Business Intelligence and Analytics

Business intelligence can be considered to be what’s and how’s of past occurrences, while business analytics assesses the why’s that involves causality investigation. The 9-step Integrated Enterprise Excellence (IEE) business management system provides a framework for integrating business intelligence with business analytics.

In step two of the 9-step IEE system, a value chain integrates statistical 30,000-foot-level predictive performance measures throughout an organization with their associated processes. An IEE value chain considers what’s and how’s (i.e., execution of processes) of past occurrences for this business intelligence creation.

Step three of the 9-step system, analyzes the business as a whole looking for the why’s of occurrences. When causality occurrences are uncovered, this information can lead to development of targeted improvement strategies (step 5) which leads to goal setting for functional 30,000-foot-level metrics (step 6) and then process improvement efforts (step 7), which benefit the business as a whole.

Design of Experiments Approach Example

Business Intelligence and Analytics, Design of Experiments /

In Design for Six Sigma (DFSS) and elsewhere, Design of Experiments (DOE) can be used in product design validation tests. In this example, Design of Experiment (DOE) techniques are used to validate a product design change in conjunction with an assessment of currently planned manufacturing tolerances. In this book excerpt, the reader is taken through a very robust sequence for the analysis of any DOE that provides a rapid and repeatable conclusion. This example includes a unique method to use probability plots to assess design limits.

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Monte Carlo Simulation Explained with Lean Six Sigma Application

Business Intelligence and Analytics, Statistical Analyses /

The stereotypical Lean Six Sigma DMAIC project has a nice process that produces data at a rapid enough rate that you can evaluate improvements during the project, but what options do you have if that is not the case? Using Monte-Carlo simulations can be a powerful way to estimate future performance without having to actually pilot test or prototype the improvements.

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