10 Best Root Cause Analysis Tools. Effective Problem-Solving

In the workplace, root cause analysis tools are often essential to help you find an effective solution to a complex problem.

Sometimes problems are easy to solve, but other times solving problems requires a systematic approach to help you dig deep into what is causing the problem. For example, if customer satisfaction is low, you need to find out why it is low, so you can change your procedures to satisfy your customers.

Unfortunately, identifying the cause of a problem is not always easy as causes are not always obvious. In addition, there may be more than one cause to a problem and you may need to prioritize which cause to address first.

This is when adopting a root cause analysis approach and using root cause analysis techniques can be extremely helpful.

Whether you are in marketing, sales, healthcare, or manufacturing, root cause analysis tools can help uncover hidden problems and guide you toward effective solutions.

In this post, I will guide you through 10 of the most commonly used cause analysis techniques you can adopt, with real-world examples from the workplace.

What Is Root Cause Analysis (RCA)?

The root cause analysis method of problem-solving (often shortened as RCA) has its origins in the manufacturing industry in the 19th century when it was applied to improve production processes.

Since then, the root cause analysis problem-solving approach has become a methodology used in all industries (e.g. from manufacturing to service providers) and many root cause analysis tools have developed.

In summary:

The root cause analysis approach consists in trying to find the cause (or causes) of a problem, instead of just trying to alleviate the symptoms. The goal of RCA is to find out why a problem occurred and how to stop it from happening again.

Root Cause Analysis Steps

The root cause analysis approach to problem-solving takes place in a series of steps, which can be identified as 5 different stages.

1. Identify the Problem

The first thing you need to do is identify and define exactly what the problem is.

Try to come up with a sentence that summarizes the problem, also called a problem statement. To help you be specific, ask yourself questions beginning with what, who, why, when, and where.

For example, imagine that you manage a customer service team and recently customers have started complaining more about waiting times. So, questions can include the following:

• What is the problem? Customers are complaining about long wait times.
• Who is affected? Customers calling the support center, and customer service agents handling the calls.
• Why is it happening? Unclear at this point, but needs to be investigated.
• When did it start? Over the past three months.
• Where is the issue occurring? Primarily in the phone support department.

The problem statement might thus be: “Over the last three months, customers calling our support center have experienced increased wait times, leading to a rise in complaints.”

This way, you are highlighting a specific timeframe, the people involved, the context, and the issue.

2. Collect data

Now that you have a problem statement, it is time to gather data in more detail.

For example, you might want to find out exactly how often the problem happens, what exactly its impact is, and any facts and figures that might be relevant.

Continuing from the previous example, now you might review call logs and find out that 30% of calls had wait times longer than 10 minutes, compared to only 10% in the previous period.

In addition, looking at customer feedback, you discover that there was a 20% increase in customer complaints and a 5% drop in customer satisfaction scores.

You also find out that the call volume has remained the same but staff attendance logs show that agent availability has dropped by 15%, which points to a problem with understaffing (an issue that needs investigating further).

3. Identify the Cause (or Causes)

You should now have more information to help you look for the cause of the problem.

This is when you can use one or more root cause analysis tools to help you identify the causes.

4. Develop a Solution

This is the problem-solving part of the process, where you try to come up with a solution.

You can gather your team and brainstorm ideas, gather feedback, ask questions, run focus groups and use other strategies to generate ideas for creative problem-solving.

5. Implement, Monitor and Review

After you have come up with ideas, you need to make a decision for which solution (or solutions) you want to implement.

However, the process does not end with implementation because you then need to keep an eye on what happens after you implement the solution (i.e. by monitoring the situation) and review the results, so you can see if you need to adjust your approach in case the solution has not worked as desired.

10 Best Root Cause Analysis Tools with Examples

Below, I have listed 10 different root cause analysis techniques with examples of how they may apply to the workplace.

These tools of root cause analysis can be useful in a variety of situations and in different industries.

1. 5 Whys

This is one of the most famous root cause analysis techniques and it was first created by Sakichi Toyoda, founder of Toyota Industries Corporation in Japan.

The 5 whys technique consists in asking “Why?” 5 times until you reach the root cause of the problem.

For example, imagine that customers are unhappy about having to wait too long when calling customer support. You can use the 5 whys root cause analysis tool as follows by asking:

1. Why are customers waiting too long? Not enough agents are available.
2. Why aren’t enough agents available? High absenteeism.
3. Why is absenteeism high? Agents feel stressed and overwhelmed.
4. Why do they feel stressed? They are handling too many calls without breaks.
5. Why are they not getting breaks? The scheduling system doesn’t account for high-volume periods.

The root cause then seems to be that poor scheduling systems are leading to agent overload.

2. Fishbone Diagram (Ishikawa Diagram)

This is one of the best root cause analysis tools to visualize the possible causes of a problem and sort them into categories.

You draw a diagram in the shape of a fishbone, with the head representing the problem and the bones along the spine each representing a main possible cause, broad enough to constitute a category.

Then, you brainstorm with the team to further divide each category into more specific causes.

For example, imagine a digital marketing campaign that did not meet conversion goals. A short problem statement is written at the head of the fishbone.

The team may identify 5 broad categories of causes such as: people, methods, technology, content, and budget.

They would then draw each of these as a bone on the fish diagram. Under each category, you then assign one or more possible causes, which they then write next to the corresponding bone, such as:

• People: Insufficient staff to manage the campaign.
• Methods: Poor ad targeting.
• Technology: Low website loading speed.
• Content: Weak call-to-action.
• Budget: Not enough budget for ad placements.

A possible root cause may then be identified as poor ad targeting combined with slow website loading speed that led to low conversion rates.

You can see an example of the Fish Bone diagram here.

3. Pareto Chart

The Pareto chart is a visual representation of the Pareto analysis, which in turn is grounded on the Pareto principle (also called the 80/20 rule).

According to this principle, roughly 80% of effects come from 20% of causes. This means that the majority of problems originate from factors that are in the minority.

To apply the Pareto analysis, collect data about a problem, sort them by frequency or impact, and focus on the factors that create the highest number of issues, or that create issues with the highest frequency.

Then, create a bar chart where each bar represents a different cause of a problem arranged in descending order of frequency or impact.

Add a cumulative line on top, showing the total percentage of the total effect that each category contributes as you move from left to right across the bars.

For example, a sales team is underperforming, with a significant drop in conversions.

The data that you have collected shows the following figures in terms of how much each possible cause contributes to the problem: Low lead quality (30%), long sales cycle (25%), insufficient follow-up (20%), untrained sales reps (15%), outdated CRM system (10%).

As you do the analysis, the Pareto chart shows that 80% of the issue comes from low lead quality and a long sales cycle.

As a result, you discover that addressing lead quality and improving sales cycle efficiency could solve the majority of the performance problem.

4. Scatter Plot Diagram

The scatter plot chart uses a cartesian diagram to position two sets of data in the form of dots, to see if there is a correlation.

For example, imagine you work in healthcare and notice an increasing number of patient readmissions after surgery.

So, you use a scatter plot to analyze the correlation between readmission rates (positioned on the x-axis) and patient follow-up appointments (positioned on the y-axis). You then make a dot on the chart where the data from the two sets of variables meet.

In this example, the scatter plot may reveal a strong correlation between missed follow-up appointments and higher readmission rates. This may mean that poor follow-up management after surgery is contributing to the rise in readmissions.

5. Affinity Diagram

The affinity diagram groups a large amount of data into categories or groups based on natural relationships.

With your team, you brainstorm potential causes for the problem and group them into categories. This process can help you see relationships between causes that you might otherwise miss.

For example, imagine you work in a university where there are high dropout rates among first-year students.

So, with your team, you use an affinity diagram to group the various factors that might influence the dropout rates. For instance, you might identify factors such as financial problems, academic difficulties, social integration issues, and mental health concerns.

You may then notice that financial difficulties and lack of social integration were the most common factors across multiple categories and, as a result, the university decides to offer financial support and mentorship programs for new students.

6. Fault Tree Analysis (FTA)

The fault tree analysis (FTA), also called event tree analysis, is a top-down approach to identifying the possible causes of an undesired event and the possible relationships between these causes.

FTA is one of the root cause analysis techniques that uses a diagram to represent visually the causes and possible correlations.

In this case, the top event (i.e. the fault) goes at the top of the diagram and then, underneath and connected by a line, you position the possible cause. You then break down each cause into possible sub-causes to create a diagram that looks like an inverted tree with branches going down.

FTA also uses specific symbols to signify different types of events, so you can better visualize the types of causes involved and their relations to each other.

A fault tree analysis can be done after the problem has occurred, or before the event in order to prevent it.

Fault tree analysis is predominantly used in industries such as engineering, mining, aerospace, transportation, chemicals, software engineering, and manufacturing. For example, imagine that there is some equipment failure. You can use FTA to break down its potential causes.

So, you represent the failure as the top event and then add the branches underneath, which might be: Maintenance (lack of scheduled maintenance), Mechanical issues (worn-out components), Operator errors (inexperienced staff), Power issues (frequent voltage fluctuations).

As a result of the analysis, you might identify the cause of the failure as a lack of regular maintenance and inexperienced operators. As such, you may decide to Implement preventive maintenance schedules and training programs.

7. Change analysis/Event analysis

These are two root cause analysis techniques that are similar. However, change analysis focuses on how a system or process has changed over time, while event analysis focuses on what happened during a specific incident.

For example, imagine that you work at an embassy and there have been delays in processing citizens’ passport applications.

You can use change analysis to compare the process before and after the problem started.

So, you find out that, before the event, there were adequate staffing levels and stable IT systems. However, at some point in time, there was a reduction in staff and a new IT system was adopted.

So, it becomes clear that staffing reductions and glitches in the new IT system contributed to the delays. As a result, the embassy decides to increase staffing and/or improve the new IT system functionality.

8. Failure Mode and Effects Analysis (FMEA)

Failure mode and effect analysis (FMEA) is one of those root cause analysis tools that are prevalently used in manufacturing and engineering industries, as it is very detailed and good for identifying risks.

FMEA systematically identifies potential failure points in a process or product and prioritizes them based on their risk linked to occurrence and detectability.

You run this type of analysis by creating a table where you list all the possible failure modes (i.e. the inabilities to perform a certain function correctly), their potential effects, the causes, how likely they are to occur, and how easy or hard they are to detect. Then, prioritize these possible failures based on their level of risk and act to prevent them.

For example, imagine you work in the automotive industry and a vehicle component frequently fails after 10,000 miles of use.

You identify the potential failure modes as material fatigue, poor installation process, and component design flaws. The possible effects of these include breakdowns, safety hazards, and customer dissatisfaction.

You then identify material fatigue as being the mode that has the highest risks of happening and of producing the worst effects out of all. The reason why the material deteriorates easily is if you use poor quality in the manufacturing process, so you decide to use better material instead.

9. 8D Report (Eight Disciplines)

the 8D report requires a team of individuals to work together and it involves 8 steps, or disciplines, to find the cause of a problem and address it. So, the 8D report is a whole process that involves various tools as opposed to just being one technique.

For example, imagine that your company has just launched a product that, unfortunately, has a high defect rate. So, you decide to use the 8D report technique to address this problem step by step as such:

1. Form a team: choose the right people with the appropriate expertise to work together (e.g., product designers and experts in manufacturing)
2. Define the problem: create a clear problem statement by using the 5W2H questions (i.e. what, who, why, when, where, how, and how many)
3. Implement a temporary fix to contain the problem (e.g., stop shipping the product)
4. Identify the root cause: you can use one of the other root cause analysis tools mentioned in this article to do it.
5. Develop a permanent corrective action: e.g., decide to fix a problem in the design.
6. Implement this action: e.g., ask designers to re-design the product and test it thoroughly.
7. Prevent recurrence: for instance, by regularly reviewing and testing the design.
8. Congratulate the team: this is an important step for staff motivation and engagement.

10. DMAIC (Define, Measure, Analyze, Improve, Control)

Like the 8D report, this is a process rather than just one individual root cause analysis technique as it involves 5 steps to provide a structured approached to problem-solving.

For example, imagine you are a manager working for a retail chain that has seen a sharp decline in customer satisfaction scores. You then decide to use the 5 DMAIC steps to solve this problem.

So, your team will:

1. Define the problem as precisely as possible. Let’s say, for this example, you define it as a decline in satisfaction scores.
2. Measure: In this case, you might collect customer feedback and satisfaction data.
3. Analyze: you use any tool at your disposal to understand the problem, and root cause analysis tools can be useful here.
4. Improve: for example, as a result of your analysis, you decide that it will be useful to introduce self-checkout systems and retrain staff.
5. Control: for instance, you decide to monitor satisfaction scores and customer feedback to make sure customer satisfaction remains high in the future.

Using Root Cause Analysis Tools for Problem-Solving

All the root cause analysis tools, techniques, and processes we have seen so far are very helpful for problem-solving and can be applied in many industries and situations.

If you are a manager it is worth being aware of these tools and choosing the most appropriate based on the situation.

Likewise, if you are a corporate trainer or facilitator, you can introduce one or more of these techniques when you teach workshops on problem-solving in the workplace.

If you are teaching or delivering training sessions on problem-solving, you may find these editable problem-solving training course materials useful.

Owner at Symonds Research Training Materials

Valeria has a PhD in Sociology, has 25 years+ working in education, has taught at the University of Bath &Cardiff Metropolitan University, & Hankuk University (Korea) & worked as a researcher for Exeter University, & has many years of industry experience including with Cardiff University in management roles. She now creates training course materials courseware.

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