Root Cause Analysis With Fishbone Diagram Example, Pareto, And Control Charts

This Statistics Assignment Help sample showcases the use of the Fishbone Diagram using an example to perform the root cause analysis. The sample assignment also discusses Pareto Diagram and Control Charts in quality management. It investigates how these techniques can evaluate process stability, rank important issues, and analyze underlying causes. It demonstrates their use in statistical and business contexts by highlighting real-world examples, such as determining the reasons behind exam failure and assessing mobile quality issues. This illustration demonstrates the superior support we offer to students enrolled in business, statistics, and mathematics courses.

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Root Cause Analysis with Fishbone Diagram

Quality Management Tools Explained: Fishbone, Pareto, and Control Chart Examples

Assignment Question:

Analyze quality management issues using the Fishbone Diagram, Pareto Diagram, and Control Chart. Identify root causes, prioritize critical problems, and evaluate process stability through statistical tools. Provide recommendations to address identified challenges and improve overall quality.

Part 1:

A) Fishbone Diagram in the context of Quality Management

Question: What is a Fishbone Diagram?

Answer: A Fishbone Diagram, also known as a Cause-and-Effect Diagram or Ishikawa Diagram is a tool in a visual chart form that is used to break down the main problem into possible causes. A Fishbone Diagram helps in finding the root cause and categorizing them visually in an easily understandable form. It is often used in management and statistics courses and during brainstorming sessions while performing the root cause analysis.

According to Sheikh (2019), “A fish-bone diagram is one of the seven quality circles (QC) tools. It helps to visualize the potential causes to find the root cause of a particular problem. It helps to identify, analyze, and improve quality issues.”

The main purpose of a Fishbone Diagram (Alcocer, n.d.) “is to help people figure out all of the things that are causing a particular problem. For example, if you have a Kitchen knives business, from your fishbone diagram, you’ll be able to see that your pricing, your advertising, and the quality of your product are causing your sales to be slow.”

Below is a fishbone diagram for the defect ‘failing an exam’:

fishbone diagram example for the defect ‘failing an exam’

The above diagram was constructed by following the given steps:

Drawing the arrow towards the right which contains the effective result i.e., Failing the exam.
Identify the key issues and people of interest that are studied to determine the main reasons for failing the exam. These are known as categories. In this case, I found them to be Students, Teachers, Resources, Mental Ability, Socio-Economic factors, and, Learning environment.
Analyze the categories separately and find the causes that contribute to the effect. Connect these causes to the category branches respectively.

While mostly useful potential challenges may arise when applying the Fishbone diagram. (Trout, J., n.d.) “The brainstorming process can produce irrelevant potential causes along with relevant ones, which can result in confusion and a time drain.

Complex diagrams with multiple factors can lead to a jumbled mess that is too difficult to display in a fishbone diagram.”

B) Pareto Diagram and interpretation of the findings

Below is a Pareto diagram from the given data:

Pareto Diagram and interpretation of the findings

DATA FOR THE INTERPRETATION

(Clinical Excellence Commission, n.d.) “The Pareto principle (also known as the 80/20 rule) states that, for many events, roughly 80% of the effects come from 20% of the causes. Vilfredo Pareto showed that approximately 80% of the land in Italy was owned by 20% of the population.”

The chart suggests that almost 80% (77%) of the occurrences of poor mobile quality are identified by just 2 out of 7 causes i.e., approx. 29% of the causes. Our results are close enough to the Pareto principle. The main reason for the observed deviation in our results is the small sample size, we can expect more accurate results with increased sample size.

Part 2:

A) Control Chart for minor defects

STATISTICS ASSIGNMENT HELP - Control Chart for minor defects

The chart shows that the process of minor defects is a stable process as most of the points are near the average, few points are near the control limit and no point is beyond the control limits. The chart passes the rule of seven tests.

B) Challenges of using Control Charts

Though Control charts are useful in many industries, they do carry some disadvantages, (DeBenedetti, J., 2017),

“Flawed Assumptions– There are two main assumptions underlying control charts. The first is that the measurement function monitoring a process parameter has a normal distribution. In reality, though, this may not be the case, meaning a control chart will fail to produce meaningful data. The second assumption is that measurements are independent of each other, which also may not be true. If both assumptions are in some way flawed, then control charts will fail to be useful.

Special Technical Training- Although control charts are not difficult to understand mathematically, they do require special training to create and use. Control charts use basic statistics, such as mean and standard deviations. Small organizations with limited training resources and limited experience with quality assurance techniques will likely have difficulty implementing and using control charts. Businesses have to decide whether or not they can train their employees on lean and Six Sigma tools before using these quality tools to help improve their processes.

Misplaced Control Limits- Control limits may be set too close or too far away from the process mean, distorting the information produced by control charts.”

Wider Application– The idea behind stratification is to divide the overall population of data into two or more groups based on some factor that may influence the data. This cannot be done on a wider stage simply because such large amounts of data can only be handled by Super computers.