In modern manufacturing, optimizing productivity and efficiency is paramount to success. One crucial metric that serves as a cornerstone in assessing manufacturing performance is overall equipment effectiveness (OEE). At its core, OEE in manufacturing provides a comprehensive assessment of efficiency by considering three key performance indicators (KPIs) related to equipment performance.
Availability: This measures the proportion of time that equipment is available for production, taking into account downtime due to equipment failure, changeovers, and scheduled maintenance.
Availability = uptime / scheduled run time.
Performance: This evaluates the speed at which equipment operates compared to its maximum potential speed. It takes into account factors like idling and minor stoppages.
Performance = throughput / ideal cycle time.
Quality: This assesses the rate of production of defect-free units, reflecting the effectiveness of the manufacturing process in meeting quality standards.
Quality = acceptable units / total units.
Organizations calculate OEE to identify areas for improvement, optimize production processes, and maximize equipment utilization. It involves a formula that considers the three previously outlined components: availability, performance, and quality.
Each component is expressed as a percentage, ranging from 0% to 100%, with higher values indicating greater efficiency and productivity. A good OEE score varies depending on the industry, equipment type, and specific manufacturing processes.
The formula for OEE is:
(Availability) x (Performance) x (Quality)
According to the OEE Foundation, the OEE industry standard for manufacturing processes is 85 percent or higher. However a score of 60% or lower indicates that there are inefficiencies in the production process.
This assessment enables manufacturers to track performance over time, set benchmarks, and drive continuous improvement. In this article, we’ll uncover everything you need to know about OEE’s significance in manufacturing, the inefficiencies that cause a low OEE score, and how to improve your performance in relation to low-performing KPIs.
While both overall equipment effectiveness (OEE) and overall process effectiveness (OPE) are metrics used to gauge manufacturing efficiency, they differ slightly in scope and focus. OEE assesses the efficiency of individual manufacturing equipment or machinery within a facility, considering factors like availability, performance, and quality to assess performance.
For instance, we can look at a robotic assembly line in a manufacturing plant. Here, OEE would measure:
Its availability by tracking the time it is operational versus scheduled production hours.
Its performance by comparing its actual cycle time to its ideal cycle time.
Its quality by monitoring the number of correctly assembled products versus defective ones.
On the other hand, OPE evaluates the efficiency of the entire manufacturing process, taking into account factors beyond individual equipment such as material flow, workforce productivity, and process design. If we look at the same manufacturer, for example, OPE would measure things like how machines are performing during production, how effective current workflows are in the facility, and how efficient workers are at fulfilling orders. For instance, when it comes to labor, a study conducted by Shappell and Depar found that 70% of mistakes in manufacturing are human-related.
Furthermore, both OEE and OPE accounts for total effective equipment performance (TEEP), which focuses on how well individual machines perform, yet OPE looks at a wider picture. It includes things like total productive maintenance (TPM), which manages maintenance for the entire manufacturing process, including how resources are used and how workflows can be improved.
When evaluating OEE, there are six key losses that can indicate a low score.
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The Six Big Losses |
Affected OEE Metric |
Potential Causes |
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Equipment Failure |
Availability |
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|
Setup and Adjustments |
Availability |
|
|
Idling and Minor Stops |
Performance |
|
|
Reduced Speed |
Performance |
|
|
Process Defects |
Quality |
|
|
Reduced Yield |
Quality |
|
The first being equipment failure, which leads to downtime and production delays. Equipment failures can be caused by a lot of different factors, including lack of maintenance leading to equipment deterioration, human error, electrical issues, and environmental conditions. This directly impacts overall equipment effectiveness (OEE) by reducing availability, decreasing performance, and potentially affecting product quality. In fact, equipment failures account for 42% of unplanned downtime costs, costing organizations upwards of $50 billion per year.
In addition, downtime for repairs can eat up 1-10% of available production time, disrupting production schedules, leading to lost productivity and decreased efficiency. This ultimately lowers OEE scores and impacts overall manufacturing performance.
Equipment setup and adjustments capture the time required to prepare machinery or equipment for a new production run or to make adjustments during ongoing operations. They are therefore a part of planned downtime and can accrue up to $5.6 million in unplanned costs, leading to reduced performance and OEE. Some factors that cause downtime include setting up a new machine and adjusting calibrations, introducing new products causing changeovers, and changing quality regulations.
This often results in decreased equipment availability and output, which can cause production to either slow-down or stop completely to accommodate setup or adjustment tasks.
Idling and minor stops include brief interruptions or minor malfunctions, from setups to equipment malfunctions, that cause equipment to stop temporarily or operate at a reduced pace. As you can imagine, this contributes to decreased OEE. Minor interruptions, like equipment repairs or experiencing a shortage of raw materials necessary for production, can disrupt the flow of production, resulting in lost production time and decreased efficiency. For instance, a textile factory in Bangladesh found that 89.3% of production losses were caused by idling and minor stops.
Whether due to mechanical issues or operational constraints, when equipment operates at slower-than-expected pace, it leads to slower production rates. Factors contributing to reduced speed include equipment deterioration from a lack of part lubrication, misalignment of components and malfunctions, and material property fluctuations. This results in longer cycle times and decreased output, ultimately lowering OEE scores and affecting overall manufacturing efficiency and productivity.
Process defects, such as surface imperfections and dimensional variations, can significantly impact OEE by affecting product quality. This can be caused by anything from human error to improper process parameters that occurred during any phase of the production process.
Defective units require rework, disposal, or customer dissatisfaction, leading to decreased production output and increased downtime for corrective actions. With 73% of consumers relating customer experience to product quality, it’s essential for manufacturers to maintain quality standards.
Lastly, lower yield rates occur when equipment produces much less product than anticipated. This can be caused by any of the five losses in addition to supply chain disruptions and low productivity, indicating inefficiencies in production and leading to wasted resources and decreased overall equipment effectiveness. In fact, lower productivity rates from workers can cost organizations $1.2 trillion a year. As a result, maximizing yield is crucial for improving OEE scores, optimizing performance, and improving worker productivity.
In addition to the six big losses, there are three common OEE errors manufacturers should avoid:
Inadequate data collection methods: Inaccurate or incomplete data can distort OEE calculations, leading to misleading insights and ineffective decision-making. Employing reliable data collection systems and standardized measurement techniques is essential for accurate OEE analysis.
Lack of alignment between OEE metrics and business goals: Focusing solely on OEE metrics without considering the impact on overall business objectives can result in misaligned priorities and suboptimal resource allocation. Aligning OEE improvement efforts with strategic business goals ensures meaningful outcomes and sustainable performance gains.
Neglecting proactive maintenance practices: Failure to prioritize preventive maintenance and equipment reliability initiatives can lead to increased downtime, decreased OEE scores, and higher operational costs. Incorporating proactive maintenance strategies, such as Total Productive Maintenance (TPM), helps mitigate equipment failures and optimize OEE performance.
Two ways in which organizations can avoid these errors and leverage OEE effectively are automation and OEE software.
Implementing automation technologies such as robotics, conveyor systems, and automated material handling equipment can streamline production processes and minimize downtime. By automating repetitive tasks, automation reduces manual intervention and optimizes equipment utilization, leading to higher OEE scores. Additionally, automation enables real-time monitoring and analysis. By extracting real-time data insights on equipment performance, organizations can make proactive, data-informed decisions.
Take, for instance, National Oilwell Varco (NOV), a prominent oil and gas equipment manufacturer, who integrated 60 CNC (computer numerical control) machines to automate existing equipment. By doing this, they accessed real-time data on equipment performance, enabling OEE calculations and offering insight into existing inefficiencies. Leveraging these insights, NOV was able to increase their operational efficiency by an impressive 20% in just three months.
The second strategy includes leveraging OEE software solutions to improve OEE scores. OEE software, such as solutions offered by like Wonderware and L2L, offer real-time monitoring and analysis of equipment performance metrics. For example, OEE software can track machine downtime, analyze production losses, and provide actionable insights for optimizing equipment and minimizing disruptions. Plus, when integrated with lean manufacturing principles, OEE software can identify areas of improvement to help eliminate waste and optimize resource utilization.
As mentioned, automation is a key solution to improving OEE in manufacturing. At Cyngn, our DriveMod autonomous vehicles can be integrated into existing manufacturing processes to improve efficiency, minimize downtime, and optimize equipment utilization. This allows manufacturers to achieve a high OEE score by maximizing equipment availability, performance, and quality. In fact, our DriveMod vehicles were shown to increase efficiency by 4x.
Additionally, real-time data collection and analysis from our vehicles help organizations extract business insights and track key metrics such as battery life, location, and cycle time. This enables manufacturers to identify inefficiencies and continuously optimize their production processes for maximum OEE.