search
cart
facebook twitter linkedin youtube
  • Sign In
  • Create Account
  • Sign Out
  • My Account
  • HOME
  • PROCESS CONTROL
  • QUALITY ANALYTICS
  • MONTE CARLO SIMULATION
  • IDEA MANAGEMENT & INNOVATION for OpEx
  • Quality Home
Next Generation SPC & Quality AnalyticsSoftwareNext Gen Quality Analytics

How Quality Engineers Use Data to Address Root Causes in Manufacturing, Part 2

A worker is using tablet to review storage report.

Image Source: Thank you for your assistant / iStock / Getty Images Plus via Getty Images

April 3, 2025

Last month, we explored in How Quality Engineers Use Data to Address Root Causes in Manufacturing, Part 1 how quality engineers identify patterns through control charts, capability analysis and Pareto charts. Part Two examines how they connect these patterns to underlying causes.

They Structure Problem-Solving with Root Cause Analysis 

Structured problem-solving tools like fishbone diagrams and 5-Why analysis help organize information from multiple sources. Quality engineers use these methods to:

  • Examine problems from multiple perspectives (machine, method, material, measurement)
  • Move past immediate symptoms to underlying causes
  • Document the logical progression from effect to cause
  • Involve knowledge from various departments

For example, when products fail in the field, these tools help trace issues back through assembly, component manufacturing, design, and material selection to find the true origin of the failure.

They test cause-effect relationships with designed experiments

Design of Experiments (DOE) methodically tests which factors affect outcomes. Quality engineers employ this approach to:

  • Test multiple factors simultaneously
  • Quantify the impact of each variable on quality
  • Identify interactions between different factors
  • Replace opinions with evidence-based conclusions

When teams disagree about what causes quality problems, DOE provides objective evidence about which factors actually matter, preventing cycles of trial-and-error fixes.

They model relationships with statistical regression

Regression analysis establishes mathematical relationships between process inputs and outputs. Quality engineers apply these models to:

  • Predict how changes in process settings will affect quality
  • Distinguish correlation from causation
  • Optimize process parameters for best results
  • Account for multiple variables simultaneously

By modeling relationships mathematically, teams can target the specific process parameters that most directly influence product quality.

They connecting data to root causes

The core value of quality engineering lies in connecting manufacturing data to the fundamental causes of problems. This systematic approach:

  • Prevents repeated occurrences of the same issues
  • Moves beyond temporary fixes to permanent solutions
  • Helps teams distinguish between symptoms and causes
  • Builds process knowledge that improves future designs

In organizations that successfully implement Six Sigma and Lean Manufacturing principles, quality engineers provide the analytical bridge between data collection and effective action. Their work turns the principle of addressing root causes into practical reality on the factory floor.

KEYWORDS: analytics data analytics manufacturing next generation software

Share This Story

Looking for a reprint of this article?
From high-res PDFs to custom plaques, order your copy today!

Recommended Content

JOIN TODAY
to unlock your recommendations.

Already have an account? Sign In

  • 2024 Quality Rookie of the Year Justin Wise 1440x750px banner with "Quality Rookie of the Year" logo inset

    Meet the 2024 Quality Rookie of the Year: Justin Wise

    Justin Wise is an exceptional individual who has been...
    Aerospace
    By: Michelle Bangert
  • Man with umbrella and coat stands outside while it rains at night looking at a building.

    Nondestructive Testing: Is there an ethics problem?

    I was a whistleblower who exposed fraudulent activities...
    NDT
    By: Dale Norwood
  • Unraveling Deflategate: Football stadium with closeup of football on field

    Unraveling the Tom Brady Deflategate

    The Deflategate scandal erupted following the 2014 AFC...
    Measurement
    By: Greg Cenker and Henry Zumbrun

Running a Monte Carlo Simulation in Minitab

Monte Carlo simulations help forecast possible outcomes and probabilities, while saving on time and resources spent running physical tests. Learn how easy it is to run a Monte Carlo simulation with Minitab.

MiniTab Whitepaper download

×

Stay in the know with Quality’s comprehensive coverage of
the manufacturing and metrology industries.

eNewsletter | Website | eMagazine

JOIN TODAY!
  • RESOURCES
    • Advertise
    • Contact Us
    • Directories
    • Store
    • Want More
  • SIGN UP TODAY
    • Create Account
    • eMagazine
    • eNewsletter
    • Customer Service
    • Manage Preferences
  • SERVICES
    • Marketing Services
    • Market Research
    • Reprints
    • List Rental
    • Survey/Respondent Access
  • STAY CONNECTED
    • LinkedIn
    • Facebook
    • YouTube
    • X (Twitter)
  • PRIVACY
    • PRIVACY POLICY
    • TERMS & CONDITIONS
    • DO NOT SELL MY PERSONAL INFORMATION
    • PRIVACY REQUEST
    • ACCESSIBILITY

Copyright ©2025. All Rights Reserved BNP Media.

Design, CMS, Hosting & Web Development :: ePublishing