Data analysis is used in the 9-step Integrated Enterprise Excellence (IEE) business management system when evaluating an organization as a whole to determine what areas of the business should be further investigated for improvement opportunities (step 4). Data analysis is also used in step 7 of the IEE system when investigating what might be done differently to improvement a particular process’ output response.
Data analysis that attempts to uncovering causal relations to a response can involve hypothesis tests that are to assess what is impacting an IEE 30,000-foot-level process-output predictive response. Statistical tools such as linear regression and analysis of variance can be involved in this assessment of historical data. Design of Experiments (DOE) can also be used to statistically determine through experimentation what factors and levels of factors might improve a desired response.
What is Quality? Sometimes this question is respect to a Lean Six Sigma topic to improve quality and in other cases it might be about how to define quality with respect to metrics and dashboards. One perspective is whether this “What is Quality? question is from an internal or external view of quality.
Different views of Quality: Internal vs. Consumer Read More »
A list of the important skills for data scientists needs to include a structured approach for incorporating business systems thinking in organizations. This need is addressed through the Integrated Enterprise Excellence (IEE) Business Management System.
Important Skills for Data Scientist need to Include Business Systems Thinking Read More »
Join Rick in this webinar, where he will share lessons learned from years of analysis and coaching improvement projects. We will discuss the different considerations involved in the collection of representative data to make a decision, whether it is for a LSS project, a process control initiative, or the creation of enterprise performance scorecards. The goal is to effectively collect the smallest amount of data to make the decisions that are needed. This webinar will provide you with some of the rules and guidelines that will allow you to collect data on a process in an optimal method for the purpose you need. It is not true that more data is always good. You will learn how different sample periods and different sampling plans will be able to improve your ability to support a decision with a minimal amount of data. The topics covered will benefit practitioners in all environments, from large automatically collected data sets, to ones where you must manually collect any data you will need.
Data Collection Concepts for Projects, Process Control, and Scorecards Read More »
The Wall Street Journal had an article this weekend titled “When Risk is a Red-Meat Issue” by Carl Bialik on March 23, 2012. This may be behind the WSJ pay wall, but if you can read it, please do. The article starts with a study that concluded that eating one serving of red meat per
Transformation of process data to achieve normality seems like magic, but it is not. There are a lot of reasons that specific transformations make sense and should be used, such as a lognormal transformation of standard deviations and of time data. But, when it comes to the Weibull distribution, there is no logical or inferential information that provides any guidance on a transformation to normality, until now. Rick Haynes has found a relationship between the Weibull distribution parameters and the optimal Box-Cox transformation lambda value. Through the use of the @Risk simulation program and Minitab, this article walks through the generation of an equation to predict a lambda value.
Weibull Distribution Example: A Transformation with Surprising Result Read More »
The Lean Six Sigma toolset is not very efficient in addressing root cause identification for an single event (special-cause event) or problem, it works best to address chronic (common-cause problem) issues. That should not imply that there are not Lean Six Sigma tools that can be used to support a root cause analysis or corrective action effort. In this webinar we will show you a set of Lean Six Sigma tools that can be applied to these efforts, which may seem familiar to an RCA practitioner but they have different names in Lean Six Sigma. We will work through a general corrective action event and show how the Lean Six Sigma practitioner can use their skills to quickly determine the root causes in a method that everyone will understand. Take-aways: – Identify the Lean Six Sigma DMAIC tools that are beneficial in a RCA; – Describe how the use of the DMAIC tools overcome one of the great weaknesses in RCA.
These methods as discussed and shown in real life examples are commonly not utilized by Master Black Belts and Black Belts; however, in certain situations these methods may be your only option. VIEW NOW, and add Censored Data Analysis to your tool box!
Benefiting from a Censored Data Statistical Model Read More »
Described in the article below is a cycle time analysis example that addresses a process with the backlog of work.
Cycle Time Analysis Example: Processes with a Backlog of Work Read More »
A Minitab statistical analysis software help resource is described in the link below, which can be used in the 9-step Integrated Enterprise Excellence (IEE) Business Management System Analyze Phase.
Minitab Statistical Analysis Software Help Resource Read More »
A quick method for changing the time scale in Minitab is described in the attachment, which can be used for predictive analytics 30,000-foot-level charting.
Changing The Time Scale In Minitab for a 30,000-foot-level Metric Chart Read More »
