Forrest Breyfogle

30,000-foot-level Full of Problems OR Paradigm Shift?

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 »

Control Chart for Non-Normal Data Example: Log-normal Distribution with Negative Values

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.

Enhanced Control Chart for Count Data with Prediction Process Capability Statement

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.

Using Six Sigma to Lower Medical Costs, while Improving Patient Service and Outcomes

CIGNA’s Leslie Benke and Smarter Solutions’ Forrest Breyfogle team up in discussion of using Six Sigma to Lower Medical Costs, while Improving Patient Service and Outcomes at the ASQ World Conference in May 2005. In regards to Six Sigma helping to change paradigms, they find that lower medical costs do not have to equate to lower quality outcomes. In addition, CIGNA focuses on its 3-tier approach to Six Sigma in proving the cross-divisional/business paradigm. Both find that Six Sigma data analysis tool provide new insights into medical services utilization, costs, and proactive solutions for improved clinical outcomes, leveraging benefits and resulting in more robust solutions.

Enhanced Approach for Lean Six Sigma in Healthcare Insurance Industry

While a great deal of process improvement work has finally begun to take hold within healthcare around patient safety and hospital operational efficiencies, what about the other non-clinical aspects of the overall healthcare system which can consume physician resources and adversely impact hospital finances? Healthcare insurance processes can have a major impact on both patient care as well as operational efficiencies. This paper describes how one healthcare insurance company has begun to use Six Sigma to not only make itself more efficient and effective internally, but is now also directing its energies toward lowering medical costs while improving patient service and outcomes