Measuring machine performance and identifying issues can inform decision-making for upgrades. This can help improve product quality and cost, as well as enhance manufacturing line performance.
Overall equipment efficiency (OEE) is the measurement of performance of manufacturing equipment used in industrial production facilities to measure their effectiveness. The efficiency of manufacturing equipment is the ratio of successful work completed by the process to the total work which could be completed by the process. The combined equipment efficiencies can be utilized to assess the overall production line. This assessment will show how well the entire manufacturing process is performing. This production-line perspective is considered the overall machine efficiency, as the efficiency is taken from a holistic performance standpoint.
The OEE percentage can be derived from a lot of key performance indicators across three main attributes: available production time, process uptime and output quality.
• Available production time: This factor measures the actual production time of the equipment relative to the planned production time. It considers any downtime or stoppages due to various reasons like equipment breakdowns, changeovers and scheduled maintenance. The formula for availability is: Availability = total time equipment is operating in a specific shift / total time available in a specific shift.
• Process uptime: This factor measures the speed or efficiency at which the equipment is running during the production time. It considers factors like the actual cycle time compared to the ideal cycle time and any minor stoppages or slowdowns that may occur during production. Please note that the actual cycle time should be measured from stable processes. Additionally, cycle times should be taken over sample groups larger than 30, where the mean cycle time represents the actual time. The formula for performance is: Process uptime = target cycle time per SKU / mean cycle time per SKU.
• Output quality: This factor measures the percentage of good-quality products produced by the equipment. It considers the number of defects or non-conforming products produced during production. The formula for quality is: Output quality = number of good parts produced per shift / total number of parts produced per shift.
The overall equipment efficiency (OEE) is calculated by multiplying the three factors: OEE = availability * performance * quality.
A perfect OEE score would be 100%, indicating that the equipment is operating at its maximum potential with no downtime, while running at the perfect speed, and producing quality products. However, achieving a 100% OEE is the target, and OEE is often used as a performance metric to identify areas for improvement in equipment usage and production processes. By tracking OEE over time, businesses can identify inefficiencies and implement targeted improvement projects to enhance equipment performance and overall productivity.
Ensuring OEE score accuracy
Operators or plant managers may falsely calculate the OEE score when they fail to understand the reasons behind a machine's low productivity or when they do not collect accurate data on machine downtime. For example, they may mistakenly attribute a machine's poor performance to the machine itself, without considering external factors like upstream or downstream machines not running. This can lead to misleading OEE scores.
To identify incorrect calculations, operators should regularly review and analyze the data collected, comparing the OEE score with other relevant metrics. Implementing reliable data collection systems, using standardized procedures for data collection and analysis and providing training on OEE concepts can help ensure accurate measurements. By taking these steps, operators can make informed decisions to improve machine performance and overall productivity.
What can affect an OEE score?
Many factors can cause low OEE metrics in machines, and identifying the root cause is paramount for achieving target OEE numbers. Some of the leading causes of downtime in machines may include the following:
• Equipment malfunctions—Mechanical failures, electrical issues and breakdowns in machine components can lead to significant downtime. Lack of proper maintenance, wear and tear or aging equipment can contribute to these malfunctions.
• Changeovers and setups—When switching between different products or production processes, changeover and setup times can lead to downtime. Reducing changeover times through efficient procedures can help minimize these interruptions.
• Quality issues—Production stoppages can occur when machines produce defective or out-of-spec products. This leads to rework or the need to recalibrate equipment, as well as wasting raw materials, creating scrap.
• Software or control-system errors—In automated systems, software glitches or control-system malfunctions can cause machine downtime.
• Tooling and equipment changeover—Downtime can occur when changing worn-out tooling, fixtures or other equipment required for the manufacturing process.
• Human error—Operator errors, incorrect settings and improper handling of machinery can lead to downtime. Error-proofing techniques can mitigate human-related downtime causes.
• Material shortages—Running out of raw materials or components required for production can cause idle machines. Effective inventory management and supply chain coordination are essential to avoid these situations.
To address downtime, it is essential to implement: effective preventive maintenance, increase equipment reliability, optimize changeover procedures and have contingency plans in place for potential issues that may arise during production.
Tracking key performance indicators, such as OEE, during events can help track down the root causes. Of course, this process is evergreen, as manufacturing facilities change processes due to compliance or consumer demands almost continuously.
Overall equipment efficiency (OEE) is a crucial metric for measuring the performance of manufacturing equipment in industrial production facilities. By considering factors such as availability, process uptime and output quality, OEE provides a holistic view of machine efficiency.
While a perfect OEE score of 100% is the ideal, businesses often use OEE as a performance metric to identify areas for improvement. Accurate measurement of OEE requires reliable data collection systems and analysis procedures.
Factors such as equipment malfunctions, changeovers, quality issues and human error can affect OEE scores. Addressing these factors through preventive maintenance, optimized procedures and contingency plans can help improve machine performance and overall productivity.