Overview

Welcome to this advanced training from the Tabtrainer® Series - a recognized learning platform for high-impact statistical training in industry and academia.This course is developed and taught by Prof. Dr. Murat Mola, founder of Tabtrainer®, certified by TÜV and awarded "Professor of the Year 2023" in Germany. Tabtrainer® courses are known for bridging the gap between theory and industrial application - with clarity, precision, and actionable outcomes.What This Course CoversThis comprehensive training course provides a deep, practice-driven introduction to Statistical Process Control (SPC) using attribute control charts in Minitab. It is based on two detailed real-world scenarios from the final assembly process of skateboards at Smartboard Company. The training focuses on understanding, selecting, applying, interpreting, and differentiating the most relevant SPC tools for attribute data: P charts, NP charts, Laney P′ charts, U charts, C charts, and Laney U′ charts.Participants learn not only the technical application of each control chart but also the underlying statistical distributions (binomial and Poisson), diagnostics, interpretation of process instabilities, and the impact of subgroup structure and process changes on control chart accuracy.Module 1: Monitoring Defective Units (Binomial Distribution)In the first part of the course, you will work with a dataset that reflects the number of defective skateboards identified during final surface inspection. The analysis focuses on:P Chart - to monitor the proportion of defective products across subgroups of varying size.NP Chart - to evaluate the number of defectives in subgroups of constant size.Laney P′ Chart - a modified version of the P chart that adjusts for overdispersion or underdispersion, providing more reliable control limits.Key learning points include:How to diagnose binomial suitability using probability plots.When to apply the Laney P′ chart to avoid false alarms or missed process shifts.How to detect special cause variation using built-in Western Electric control tests.How to interpret control chart results in the context of real production shifts and inspection quality.Module 2: Monitoring Defect Counts (Poisson Distribution)In the second part, you transition from the classification of defective units to analyzing the number of defects per product-such as surface scratches detected per skateboard. This requires a different statistical approach based on Poisson distribution and the use of:U Chart - for tracking the defects per unit, especially when subgroup sizes vary.C Chart - for analyzing total defect counts in subgroups of constant size.Laney U′ Chart - a dispersion-adjusted U chart used when Poisson assumptions are not fully met.This module also introduces advanced techniques such as:Running a U chart diagnostic to check Poisson distributional fit.Manual calculation of control limits based on AIAG formulas.Understanding and applying the "Stages" function in Minitab to split charts before and after process improvements.Visual comparison of U chart vs. C chart when working with the same data under different conditions.Learners explore how mixing data from two different process phases in a single chart leads to distorted control limits, and how correct segmentation enables meaningful interpretation and true process insight.By the End of the Course, You Will Be Able To:Select the appropriate attribute control chart based on defect type, data structure, and distribution.Understand the difference between binomially and Poisson-distributed quality data.Perform diagnostics and validate data suitability for P, NP, U, or C charts.Interpret agreement rates and confidence limits in probability plots.Use Laney charts to correct overdispersed or underdispersed data and avoid misinterpretation.Apply control tests to detect assignable causes and process instability.Split your analysis into pre- and post-improvement process phases using stage control.Manually calculate control limits to validate software-generated results.Present and document your findings in a structured Minitab project for quality reporting.This course combines theory, diagnostics, and applied analytics into a complete learning journey for mastering attribute SPC methods in Minitab-ideal for both industrial practice and academic advancement.

Skills

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