Predictive Analytics Scorecard On-line Training Agenda: 30,000-Foot-Level Reporting

Provided in this predictive analytics scorecard training agenda is the how-to’s for creating a predictive process output statement for stable processes —  in one chart.

This on-line course provides instruction on how to create the Integrated Enterprise Excellence (IEE) business management system 30,000-foot-level charts, which can offer a process futuristic statement.

IEE 30,000-foot-level metric reporting provides a means for organizations to “look out the windshield of their vehicle” as opposed to driving their car by only examining the “rear view mirror”.

 

predictive analytics scorecard training provides a futuristic statement
Predictive key performance indicators examples, making decisions by looking out the windshield of an automobile

 

When a what is futuristic statement is not desirable, an improvement effort is needed to enhance the process-output’s response.

 

On-line Predictive Analytics Scorecard Training Agenda: 30,000-Foot-Level Reporting


1. Statistics Defined

This topic covers a little background covering statistics and how we will be using them in the course


2. Understanding Variation

This topic introduces how the course will treat variation within data. Understanding how to handle variation and its patterns in a data set will be a real big take-away in this course.


3. Data Distributions

This topic covers the 5 common data distributions that will be used throughout the course. Understanding the distributions will benefit you in the later lessons.


4. Introduction to Minitab

This topic will provide a basic understanding of the Minitab Software which will be used extensively throughout the course


5. Individuals Control Chart

This topic introduces one of the two primary tools used in the 30,000-foot-level methods. The individuals chart is used to assess the stability or predictability of a process and to identify significant changes to the process over time.


6. Probability Plot

This topic introduces the second of the primary tools used in the 30,000-foot-level methods. The probability plot is used to evaluate data for a best fit distribution and to make capability estimates.


7. 30,000-foot-level introduction

This topic provides the WHY for the 30,000-foot-level methods and answers a number of questions that students may have about the method.


8. Normal Continuous data, no subgrouping

This is the first of the 30,000-foot-level lessons. This covers the specific methods that are applied to a data set of continuous data that is normally distributed and collected one point at a time.


9. Non-Normal Continuous data, no subgrouping

This topic is a small extension on the prior lesson, where we introduce data that is not normally distributed. Data transformations are introduced into this lesson.


10. Continuous data with subgrouping

This topic introduces the concept of treating data in small groups that have a similar characteristic such as batch or shift. When the data is grouped, there are many advantages.


11. Attribute Data (frequent occurrences)

This topic covers the most common attribute data found in business. Process data that includes count data where there a many events each period. This type of data is much easier to use the 30,000-foot-level method on.


12. Attribute Data (infrequent occurrences)

This topic covers the use of the 30,000-foot-level method to assess a type of data that there is no easy reporting method in the standard tool-set. This lesson discusses how to convert the count data into continuous data and then performing an assessment.


13. 30,000-foot-level method wrap up

Just what it says.

 

On-line Predictive Analytics Scorecard Training Agenda: Next Steps

For additional details about the course and its registration CLICK HERE

 

Contact Us to set up a time to discuss with Forrest Breyfogle how your organization might gain much from an Integrated Enterprise Excellence (IEE) Business Process Management System and its 30,000-foot-level predictive analytics scorecard training described methodology. 

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