Tabtrainer Minitab: Quality Charts: P-, NP-, P'-Laneychart

Achieve top-level expertise in Minitab with Prof. Dr. Murat Mola, recognized as Germany's Professor of the Year 2023.

What you'll learn
- Identify nominally scaled attribute data and apply the principles of binomial distribution to evaluate production quality using real data.
- Differentiate between relative defect rates in P Charts and absolute defect counts in NP Charts, understanding when to use each method.
- Analyze control chart signals and trace root causes of process instabilities, using real examples like increased defects during holidays.
- Interpret probability plots from the P Chart Diagnostic to validate if process data meet binomial assumptions and decide on the correct control chart.
- Perform a full P Chart Diagnostic to determine if the data's dispersion matches the expected random behavior of a binomial distribution.
- Apply AIAG guidelines to smooth control limits by assessing whether subgroup size variations meet the standard’s 75% tolerance condition.
- Import and explore real-world production data with 365 records, analyzing assembly dates, subgroup sizes, and number of defective skateboards.
- Calculate daily defect rates by relating the number of bad skateboards to the total produced, adapting to fluctuating daily subgroup sizes.
- Save the entire defect rate analysis project, ensuring structured documentation and easy reference for future process improvements.

Requirements
- No Specific Prior Knowledge Needed: all topics are explained in a practical step-by-step manner.

Description
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.

Who this course is for:
- 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.
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