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Machine data from the first weeks of measurement regularly reveals that a large proportion of factories operate at 60\u201365% of their actual capacity \u2014 a gap that many production managers discover with surprise. This is not a failure of the teams: it is simply that no indicator was making these losses visible. OEE (Overall Equipment Effectiveness), or TRS in French, is precisely that indicator. In a single number, OEE condenses what your line actually produces relative to what it could produce under ideal conditions.<\/p>\n
Overall equipment effectiveness is both a diagnostic tool and a decision-making lever. By the end of this article, you will know how to calculate your OEE component by component, interpret your score according to your sector, identify priority losses and choose the methods to correct them.<\/p>\n
OEE does not measure a single aspect of production. It simultaneously captures three dimensions: the time during which your machine is actually running, the speed at which it produces, and the proportion of conforming parts it generates. This combination is what makes it a unique KPI. A machine that is available but running at reduced speed shows a degraded OEE. So does a fast machine generating 10% rejects.<\/p>\n
This synthetic nature distinguishes OEE from isolated metrics. The breakdown rate alone does not tell you whether your machine compensates in speed or produces defects. The reject rate alone does not tell you whether your line is available long enough. Overall equipment effectiveness is the only indicator that gives a complete reading of an equipment’s performance in a single number.<\/p>\n
A score of 65% immediately tells you that 35% of capacity is lost. But the three components that make it up indicate exactly where those 35% disappear. Is it in unplanned stoppages? In invisible slowdowns? In manufacturing defects? Each answer calls for a different action. This is why this KPI advantageously replaces a stack of partial dashboards that often lead to poorly targeted priorities.<\/p>\n
The complete formula is: OEE = Availability \u00d7 Performance \u00d7 Quality<\/strong>. Each component has a precise operational meaning. The availability rate measures the ratio between actual running time and planned time. The performance rate compares actual production to theoretical production. The quality rate calculates the proportion of conforming units out of the total produced. For a deeper look at the calculation method component by component, see our practical guide on how to calculate and improve OEE<\/a>.<\/p>\n Concretely: if your machine was scheduled to run for 8 hours but was stopped for 1 hour due to a breakdown, your availability is 87.5%. If your target rate is 600 units per hour but you only produce 500, your performance rate is 83.3%. If you count 200 rejects out of 4,000 parts produced, your quality rate is 95%. Understanding the physical meaning of each ratio is more useful than memorising the formula.<\/p>\n Let us take a standard production day with these figures: 8 hours planned, 1 hour stopped for breakdown, actual speed of 500 units\/hour versus 600 in theory, and 200 rejects out of 4,000 parts produced. The calculation gives: availability rate 87.5%, performance rate 83.3%, quality rate 95%.<\/p>\n OEE = 0.875 \u00d7 0.833 \u00d7 0.95 = 69.5%<\/strong><\/p>\n This score of 69.5% is not an abstract grade. It reveals three distinct losses with their respective causes: 12.5% of capacity lost due to unplanned stoppages, 16.7% due to insufficient speed, and 5% due to rejects. Each calls for a different action: preventive maintenance for availability, parameter adjustment for performance, quality control for defects.<\/p>\n A particularly widespread bias in workshops consists of including planned stoppages (scheduled maintenance, cleaning, planned changeovers) in the available time. This crushes the availability rate and masks the reality of actual breakdowns. Planned time must be the time actually scheduled for production, with unplanned stoppages excluded.<\/p>\n A second trap concerns multi-product lines: using a single theoretical speed artificially inflates performance on short runs. The best practice is to define a theoretical cycle time per product reference. Finally, rework is often ignored in the quality calculation. A part reworked before shipment has consumed additional time and resources: it cannot count as good at first pass, just like a direct reject.<\/p>\n A score of 85% is considered world class in discrete manufacturing, particularly in automotive and electronics. In practice, only a minority of sites reaches this level \u2014 an analysis of 2025 industrial benchmarks estimates this proportion at around 9% of manufacturing establishments. The aerospace sector shows an average of around 64.7%, reflecting the complexity of configurations and the frequency of changeovers. Below 65%, whatever your industry, significant levers remain to be activated.<\/p>\n The worldwide average for overall equipment effectiveness is around 60\u201365%, according to the same benchmark data. This means an ordinary factory leaves one third of its theoretical capacity on the table, without needing new machines to recover it. To benchmark against international references, see our analysis of worldwide OEE benchmarks<\/a>.<\/p>\n TPM (Total Productive Maintenance) has formalised six major categories of losses, mapped directly onto the three OEE components. Breakdowns and setup\/changeover time attack the availability rate. Micro-stoppages and slowdowns degrade the performance rate. Process rejects and start-up losses weigh on the quality rate. For a detailed description of their operational impact, see our article on the six big losses in production<\/a>.<\/p>\n Performance losses are the most dangerous precisely because they are the most invisible. A 30-second micro-stoppage alerts no one. But if it repeats 40 times per shift, it represents 20 minutes of lost production. To illustrate: across 5 lines running 250 days per year at this rate, that is nearly 25 days of capacity evaporating without ever triggering an alarm. This is what automatic machine signal monitoring detects where the human eye sees nothing.<\/p>\n Total Productive Maintenance involves all teams, operators included, in the monitoring and preventive upkeep of equipment. The approach begins with a Pareto audit of critical equipment: identifying the 20% of machines causing 80% of stoppages. A structured maintenance plan is then put in place, with defined frequencies and result tracking. Preventive maintenance reduces unplanned stoppages, which represent 20 to 50% of total losses depending on the site.<\/p>\n SMED (Single-Minute Exchange of Die) aims to convert internal operations (performed with the machine stopped) into external operations (performable with the machine running), then to standardise what remains. The analysis begins with a filmed observation of the changeover to identify and reclassify each task. Documented case studies in SMED literature show changeover time reductions of 30 to 50%, sometimes more, on well-analysed lines.<\/p>\n This method is particularly relevant for workshops with short runs or a high number of references, where changeovers can represent 15 to 25% of planned time. Every minute saved on a changeover translates directly into additional availability rate.<\/p>\n The kaizen loop applied to OEE follows four steps: measure, analyse, act, verify. Shop floor engagement is decisive. A daily 15-minute meeting on the previous day’s results, supported by readable operator dashboards, allows teams to take ownership of the data and propose corrective actions. Combining TPM and SMED with rigorous OEE tracking delivers gains of 10 to 20% in production capacity without investment in new machines.<\/p>\n The classic scenario is well known: the operator notes stoppages at the end of the shift, the manager consolidates at the end of the week, and the decision comes several days after the deviation. Manual entries introduce systematic errors and unconscious biases; short micro-stoppages are rarely recorded. An OEE calculated retroactively from this data describes the past approximately, but does not drive the present. When data arrives too late, root causes have already disappeared and teams are debating memories rather than facts. To understand how continuous monitoring changes the game, see our article on real-time OEE monitoring<\/a>.<\/p>\n TEEPTRAK is an industrial performance monitoring solution deployed in many French and international factories, including Hutchinson, Safran and Nutriset. The platform connects directly to equipment to collect machine data in real time. Depending on the context and the machine fleet, deployment can be carried out without manual data entry and with minimal IT integration. The approach follows four concrete field steps: machine connection, automatic signal collection, operator dashboard set-up and deployment support.<\/p>\n Result: teams see losses as they occur, not the following morning at the production meeting. A recurring micro-stoppage becomes visible within hours, not weeks. Managers have a reliable OEE to prioritise their decisions with confidence.<\/p>\n Factories switching from manual to automated real-time monitoring observe OEE gains of 10 to 15% within a few months. Nutriset documented a 29% efficiency improvement in the first month after deploying a real-time monitoring solution. In the automotive sector, each OEE point gained can represent between \u20ac100,000 and \u20ac300,000 in additional capacity per year, depending on the size of the installation and the volume produced.<\/p>\n The gain does not come from the tool itself. It comes from the ability to make decisions based on reliable data rather than assumptions. Losses exist in every factory: the difference between those that progress and those that stagnate is simply the ability to see them.<\/p>\n OEE measures the overall effectiveness of your equipment by combining availability, performance and quality. A low score tells you exactly where to look: in unplanned stoppages, in invisible slowdowns or in manufacturing defects. Structured methods such as TPM and SMED make it possible to correct each type of loss in a targeted way, with documented gains of 10 to 20% without investment in new machines. For a complete definition and educational resources, see our guide on what is OEE (Overall Equipment Effectiveness)<\/a>.<\/p>\n The first action to take is not to invest in a method or a tool. It is to measure your current OEE rigorously, component by component, to find out where your losses are hiding. Once this foundation is established, everything else becomes actionable.<\/p>\n If you want to move from manual calculation to automatic real-time monitoring on your lines, TEEPTRAK supports you from machine connection to the first operator dashboards, in 4 field steps designed for production teams.<\/p>\nConcrete Example: An 8-Hour Day Broken Down<\/h3>\n
Calculation Errors That Distort Results<\/h3>\n
Reading Your Score: Sector Benchmarks and the Six Big Losses<\/h2>\n
What Is a Good OEE Score for Your Sector?<\/h3>\n
The Six Big Losses: Identifying Where Your Capacity Goes<\/h3>\n
Proven Methods to Improve OEE<\/h2>\n
TPM and Preventive Maintenance: Attacking Availability Losses<\/h3>\n
SMED: Reducing Changeovers to Gain Availability<\/h3>\n
Measuring Overall Equipment Effectiveness: Turning Data into an Action Plan<\/h3>\n
Why Real-Time Monitoring Transforms OEE into a Decision-Making Lever<\/h2>\n
The Limits of Excel Tracking: A Delay That Costs Dearly<\/h3>\n
How TEEPTRAK Connects Machines and Makes OEE Actionable<\/h3>\n
Concrete Results: What Factories Have Measured After Deployment<\/h3>\n
Conclusion: Start by Measuring, the Rest Will Follow<\/h2>\n