Process Capability Analysis in Minitab – Tabtrainer®
What you'll learn
- Explain the concept of process capability and distinguish between overall and potential capability.
- Perform a complete process capability analysis for continuous, normally distributed data using Minitab®.
- Verify normality assumptions using the Anderson-Darling test and interpret p-values correctly.
- Construct and interpret X-bar and R control charts to assess process stability.
- Differentiate between standard deviation within and standard deviation overall, and understand their implications for capability.
- Calculate and interpret key capability indices including Cp, Cpk, Pp, Ppk, and Cpm.
- Assess the centering and variation of a process using visual tools such as histograms, probability plots, and dot plots.
- Identify and analyze causes of process shift and variation using practical tools like root cause analysis and supplier-based grouping.
- Apply improvement measures and validate their effectiveness through comparative capability analysis.
- Communicate capability results effectively to stakeholders and support continuous quality improvement initiatives.
Requirements
- Software Minitab
- No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.
Description
Welcome to the Tabtrainer® Certified Series – your expert platform for science-based quality training and applied statistical thinking.
In this course, you’ll gain deep, hands-on expertise in Process Capability Analysis using Minitab®, guided by real industrial data from the die casting of skateboard axles. You'll learn to evaluate and optimize your processes using industry-standard metrics such as Cp, Cpk, Pp, Ppk, and Cpm, while applying AIAG and ISO-compliant methods to ensure long-term process capability.
Led by Prof. Dr. Murat Mola, TÜV-certified Six Sigma expert and founder of Tabtrainer®, this course bridges theory and practice to help you identify process instability, reduce scrap, and communicate capability clearly to technical and non-technical stakeholders alike:
1. Data Preparation & Exploratory Analysis
How to import and clean quality data
Use of descriptive statistics to assess central tendency and dispersion
Identifying outliers and initial trends
Performing the Anderson-Darling test to verify normal distribution assumptions
2. Process Stability Analysis
Construction and interpretation of X-bar and R control charts
Application of all 8 AIAG control tests to identify process instabilities
Understanding subgroup structures and the difference between within-group and between-group variation
Verifying whether the process is stable enough to begin capability analysis
3. Capability Metrics and Their Interpretation
Introduction to key process capability indicators: Cp, Cpk, Pp, Ppk, and the Cpm (Taguchi Index)
Understanding the difference between overall and potential capability
Explanation of process centering and process shift (Katayori)
Visual interpretation through histograms, density functions, and z-benchmarks (Sigma Level)
Assessing scrap rates using PPM values (Parts Per Million)
4. Root Cause Analysis and Optimization
Root cause investigation using supplier-coded ID data
Creation of dot plots to compare raw material quality across suppliers
Implementing technical and supplier-related improvement actions
Evaluating before-and-after scenarios using the Capability Sixpack tool
5. Final Evaluation and Best Practices
Comparing pre- and post-optimization results based on capability indices and control charts
Interpretation of visual and statistical outputs to determine long-term capability
Guidance for saving, documenting, and communicating capability projects
Understanding when a process can be considered statistically capable, and how to sustain performance
Learning Outcome:
By the end of this course section, participants will be able to:
Apply the complete capability analysis cycle from start to finish
Use key performance indicators to assess and compare process capability
Identify causes of process instability and high variation
Implement targeted improvements to meet customer requirements
Use Minitab® tools efficiently, including control charts, capability plots, and the Capability Sixpack
This training empowers learners to make data-driven decisions, communicate process capability clearly, and support sustainable quality improvements in real industrial settings.
Who this course is for:
- Data Analysts, Six Sigma Belts, Minitab Process Optimizers, Minitab Users
- Quality Assurance Professionals: Those responsible for monitoring production processes and ensuring product quality will gain practical tools for defect analysis.
- Production Managers: Managers overseeing manufacturing operations will benefit from learning how to identify and address quality issues effectively.
- Six Sigma Practitioners: Professionals looking to enhance their expertise in statistical tools for process optimization and decision-making.
- Engineers and Analysts: Individuals in manufacturing or technical roles seeking to apply statistical methods to real-world challenges in production.
- Business Decision-Makers: Executives and leaders aiming to balance quality, cost, and efficiency in production through data-driven insights and strategies.
Instructor
- 4.9 Instructor Rating
- 9 Reviews
- 55 Students
- 33 Courses
Prof. Dr.-Ing. Murat Mola – Six Sigma Master Black Belt | Minitab® Expert | Professor of Operational Excellence
English Version
I am Professor of Operational Excellence and Materials Science at Ruhr West University of Applied Sciences, Germany, and founder of the learning platforms TABTRAINER® and SIXSIGMAPRO®. I specialize in Lean Six Sigma, statistical data analysis with Minitab®, process capability, and design of experiments (DOE) – all aligned with ISO 13053 and ISO 18404 standards.
Awarded the ThyssenKrupp Materials Innovation Prize
Named Professor of the Year 2023 in Engineering (Unicum Foundation)
Former Head of Central Quality Management at ThyssenKrupp
25+ years of industrial experience in process optimization and SPC
My journey began with vocational training as an industrial mechanic and led to a PhD in materials engineering. Today, I help professionals apply statistical thinking and data-driven methods to achieve real improvements.
What I Teach on Udemy:
- Six Sigma Green & Black Belt Certification
- Gage R&R and Measurement System Analysis (MSA)
- Process Capability Analysis (Cp, Cpk, Pp, Ppk)
- ANOVA, Regression, and DOE with Minitab®
- Control Charts and Statistical Process Control (SPC)
Let’s turn data into action – with real-world case studies and practical tools.
Deutsche Version
Ich bin Professor für Operational Excellence (OPEX) und Werkstoffwissenschaften an der Hochschule Ruhr West sowie Gründer der Plattformen TABTRAINER® und SIXSIGMAPRO®. Mein Fokus liegt auf Six Sigma, Minitab®, Qualitätsmanagement, statistischer Datenanalyse und Versuchsplanung (DOE) – nach ISO 13053 und ISO 18404.
Auszeichnung mit dem ThyssenKrupp Werkstoff-Innovationspreis
Gewählt zum Professor des Jahres 2023 – Ingenieurwissenschaften (Unicum Stiftung)
Ehemals Leiter des zentralen Qualitätsmanagements bei ThyssenKrupp
Über 25 Jahre Erfahrung in der Industrie, speziell in Prozessoptimierung und SPC
Ich startete mit einer Ausbildung zum Betriebsschlosser und promovierte später im Bereich Werkstofftechnik. Heute helfe ich Unternehmen, messbare Ergebnisse durch datenbasierte Methoden zu erzielen.
Was du in meinen Udemy-Kursen lernst:
- Six Sigma Green & Black Belt Ausbildung
- Messsystemanalyse (MSA) und Gage R&R
- Prozessfähigkeitsanalyse (Cp, Cpk, Pp, Ppk)
- ANOVA, Regressionsanalyse und DOE mit Minitab®
- Qualitätsregelkarten (SPC) und Prozessstabilität
Mein Ziel: Komplexe Methoden einfach erklären – für echte Erfolge in der Praxis.