level metric reporting methodology, which centers on use of the individuals control chart, process response is evaluated for regions of stability, where time between incidents is a response that can be tracked for adding power to the test when there are infrequent count occurrences. Within identified stable regions, a process capability estimate can then be reported. If there is a recent region of stability, one can consider the data in this region to be a random sample of the future; hence, a prediction statement can be made. An enterprise can assess its value-chain metrics collectively – where each has 30,000-foot-level reporting – to determine where improvements can be made that positively impact the enterprise financials as a whole. Goals to these metrics would pull for a process improvement or design project creation that positively impacts these 30,000-foot-level metrics.
Enhanced Control Chart for Count Data with Prediction Process Capability Statement Read More »
This article provides a Cigna Insurance Company leadership excellence example for implementing lean Six Sigma in an organization.
