Uncategorized

Individuals control charts can be used for time-series tracking of a process to determine if the process is in statistical control and can be considered stable. When a process is considered stable, it experiences only common-cause variability. When a process is not in control, special-cause conditions can be causing non-stability.

Attribute, pass/fail proportion data, can be monitored over time for stability and then, when a process is stable, provide a prediction statement.

Time-series data that have multiple subgroup samples can be monitored over time for stability and then, when a process is stable, provide a prediction statement.

When non-normally distributed data are tracked over time at the 30,000-foot-level, process stability is to be assessed and a predictive statement provided, when appropriate. However, to make these assessments, a transformation that makes physical sense for this assessment may be needed.

Described in this article, is a methodology for tracking a single-process output response over time to determine process stability and then, if the process is stable, provide a prediction statement.

Predictive measurement statements can be created using the methodologies described in 30,000-foot-level Reports with Predictive Measurements. Organizations can benefit from this enhanced measurement technique.

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.

Acceptable Quality Level (AQL), which provides a sample size to determine if a lot should be accepted or rejected, statistically does not protect customer. Sampling plans are typically determined from tables as a function of an AQL criterion and other characteristics of the lot.

Big data in information technology is a collection of very large, complicated data sets. With these datasets, it becomes difficult to process using traditional data processing applications. With this large amount of data, correlations can be found to identify business trends and other complex relationships.

Executive leadership benefits when it has a business management system that orchestrates predictive scorecards, analytically/innovatively determine strategies, and identification/execution of process improvement projects that benefit the enterprise as a whole. Current business practices have shortcomings relative to the integration of these methodologies.