How can you use a scatter diagram to analyze variables in quality management?

Why is it that worse quality gets the more ways we need (metrics, KPI’s, etc.) to “measure” it. When quality is taken seriously from the start, the less ways you need to “measure” how bad it is. 20+ years ago we didn’t need all this because we actually cared about what we were making - result was things got delivered on time & in compliance. If companies spent 1/10 the money they spend on safety to use for quality, not only would quality improve but safety also. Stop wasting time on ways to measure quality and just do it. Failure to implement quality early & often will result in you spending unneeded $$$ on why things went bad. How much money is spent wondering why things went wrong when effective quality was always the answer.

Scatter plot tries to bring a relationship between 2 independent variables..when we say independent it means separate ways of measuring 2 variables..like the traditional height and weight, how related they are..but the plot does not determine the cause..As they famously say, correlation is not causation..

The use of a scatter diagram proves to be an effective tool in a proactive approach to map processes, identify potential anomalies, and pinpoint opportunities for optimization. The term 'proactivity' is deliberately employed to highlight the importance of ensuring adherence to quality rules in routine processes. This vigilance not only allows a focus on continuous improvement opportunities but also prevents non conforming situations arising from the non-application of quality rules. Thus, employing a scatter diagram aligns with a proactive approach that emphasizes problem prevention and fosters a culture of continuous improvement within the organization.

Scatter plot is one of the Seven QC tools , that helps with the data analysis. It helps with the Visual data analysis. Easy to use, helps with validating the relationship between the dependent and the independent variable It can used to either confirm or eliminate a cause, by plotting the Dependent variable (effect) against the independent variable (cause).

Example : Objective: Assess how drilling speed impacts PCB drilling defects. Variables: Independent - Machine Speed (500, 1000, 1500, 2000 RPM); Dependent - Defects (1, 3, 5, 8). Plotting: A scatter diagram was plotted with drilling speed on the X-axis and number of defects on the Y-axis. Method: Recorded defects at four speeds; plotted on a scatter diagram. Results: Increasing speed correlated with more defects, indicating a need to optimize speed for quality efficiency.

Scatter diagrams are powerful tools in data analysis, quality improvement, and problem-solving. 1. Visual Representation: - Clearly depict relationships between two variables. - Enable quick recognition of patterns and trends. 2. Correlation Analysis: - Assess the strength and direction of correlations. - Aid in predicting and understanding dependencies. 3. Root Cause Analysis: - Identify potential causes of problems or variations. - Contribute to targeted problem-solving efforts. 4. Decision Support: - Provide clarity for informed and data-driven decision-making. - Enhance communication and collaboration within teams.

It's a visual representation of the relationship between two variables in numerical data. Create it by gathering data pairs, like ambient temperature and product defects. Plot the independent variable on the x-axis, the dependent variable on the y-axis. A correlation is evident if points align along a line or curve. When a clear pattern indicates correlation, analysis concludes. For deeper insight, use regression or correlation analysis to quantify strength and direction. If no clear pattern, employ a trend test: divide the graph into 4 quadrants, count points in quadrants, compare sums. If the smaller sum is below the trend test limit, variables are related; if equal or above, the pattern may be due to chance.

Acredito que as métricas atreladas a qualidade est?o atreladas aos KPIs de resultado, tais como NPS, ROI, CS. Os KPIs, diferente disso consideraria o uso para gest?o dos riscos para a n?o qualidade. E os OKRs para planos te melhoria.

-7 QC tools help solve most (80% or more) of the problems and a scatter diagram is one of those tools. Hence, we cannot afford not to use it. - A scatter diagram is an easy-to-use visual tool that doesn't involve any statistics or calculation and, hence can be implemented easily. - People can be trained easily for the use of this tool to promote its use and easier implementation

A scatter diagram is a tool to graph pairs of numerical data and test for a relationship between them. We use this type of chart to make decision, we have cost and frequency, then will be easy to visualize what decision we can take it.

There are generally 2 axes used in generating a scatter diagram. One is called the X-axis and another is the Y-axis. One variable is shown on X and another on Y using a scale based on the data.

Scatter diagram we can use to compare Quality variation across different quality gates from Raw material Inspection to Final Inspection.The Quality management can use this data for decision making

Scatter diagrams in quality management provide a visual representation of variable relationships, aiding in pattern recognition and correlation assessment. They support informed decision-making, root cause analysis, and continuous improvement by offering insights into factors influencing product or process quality. To create a scatter diagram, organize data pairs for two variables, choose scales for axes, and plot points on graph paper or digitally. Label axes, add a title, and analyze for patterns. Optional trendlines can enhance correlation representation. Digital tools like Excel or Python aid in the creation, serving as visual aids for understanding variable relationships in fields, especially quality management.

Creating a scatter diagram involves: collecting data on two continuous variables, selecting axes, scaling them appropriately, plotting data points based on pairs of values, labeling axes clearly, adding a title for context, and analyzing the plot for patterns or trends. Use tools like spreadsheets or statistical software for ease and customization in generating and analyzing scatter plots to explore relationships between variables.

La metodología no es el verdadero camino. El control y manejo de la información es lo indispensable. El buen analisis es el camino correcto.

Correlation measures the linear association between two variables. It is important to note that correlation does not imply causation. A well-known example of this concept states that ice cream sales are positively correlated with shark attacks on swimmers. It would be a mistake to conclude that ice cream consumption is somehow causing sharks to attack. The more mundane explanation here is that warm weather brings about increases in both ice cream sales and oceanic swimming. Also, a low correlation coefficient does not necessarily imply the absence of a relationship, but rather suggests that the relationship is not linear. In other words, the variables may be related in a curved or non-linear manner, such as a parabolic or polynomial graph.

Shape: Whether the relationship between the two variables is linear or nonlinear Direction: Whether the relationship is positive (increasing) or negative (decreasing) Density: Whether there are trends, clusters, or outliers in the data A correlation is evident if points align along a line or curve. Randomly scattered data points with no clear trend indicate no correlation or a weak relationship

Generally, there can be 4 types of interpretations of scatter diagram: - No correlation: X doesn't impact Y: A line cannot be drawn to connect the dots or where the maximum number of dots can fall nearby - Positive correlation: Increase in X increases Y: An increasing trend line can be drawn to connect the dots or where the maximum number of dots can fall nearby - Negative correlation: Increase in X causes a decrease in Y: A decreasing trend line can be drawn to connect the dots or where the maximum number of dots can fall nearby -Curvilinear correlation: X impacts Y but not in continuous positive or negative way: When the dots form a curved pattern or maximum dots fall near a curved line such as U-shaped, inverted U-shaped, S-shaped, etc.

Scatter diagrams are used to look for trends in a data-set of two variables. As the data points get closer to forming a straight line, the correlation between the two variables strengthens, and vice versa. In cases where the data points form a linear trend going up from left to right, then there is positive correlation. This means that as x increases, so does y. A trend forming a straight line going down from left to right, has a negative correlation or as x increases, y will decrease. Observing any outliers or distinct patterns within the diagram is also needed. Outliers might indicate anomalies or exceptions in the data, while specific patterns (like clusters or groupings of points) might suggest subgroups within the data.

El control de la información puede ser cíclico o al azar, dentro de ciertas pautas. Lo importante, es el análisis posterior. De ahí las conclusiones y planes de acción.

A scatter diagram, like a scatter plot or graph, is a helpful tool in understanding data and making things better. It shows how two things are connected visually. It's simple but powerful because it makes complex data easier to see. By looking at the diagram, you can quickly spot patterns and unusual points that might be important. In improving quality, the scatter diagram helps find the reasons behind problems and makes processes better. It's like a map that guides decisions based on facts. The best part? It's easy to share with others, making teamwork smoother.

A scatter diagram can be used for the following applications: - Hypothesis testing - Root cause analysis (to identify the root causes based on identifying the relationship between 2 variables. E.g. Sleep time vs. weight / Health, Exercise duration and health - To check the effectiveness of solutions (similar to above)

Many times, you not only want to find the existence of a relationship between two variables (if any), but you also want to determine a value of the independent variable based on a mathematical model or relationship, which puts the resulting dependent variable in a specific optimal value. The scatter diagram is a powerful tool to achieve this goal (optimization).

1. Define Quality and Influencing Factors: Identify the key quality characteristic and potential influencing factors. 2. Collect Data: Gather relevant data for both variables. 3. Plot Diagram: Place the influencing factor on the x-axis and the quality characteristic on the y-axis. 4. Analyze Patterns and Correlations: Look for trends in the diagram to understand the relationship. 5. Identify Improvements: Based on the analysis, pinpoint areas for quality enhancement. 6. Prioritize Based on Impact: Focus on changes with the strongest correlation and potential impact. 7. Implement Actions: Take targeted steps to influence the identified factors. 8. Monitor and Adjust: Continuously track the effects and adjust as needed.

Scatter diagram can help you, within limits, to predict how a process may behave at future We sometime use it in measueremen to see a drift of equipment (or process) and correct it

In an industrial setting, let's say we're aiming to improve the efficiency of a manufacturing process. By using a scatter diagram, I could analyze the correlation between two variables, such as machine speed and defect occurrence. This helps identify if there's a relationship between the speed of the machine and the number of defects produced. With this insight, adjustments in machine speed could be made to potentially reduce defects, contributing to overall process improvement.

Without calling into question the relevance of the tool, it is essential never to rely solely on it, even if it tends to reflect reality in the field. A number of parameters have an impact on the tool's data, and can only be known by talking to the people concerned by the data collected in the field. Because of "laziness" or discomfort, it's easy to make decisions alone behind your screen, leaving the field work to N-1... and this is toxic in the long term. The same goes for decision-making. Sometimes, it's better to forego conclusions to preserve social peace. Not all conclusions are necessary. The decision-making tool is relevant, but only if it is accompanied by investigation, listening and communication on the ground.