Six Big Losses of OEE Manufacturing

In manufacturing efficiency and productivity is key to being competitive. One of the key metrics to measure and improve efficiency is Overall Equipment Effectiveness (OEE). But to really optimize OEE you need to understand the “Six Big Losses”, the common production issues that affect manufacturing performance. This article will look at OEE manufacturing, the Six Big Losses in Lean and how to mitigate them for better performance. The article will also look at how targeted efforts to eliminate these losses can increase productivity and profits.

What is Overall Equipment Effectiveness (OEE) in Manufacturing?

OEE (Overall Equipment Effectiveness) is a measure of effectiveness of production equipment. It includes three components:

• Availability: The percentage of time the equipment is available to run.
• Performance: The speed at which the equipment operates as a percentage of its designed speed (including for reduced speed and minor stops).
• Quality: The percentage of good parts produced out of total parts.

OEE provides a clear picture of how effectively a manufacturing process operates and highlights areas for improvement. An OEE of 100% means no losses. In reality, such a result is almost impossible to achieve.

Six Big Losses in Lean

The Six Big Losses are a universal framework within Total Productive Maintenance (TPM) and lean principles that categorise the top 6 causes of production inefficiencies. Understanding these losses is key to improving OEE. Let’s get into each one.

1. Equipment Failure (Unplanned Downtime Losses)

Equipment failure is a situation, in which machinery stops working unexpectedly due to unforeseen events like equipment failure or tool failure and production stops. This results in availability loss as the equipment can’t run during downtime. Common causes are mechanical wear and tear, lack of maintenance and operator error. Implementing downtime tracking can help you identify patterns and root causes of equipment failure so you can target your maintenance more effectively.

Mitigation Strategies:

• Implement a robust preventive maintenance program.
• Use predictive maintenance tools that leverage OEE data to identify potential failures before they occur.
• Train operators to detect early warning signs of equipment issues.

2. Setup and Adjustment Losses

Setup and adjustment losses occur during changeovers or when adjustments to manufacturing equipment are needed for new production runs. This results in longer changeover times. These losses reduce available production time and contribute to availability loss.

Mitigation Strategies:

• Standardize and simplify changeovers using SMED (Single-Minute Exchange of Die).
• Schedule setup activities during planned downtime.
• Use automation to reduce manual adjustments.

3. Small Stops (Idling and Minor Stoppages)

Small stops are short, frequent interruptions caused by things like sensor misalignment, material jams or minor operator intervention. While these may seem insignificant they add up over time and cause performance losses. Another cause of performance losses is slow cycles where equipment is running slower than ideal cycle time. This is often due to dirty or worn out equipment.

Mitigation Strategies:

• Do root cause analysis to eliminate recurring issues.
• Adjust equipment settings to prevent small stops.
• Train operators to deal with small issues quickly and efficiently.

4. Reduced Speed (Performance Losses)

Reduced speed is when equipment is running below designed speed resulting in longer cycle time and performance losses. This is due to ageing machinery, suboptimal maintenance or inefficient processes.

Mitigation Strategies:

• Calibrate and maintain equipment regularly to ensure optimal performance.
• Monitor OEE data to see trends in speed reductions.
• Upgrade old machinery that can’t keep up with current production demands.

5. Process Defects (Scrap and Rework)

Defects are products that don’t meet quality standards, often caused by equipment handling errors and need rework or scrapping. This results in quality losses and reduces the effective output of a process. Monitoring the scrap rate can give you insights to the frequency and causes of process defects so you can implement better quality control measures.

Mitigation Strategies:

• Implement strict quality control.
• Do root cause analysis to address the underlying issues causing defects.
• Train staff on proper handling and manufacturing techniques.

6. Reduced Yield (Startup Losses)

Reduced yield, also known as yield loss in manufacturing, occurs during the startup of production when processes haven’t stabilised. This results in quality loss as defective products are produced during this time.

Mitigation Strategies:

• Standardize startup procedures to ensure consistency.
• Use OEE data to analyse and improve startup efficiency.
• Invest in advanced process control systems to reduce startup waste.

Six Big Losses to OEE

The Six Big Losses provide a framework to understand and address the specific areas that are reducing OEE. Each loss maps to one or more of the OEE components:

• Availability Losses: Equipment Failure, Setup & Adjustment.
• Performance Losses: Small Stops, Reduced Speed.
• Quality Losses: Process Defects, Startup Losses.

Using OEE Data to Address the Six Big Losses

OEE data is a powerful tool to identify and prioritize areas for improvement. By analyzing OEE performance losses trends, manufacturers can find the biggest sources of production loss and develop targeted solutions. For example:

• If OEE data shows frequent breakdowns, then preventive maintenance is a priority.
• If reduced speed is a recurring issue, then equipment upgrade or process optimization may be needed.

OEE data also allows benchmarking, so you can compare performance across shifts, lines or facilities and implement best practices. Maximizing equipment effectiveness is about improvement efforts that addressing the Six Big Losses is all about.

Real-World Examples

Many manufacturers have used the Six Big Losses framework and OEE to achieve big results. For example:

• A packaging company found that small stops were 15% of their production loss. By fixing those, they went from 65% to 80% OEE.
• An auto parts manufacturer reduced yield loss in manufacturing by standardizing start up procedures, leading to a 10% reduction in waste and significant cost savings.
• A food processing plant reduced equipment failures by 20% with a predictive maintenance program and enhanced availability.

These real world examples show how much OEE improvement can be gained by addressing the Six Big Losses. And equipment handling errors has been key to OEE improvement by reducing defects and rejects.

OEE through Cultural and Technological Change

Building a Culture of Continuous Improvement

Operational excellence starts with a culture of continuous improvement. Everyone in the business should be involved in finding and eliminating waste. Getting employees involved in improvement initiatives like Kaizen events and Gemba walks creates a collaborative environment for continuous improvement.

Digital Transformation

Technology plays a big part in OEE optimization, making sure production equipment is optimised. Solutions like real-time monitoring systems, AI based analytics and predictive maintenance platforms give manufacturers more visibility into their business. They find problems and give recommendations to prevent downtime, increase productivity and improve quality.

Training the Workforce

Even the best technology is useless without skilled operators. Training programmes should give employees the knowledge and tools to operate equipment efficiently, troubleshoot and maintain high quality. Cross training also gives flexibility and reduces the impact of labour variability on production.

Insights: Reducing Production Losses

The Role of Automation in Combating OEE Losses

Automation is key to reducing production losses. By automating repetitive tasks, you can eliminate human error, reduce cycle times and improve consistency. For example automated inspection can detect defects in real time so quality losses are addressed immediately. RPA can be used to streamline setup and changeover, reduce downtime. Automating will eliminate human error, reduce cycle times and improve consistency in your processes.

The Importance of Data-Driven Decision Making

OEE data gives you the insights into the root causes of production inefficiencies. Advanced analytics can find patterns and correlations that you may not see. For example if the data shows a pattern of reduced yield during certain shifts the root cause may be operator performance or environmental conditions. Fixing these with targeted interventions will give you measurable results. Advanced analytics can find patterns and correlations that you may not see.

Conclusion

The Six Big Losses gives you a framework to look at inefficiencies and impact OEE. By using this framework and tools like OEE data analysis, predictive maintenance and advanced automation you can improve overall productivity, quality and sustainability. By using this framework and tools you can improve overall productivity, quality and operational efficiency. And by creating a culture of continuous improvement and investing in employee training you can make these gains stick long term.

In today’s competitive manufacturing environment, understanding and mitigating the Six Big Losses is not merely an operational necessity but a strategic imperative.