Enhanced Predictive Analytics Techniques using 30,000-Foot-Level Reporting

Enhanced predictive analytics techniques are available through 30,000-foot-level reporting, which is a portion of the Integrated Enterprise Excellence (IEE) business management system.


Enhanced Predictive Analytics Techniques and 30,000-Foot-Level Reporting

30,000-foot-level charting provides a means to create a predictive measurement statement, which quantifies what internal or external customers of a process are experiencing over time; i.e., Voice of the Customer (VOC). The 30,000-foot-level control chart tracks the output of a process at a high level and is not intended to be used to determine if and when timely process input adjustments should be made.


The 30,000-foot-level metric reporting technique can provide predictive scorecards within an overall Integrated Enterprise Excellence (IEE) business management system. 30,000-foot-level metric1 reporting addresses issues with many traditional forms of performance reporting, as described in Performance Metric Reporting Issues: 30,000-foot-level Charting Resolution.


A 1-minute video description of this high-level metric reporting methodology and is  summarized by Forrest Breyfogle in “Introduction to 30,000-foot-level Reporting Concepts“.


predictive analytics techniques video


A 30,000-foot-level predictive analytics techniques metric might, for example, address the overall customer experience of time spent during checkout at a grocery store. A store would use a more frequent tracking and adjustment mechanism for adjusting associate checker coverage for natural peak-and-valley demand periods. How well this input adjustment is managed could dramatically impact both the customer experience and the company’s profitability. A 30,000-foot-level chart tracks the impact that this and other process inputs have on the response output.


For a 30,000-foot-level measurement reporting, it is not desirable to simply monitor data over some predetermined recent period of time; e.g., 3 months, 6 months, or 12 months. What is desired is to present time-series data in the report-out at least since the process’ last shift, which can extend for several years.


This assessment is made using an individuals control chart that has infrequent subgrouping and sampling as described in the article Control Charting Issues: Resolution using 30,000-foot-level Charts. With an infrequent subgrouping/sampling plan, the selection of a subgrouping interval for high-level control charts (e.g., 30,000-foot-level) is such that the typical variability from input variables that could affect the response will occur between these subgroupings.


For example, any differences between working shifts, raw material lots, departments, and/or machines that affect our output variable level would be considered originating from common-cause variability. This list of variables could lead us to a daily subgrouping interval, where the data within each subgroup interval would be a randomly-selected datum point or a compilation of data. A control chart strategy would then be created so that the magnitude of the between-subgroup variability affects the lower control limit (LCL) and upper control limit (UCL) calculations.


When 30,000-foot-level individuals chart has a recent region of stability using these predictive analytics techniques, one can state that the process is predictable. A prediction statement could be for the complete time period of the control chart or the last six weeks, if that is when a process shift was demonstrated.


If the process is predictable using these predictive analytics techniques, we can then make a process prediction statement. This statement will be made on the assumption that nothing changes either positively or negatively in the system. With 30,000-foot-level reporting, this prediction statement will be in a format that everyone can easily understand; i.e., proportion non-conformance or median response with 80% frequency of occurrence. We should note that if the prediction statement is not what we desire, we need to work at shifting the process to the better; e.g., by creating a Lean Six Sigma project. This strategy is referred to as a 30,000-foot-level metric pulling (using a Lean term) for process improvement or design project creation.


For more information about 30,000-foot-level reporting for predictive analytics techniques see:

Free 30,000-foot-level Reporting App

The following video provides an introduction to a no-charge app for creating 30,000-foot-level charts.



Application of Enhanced Predictive Analytics Techniques using 30,000-Foot-Level to Business Management Scorecards

For more information on how to apply 30,000-foot-level metric reporting to improve business scorecard reporting and metric improvement using an Integrated Enterprise Excellence (IEE) system, download the PDF article, “Positive Metric Poor Business Performance, How Does This Happen“.




  1. Forrest W. Breyfogle III, Integrated Enterprise Excellence Volume III – Improvement Project Execution: A Management and Black Belt Guide for Going Beyond Lean Six Sigma and the Balanced Scorecard, Bridgeway Books/Citius Publishing, 2008


predictive analytics techniques book


Contact Us to set up a time to discuss with Forrest Breyfogle how your organization might gain much from an Integrated Enterprise Excellence (IEE) and its predictive analytics techniques implementation.