Comment intégrer l’amélioration non normale des processus de données à d’autres initiatives et cadres de qualité ?

🤔 What is non-normal data, what does it mean and why should you care? First you are probably wondering why it isn’t called abnormal data … don’t worry about that part for now as mathematicians can be particular about these things ;) A great deal of business data collected on processes ends up be classified as non-normal meaning that you can’t you all the typical statistical tools to analyze it. There are specific analystical tools for non-normal data. If you are working in the service industry, I wouldn’t sweat this too much as the errors you will introduce by not using the right analytical tools won’t likely be material to you. Manufacturing? Healthcare? Aerospace? - use the right tools! 👍 Like to support this

Non-normal data, stemming from skewed distributions, outliers, or measurement errors, is critical in process improvement. I've witnessed how ignoring its non-normality can lead to inaccuracies in assessing process capability, potentially overestimating or underestimating defect rates, process variation, and performance indices. Such errors can cause misguided decisions, resource wastage, and customer dissatisfaction. Why this matters? Recognizing and accurately handling non-normal data ensures the reliability of process capability assessments, guiding better decision-making and resource allocation for quality initiatives.

Non-normal data in real-world processes challenges traditional statistical assumptions, impacting accuracy. Identify using tools like histograms and Anderson-Darling tests, and (if needed) transform with methods like logarithmic or square root for reliable analyses. Analyze using robust non-parametric methods like Wilcoxon rank-sum, Kruskal-Wallis for accurate insights. Integrate into quality frameworks by adjusting methodologies, ensuring precise results. In the control phase, maintain ongoing monitoring and adjustments to sustain improvements. Additional considerations include validating assumptions, team training, collaborative understanding, leveraging technology for analytics, and staying updated on evolving methodologies

Non-normal data, including skewed, bimodal, or discrete distributions, can arise from various sources such as outliers or measurement errors. Ignoring data non-normality and using inappropriate methods can lead to inaccurate or misleading results, such as: Misestimation of Defects: Overestimating or underestimating defect proportions. Incorrect Process Variation: Inaccurate assessment of process variability. Faulty Performance Indices: Misleading process performance metrics. These inaccuracies can result in poor decision-making, resource wastage, and customer dissatisfaction. It’s crucial to use appropriate statistical methods and transformations to handle non-normal data effectively.

Non-normal data matters because it can reveal important insights that traditional methods might miss. Understanding this data helps identify areas needing improvement, especially in unique or complex situations. In one project, we encountered non-normal data in customer complaints about roofing materials. By analyzing this data, we discovered patterns that led us to rethink our supplier choices. This change not only improved quality but also reduced customer issues. Recognizing the value of non-normal data can drive significant improvements in your processes.

Identifying non-normal data is a crucial step before conducting any process capability analysis. Methods to detect non-normality include: ⚙️Graphical Methods: Use histograms, box plots, or probability plots to visually compare your data against a normal distribution curve. ⚙️Numerical Methods: Calculate and compare your data's skewness and kurtosis to those of a normal distribution. ⚙️ Statistical Tests: Apply tests like Anderson-Darling, Kolmogorov-Smirnov, or Shapiro-Wilk to examine the hypothesis that your data is normally distributed. These techniques help ascertain whether your data deviates from normality, guiding appropriate analysis methods.

When faced with non-normal data, you can either transform it to a more normal shape or apply non-normality accommodating methods. Transformation involves using mathematical functions (e.g., logarithmic, square root, Box-Cox) to modify the data's distribution. This can mitigate issues like skewness or outliers but alters data scale and interpretation. Care is needed when interpreting and communicating transformed results. Alternatively, adopting non-normality-tolerant methods allows analysis without altering the data, maintaining the original meaning and scale.

1. Graphical Methods: Histograms: Plot your data and compare with a normal curve. Box Plots: Examine for symmetry and outliers. Probability Plots: Compare your data distribution against a theoretical normal distribution. 2. Numerical Methods: Skewness: Measure the asymmetry of your data distribution. Kurtosis: Assess the "tailedness" compared to a normal distribution. 3. Statistical Tests: Anderson-Darling Test: Tests if data comes from a normal distribution. Kolmogorov-Smirnov Test: Compares your data’s distribution with a normal distribution. Shapiro-Wilk Test: Evaluates the null hypothesis that the data is normally distributed.

To identify non-normal data, look for outliers or unusual patterns in your data sets. This can include unexpected results, extreme values, or any data that doesn’t fit the typical distribution. In my experience, while reviewing customer feedback, I noticed a few complaints that were significantly different from the rest. Instead of ignoring them, I dug deeper. This led to uncovering a specific issue with a particular product line. Identifying non-normal data can reveal hidden problems that need attention and drive meaningful improvements.

After years in the industry as a practitioner, instructor and leader it is important to note that non-normal data isn’t bad data. It simply reflects the reality of how the process is performing; particularly when measuring time or where seasonality, other cycles are present. I advocate to use the actual data, leveraging non-parametric testing where non-normal data is present. But if your goal is to work with normal data consider these 2 steps before resorting to transformation: 1)Verify the data – ensure it is clean and accurate (check your measurement system) 2)Use Central-Limit Theorem – aggregate the data and test again to see if the data is now normal (pick the lowest level aggregation to minimize loss of granularity in your results).

Transform the Data: Apply mathematical functions like logarithms, square roots, or Box-Cox transformations to adjust the distribution. This can reduce skewness and variance but may alter the data’s scale and meaning, requiring careful interpretation. Use Non-Normal Methods: Employ non-parametric tests (e.g., Wilcoxon rank-sum, Kruskal-Wallis) or robust statistical techniques that do not assume normality. These methods handle non-normal data effectively without altering the data itself.

1. Non-Parametric Statistics: Median: Provides a measure of central tendency. Interquartile Range (IQR): Assesses data spread. Percentile Method: Estimates process capability indices using specific percentiles. 2. Distribution-Specific Methods: Johnson Method: Fits a distribution to your data for analysis. Weibull Method: Applies Weibull distribution to estimate process capability.

Non-normal data doesn’t hinder achieving quality and excellence. You can integrate non-normal data analysis with quality initiatives like Six Sigma, Lean, ISO, or Baldrige by: 1. Aligning Goals: Ensure process capability goals match customer requirements, strategic objectives, and performance indicators. 2. Using Appropriate Methods: Apply suitable tools for non-normal data to ensure accurate analysis. 3. Validating Results: Verify and validate your findings for reliability. 4. Clear Communication: Report results effectively to stakeholders and customers.