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Consulting Services

Let our experts help your team reach their goals. Experiment design, measurement system analysis, sample size calculations and Quality control plans are specialized tools that many organizations do not have access to. Save time and money with an experienced DATA consultant handling the data analytics decisions for your team.


How Consulting Works

DATA consultants are experienced problem solvers with expertise in new product development, inspection process improvement, defect reduction, supplier qualification, data automation, quality control, supplier qualification, measurement error reduction, reliability testing and more. We work with each client individually to determine your requirements for the type of support, schedule and pricing. Whether you are looking for 5 hours, 5 days or 5 months of support, contact us with the details and we will prepare a support plan for you.

For a Consulting Quotation for your work:

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Get Started With a Free 30-Minute Phone Consultation
Call (810) 357-1877.

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Sigma Aldrich

"Lou continues to lead our learning and understanding of design of experiments tools in order to achieve our research objectives. This methodology has helped us understand processes with many variables with a reduced number of trials. Lou’s insights during data analysis have also been very valuable. His guidance gives us confidence to use our experiment results for decision-making.”

Leidy Pena Duque | PhD  Sr. Scientist | Sigma-Aldrich Corporation, a subsidiary of Merck


To develop a schedule and quote for your organization:

Contact Us


Consulting Support Examples

  1. Capability Analysis Automation - Import, analyze, and export multiple data files from Excel to complete a Cpk time series dashboard.
  2. Acceptance Sampling Quality Control Plan - Identify cost savings with an Acceptance Sampling Quality Assurance Plan.
  3. Production Waste Stream Reduction - Reduce process loss with Multiple Linear Regression Analysis of historical data.
  4. Meeting EPA Emission Requirements - Achieve EPA emissions approval by identifying the root cause of pollutant emissions through Design of Experiments.
  5. Variability Reduction - Reduce variability by identifying key sources of variation with an Expanded Gage R&R for destructive measurements.
  6. Process Defect Reduction - Use Fractional Factorial Experimentation to minimize defects.
  7. Product Time to Failure Determination - Determine the best supplier by analyzing the reliability of sample parts.


Case Studies

Design of Experiments

Design of Experiments

Acceptance Sampling

Acceptance Sampling

Multiple Linear Regression

Multiple Linear Regression

Data Automation

Data Automation