Manufacturers who have production capacity constraints, or struggle to understand where they lose capacity, can use Root Cause Analysis (RCA) to thoroughly investigate downtime incidents, and implement targeted corrective actions that lead to long-term improvements in productivity, cost savings, and equipment reliability.
This article looks at Root Cause Analysis, with a focus on how it can be used to identify the symptoms and remove altogether the causes of machine downtime.
Root Cause Analysis (RCA) is a systematic approach used to identify the underlying cause of a problem rather than just addressing its symptoms. In manufacturing, RCA is a critical tool to support a Lean manufacturing production method, as it is used for diagnosing and eliminating the causes of an issue, for example, machine downtime, thus improving efficiency, and preventing recurring issues.
RCA isn’t about searching for problems—it’s about understanding them. The process often highlights underlying inefficiencies or risks that might have otherwise gone unnoticed, enabling teams to address multiple issues at once and drive broader improvements.
By embedding Root Cause Analysis (RCA) into daily operations, teams develop a consistent, structured approach to tackling issues. While every problem has its nuances, RCA ensures a systematic and repeatable method for identifying and resolving challenges across departments.
Teams that regularly use RCA develop a habit of questioning why issues occur and how they can be prevented. This mindset fosters a culture of continuous improvement, enhancing operations, safety, maintenance, and quality across the organisation.
When one department successfully applies RCA, its insights and best practices can be shared across the business. By leveraging data and learnings from each analysis, teams can collaborate more effectively to solve problems and drive company-wide improvements, this is especially effective in organisations that have a culture of continuous improvement.
By tackling the root cause rather than just the symptoms, RCA helps prevent recurring issues that drain time and resources. This approach minimises unplanned downtime, reduces defects, and streamlines processes—ultimately improving efficiency and profitability.
Manufacturers that use RCA to reduce machine downtime can experience significant operational benefits, including:
Reduced Unplanned Downtime: Identifying and fixing underlying issues prevents repeated breakdowns.
Improved Machine Reliability: Addressing root causes enhances equipment longevity and performance.
Increased Productivity: Minimising downtime leads to higher production output, supporting better capacity management.
Lower Maintenance Costs: Preventive measures reduce emergency repairs and part replacements.
Enhanced Safety and Compliance: Eliminating root causes of failures can improve workplace safety and regulatory adherence.
Conducting an effective RCA involves a structured, step-by-step approach. Below is a framework for using RCA to diagnose and resolve machine downtime issues in manufacturing.
Clearly outline the issue by gathering key details such as:
When did the downtime occur?
What equipment was affected?
What were the observed symptoms?
How long did the downtime last?
How often does the downtime occur?
Were there any contributing factors, such as operator involvement or environmental conditions?
Gathering accurate data is essential for diagnosing the root cause. This includes:
Use analytical tools to explore potential causes. Three effective methods are: A downtime Pareto analysis, the 5 Whys and a Fishbone diagram. We explore each of these in more detail below.
A Pareto Chart is a valuable tool for performing Root Cause Analysis (RCA) on downtime, as it helps prioritise the most significant factors contributing to the issue. Based on the 80/20 rule, it visually highlights the few key causes that account for the majority of downtime, allowing teams to focus on solving the most impactful problems first.
Collect Downtime Data
Gather data on downtime events over time (e.g., a week, month, or quarter).
Categorise downtime causes (e.g., machine failure, operator error, material shortages, maintenance delays).
Record the frequency and duration for each cause.
Sort Causes by Impact
Rank downtime causes from highest to lowest based on their total impact (e.g., total minutes lost).
Create the Pareto Chart
The X-axis represents downtime causes (arranged from most to least impactful).
The left Y-axis shows the total downtime (e.g., minutes or occurrences).
The right Y-axis displays the cumulative percentage of downtime.
Use bars for downtime values and a line graph for cumulative percentage.
Identify the Key Contributors
Look for where the cumulative percentage reaches ~80%—this helps pinpoint the small number of causes responsible for most downtime.
Prioritise RCA Efforts
Focus RCA on the top downtime causes, as eliminating these will yield the biggest improvements.
The '5 Whys' is a simple but effective questioning technique that helps drill down to the fundamental cause of a problem.
Example:
Why did the machine stop? → A motor failed.
Why did the motor fail? → It overheated.
Why did it overheat? → The cooling fan was not working.
Why was the cooling fan not working? → A build-up of debris blocked airflow.
Why was debris accumulating? → Routine maintenance was missed.
Root Cause: Lack of scheduled maintenance leading to overheating issues.
A visual tool that categorises potential causes into different groups such as:
Equipment: Faulty components, ageing machines
Processes: Inefficient maintenance schedules, poor standardization
People: Lack of training, human errors
Materials: Poor-quality spare parts, supply chain delays
Environment: Temperature fluctuations, dust contamination
This method helps teams systematically identify and categorise all possible contributing factors.
Step 4: Verify the Root Cause
Before implementing solutions, validate the root cause by:
Running tests or simulations
Comparing similar past incidents
Checking whether eliminating the suspected cause prevents further downtime
Consulting experts or experienced operators
Step 5: Implement Corrective Actions
Once the root cause is confirmed, apply appropriate solutions such as:
Adjusting preventive maintenance schedules
Training operators to follow best practices
Upgrading or replacing outdated equipment
Enhancing environmental controls (e.g., dust extraction, temperature regulation)
Refining the layout of the cell/production line
Step 6: Monitor and Continuously Improve
RCA should not be a one-time activity. To ensure long-term improvements:
Track the effectiveness of implemented solutions
Use machine monitoring software to analyse downtime trends
Conduct periodic RCA reviews for recurring downtime events
Standardise best practices and integrate them into training programs
Challenge: A manufacturer experienced frequent unplanned downtime on its CNC machines, leading to production delays and high maintenance costs.
Problem Defined: Machines frequently stopped due to spindle motor failures.
Data Collected: Maintenance logs showed repeated overheating incidents. Machine downtime logs showed how many hours of downtime had been lost because of breakdowns.
Analysis: 5 Whys: Overheating was traced to clogged air filters, which were not being cleaned regularly.
Root Cause Identified: Lack of scheduled filter cleaning caused airflow blockages, leading to overheating and eventual motor failure.
Solution Implemented:
Introduced automated alerts for filter cleaning.
Trained operators to inspect filters daily.
Implemented real-time monitoring to track temperature fluctuations.
Unplanned downtime was reduced by 40%.
Maintenance costs were lowered by 25%
Improved overall equipment effectiveness (OEE) by 10%.
Root Cause Analysis is an essential tool for manufacturers looking to reduce downtime and improve operational efficiency. By systematically identifying and eliminating root causes, manufacturers can enhance productivity, reduce costs, and create a more reliable production environment.
For companies seeking a proactive approach to downtime reduction, machine monitoring systems like FourJaw provide real-time visibility and actionable insights, making RCA more efficient and data-driven.