Overview

Welcome to this expert-level course from the Tabtrainer® Series - your trusted source for applied statistical training in industry.In this training unit, you will master the use of P Charts and Laney P' Charts in Minitab, with a focus on attribute-based process control using real-world production data from the Smartboard Company. Going beyond tool usage, this course dives into the underlying statistical theory, including binomial distribution, diagnostics for overdispersion/underdispersion, and advanced control limit adjustment techniques based on AIAG standards.This course is ideal for quality engineers, Six Sigma professionals, and industrial analysts who want to detect and interpret process instabilities, understand the effect of subgroup size variation, and apply the correct control chart models in real production settings.Developed and led by Prof. Dr. Murat Mola, certified TÜV trainer and founder of Tabtrainer®, this training guarantees both technical depth and industrial relevance. Tabtrainer® stands for quality-focused learning with proven statistical tools and clear, actionable insights.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.After completing this training unit, participants will be able to:Understand the structure and workflow of the final assembly process at Smartboard Company, including early, late, and night shifts.Analyze the classification system for skateboard components based on surface inspection results into "good" and "bad" attribute categories, and recognize the financial consequences of rework and scrap due to defective parts.Import and explore real-world manufacturing data, consisting of assembly dates, subgroup sizes, and number of bad parts across a full year (365 data entries).Recognize the nature of nominally scaled data and understand its statistical treatment, particularly the application of binomial distribution for defect classification ("good" vs. "bad").Calculate daily defect rates by relating the number of bad skateboards to the total production volume per day.Perform a comprehensive P Chart Diagnostic to verify the conformity of real-world attribute data with the theoretical expectations of binomial distribution, including understanding concepts such as overdispersion and underdispersion.Interpret probability plots and agreement rates to make evidence-based decisions on the appropriate use of P Charts or Laney P' Charts.Create and interpret P Charts that visualize the relative proportion of defective skateboards over time, based on variable subgroup sizes.Understand the structure and use of NP Charts, which show the absolute number of defective units, and compare them to P Charts.Identify and correctly respond to process instabilities revealed through control chart tests (e.g., special causes detected via Test 1 - points outside three standard deviations).Perform root cause analysis for detected process instabilities, as demonstrated in the practical example of increased defect rates due to holiday-related staffing issues.Apply AIAG guidelines to assess the possibility of smoothing control limits when subgroup sizes vary, including calculations of subgroup size thresholds and adjustments for improved interpretability.Understand the impact of subgroup size variation on control limits and confidence intervals, and how larger or smaller subgroup sizes influence statistical precision.Learn the practical steps to apply smoothing of control limits by averaging subgroup sizes, when permitted under AIAG standards.Differentiate between situations where classical P Charts are sufficient and where Laney P' Charts are necessary due to significant systematic scatter effects.Create and interpret Laney P' Charts when P Chart diagnostics indicate significant deviations from binomial dispersion assumptions.Understand the practical implications of working with nominally scaled attribute data for quality control and continuous process improvement.Conclude whether a manufacturing process can be considered stable based on real-world attribute data analysis and control chart interpretation.Document and save the complete quality control analysis in a structured project format ("Defect Rate Final Assembly") for further reference and reporting purposes.

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