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Tools Used in Root Cause Analysis: The Scatter Plot Diagram

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In the first part of our article on effectively addressing the core of an issue with root cause analysis, we explored the concept of root cause analysis and introduced two powerful tools: the Pareto Chart and the 5 Whys technique. These tools are essential for dissecting complex issues, prioritizing causes, and uncovering the underlying factors driving recurring problems. Having these two tools for your root cause analysis is a great start. However, for you to have a robust root cause analysis, we believe your arsenal should have more tools to conduct your root cause analysis.

Here we will explore using the scatter plot diagram in conducting a root cause analysis, and how you can use it to find the root cause of food quality and food safety issues in your food business.

A scatter plot diagram is a graphical representation tool used primarily in statistical analysis and can find usefulness in root cause analysis when you suspect a cause-and-effect relationship between two variables but lack conclusive evidence. It helps visualize the relationship between two variables. In root cause analysis, scatter plots are particularly useful for identifying potential correlations or patterns between variables that may indicate a causal relationship. For instance, if you’re dealing with a situation where there’s a notable increase in reports of foodborne illness associated with a specific pre-packaged salad product, your initial investigations may naturally focus on factors like ingredient quality or sanitation practices.

However, a scatter plot diagram can serve as a valuable tool to explore potential links between variables. In this case, you can consider the independent variable as the internal product temperature upon delivery to retailers, recorded during deliveries, while the dependent variable could be the number of customer complaints about illness linked to the product reported over a set timeframe. By plotting these data points on a scatter plot, you can discern if a correlation exists. If you find a positive correlation, where higher complaint numbers correspond to deliveries with higher product temperatures, it may suggest improper temperature control during transport as a contributing factor to bacterial growth and potential illness.

Conversely, the absence of a clear correlation weakens the case for temperature as a primary cause, redirecting you towards exploring other factors like ingredient contamination. The benefits of utilizing a scatter plot in this context are manifold. Firstly, it helps you identify potential culprits by visually highlighting links between temperature and illness complaints, prompting further investigation into storage and transportation practices. Additionally, it aids you in prioritizing actions, as efforts can be directed toward improving cold chain management to ensure consistent product temperature throughout the supply chain.

It’s important to remember that while a scatter plot can reveal potential areas of concern, it does not definitively prove causation. Nonetheless, it remains a powerful tool in your root cause analysis process, guiding you toward key areas warranting further scrutiny and action.

How to Use the Scatter Plot Diagram

In identifying potential causes, begin by brainstorming and defining factors that could be influencing the problem at hand. Once you have a list of suspected factors, move on to the data collection phase, where you gather measurable data points for both the suspected cause (independent variable) and the problem itself (dependent variable). It’s crucial to ensure that your data collection methods are consistent and accurate throughout this process.

Next, you’ll need to plot your points using a graphing tool such as spreadsheet software or dedicated plotting tools. Remember, the independent variable goes on the x-axis (horizontal axis), while the dependent variable goes on the y-axis (vertical axis). Each data point should be plotted as a dot or symbol on the graph, with its x and y values corresponding to the respective variable measurements. As you analyze the pattern formed by the plotted data points, look for any trends or clusters in their distribution. A positive correlation would show points generally trending upwards from left to right, while a negative correlation would show points trending downwards from left to right. If there’s no clear pattern, it suggests a weak or nonexistent correlation between the variables.

Do you need assistance with conducting a root cause analysis for food safety and food quality issues in your food business? We are experts in conducting a root cause analysis and we are ready to partner with you to assist your business with resolving food quality and food safety issues so that you can focus on other key areas of running your food business. 

Get in touch with us at Fresh Group to book your consultation!