Total Effective Equipment Performance (TEEP)

What is Total Effective Equipment Performance (TEEP)?

What is TEEP? Total Effective Equipment Performance is a manufacturing Key Performance Indicator (KPI) that asks the most unforgiving question in manufacturing: how productive is this equipment compared to every hour of time that exists — including nights, weekends, and public holidays? Not just the hours you scheduled it to run. All of them.

A TEEP of 100% means a machine ran perfectly, at full design speed, with zero defects, 24 hours a day, every day of the year. Nobody achieves that. But the gap between your actual TEEP and that theoretical ceiling is precisely what makes the metric useful — it tells you how much productive capacity your asset has that your business is not currently using.

The key distinction from Overall Equipment Effectiveness (OEE) — sometimes referred to as overall equipment efficiency — is the denominator. OEE measures how well your equipment performed during the time you scheduled it to run. TEEP measures how well it performed against all calendar time available — 8,760 hours per year. A factory running one 8-hour shift can have an OEE of 85% (excellent by most standards) while its TEEP sits at 17%. Both numbers are correct; they answer different questions.

This gap between OEE and TEEP reveals what lean manufacturing practitioners call the "hidden factory" — the productive capacity embedded in every idle overnight, every quiet weekend, every unscheduled holiday. The hidden factory is not a flaw in the equipment; it is unused calendar time that the business has not yet chosen to activate. Whether that capacity should be activated is a business decision. But you can't make that decision well without knowing the number.

OEE is the metric for production managers running today's shift. TEEP is the metric for operations executives and CFOs making decisions about next year's capital budget. As a manufacturing KPI, TEEP metric is unique in that it benchmarks equipment performance against the absolute maximum — not just against what was planned.

How to Calculate TEEP

The TEEP Formula

The TEEP formula expresses equipment performance as a product of four components:

TEEP = OEE × Utilization

Expanded to its four components:

TEEP = Availability × Performance × Quality × Utilization

Where:

• Availability = the fraction of planned production time the machine was actually running
• Performance = how fast it actually ran compared to its designed speed during runtime
• Quality = the fraction of output that passed quality checks
• Utilization = the fraction of all available calendar time that was scheduled for production

The Utilization sub-formula:

Utilization = Planned Production Time ÷ Total Calendar Time

Total Calendar Time = 8,760 hours/year (or precisely: hours in the measurement period × 24). This fixed denominator is what makes TEEP comparable across sites, asset types, and time periods.

Calculation Examples

Example 1 — Single-shift factory (the most common scenario)

A stamping press runs one 8-hour shift, 5 days a week, 50 weeks a year.

Step 1: Utilization

• Planned production time: 8 × 5 × 50 = 2,000 hours/year
• Total calendar time: 8,760 hours/year
• Utilization = 2,000 ÷ 8,760 = 22.8%

Step 2: OEE

• Availability: 200 hours of downtime → (2,000 − 200) ÷ 2,000 = 90.0%
• Performance: running at 85% of design speed → 85.0%
• Quality: 98% of parts passed inspection → 98.0%
• OEE = 0.90 × 0.85 × 0.98 = 74.9%

Step 3: TEEP

• TEEP = 74.9% × 22.8% = 17.1%

A TEEP of 17.1% is not a sign of dysfunction — it is completely normal for a single-shift operation. The Utilization ceiling for a standard 8-hour, 5-day schedule is approximately 24%, which means even a world-class OEE of 85% can only produce a TEEP of around 19%. Whether that represents a strategic opportunity depends entirely on whether demand exists to fill the remaining capacity.

Example 2 — Two-shift operation

A beverage filling line runs two 8-hour shifts, 7 days a week, 52 weeks a year.

Step 1: Utilization

• Planned production time: 16 × 365 = 5,840 hours/year
• Utilization = 5,840 ÷ 8,760 = 66.7%

Step 2: OEE

• Availability: 350 hours downtime → (5,840 − 350) ÷ 5,840 = 94.0%
• Performance: 88.0% of design speed
• Quality: 99.2% good product
• OEE = 0.94 × 0.88 × 0.992 = 82.0%

Step 3: TEEP

• TEEP = 82.0% × 66.7% = 54.7%

Notice what happened: this line's OEE (82%) is actually lower than the single-shift press (74.9% → rounded up: 75%), yet its TEEP is three times higher. The difference is entirely in Utilization. Adding shifts is the most powerful lever for improving TEEP — and it requires no maintenance investment at all.

Example 3 — Reverse-engineering a capacity decision

A plastics manufacturer needs to produce 4.2 million units/year. Their injection moulding machine has a design speed of 600 units/hour. They run one 8-hour shift, 250 days/year. Historical OEE: 72%.

Current capacity:

• Planned hours: 8 × 250 = 2,000 hours/year
• Actual output at 72% OEE: 2,000 × 600 × 0.72 = 864,000 units/year

The capacity gap:

• Demand: 4,200,000 units. Current capacity: 864,000 units. Gap: 3,336,000 units.

What it would take to meet demand on one machine without buying new equipment:

• Required runtime: 4,200,000 ÷ (600 × 0.72) = 9,722 hours/year
• Total calendar time available: 8,760 hours/year
• Required hours exceed available calendar time → one machine cannot meet demand at current OEE

This example shows TEEP's power as a planning tool. It tells you precisely whether a capacity gap can be closed through operational improvement and added shifts, or whether capital investment in additional equipment is genuinely necessary. In this case, the answer is unambiguous: they need a second machine.

TEEP vs. Related Metrics

TEEP sits at the top of a hierarchy of equipment performance metrics. Understanding TEEP vs OEE — and how both relate to Asset Utilization Rate — determines when to use each one.

A machine can have world-class OEE and a low TEEP simultaneously — and both numbers can be entirely appropriate. A hospital MRI scanner running at 92% OEE across a 10-hour operating day is performing exceptionally well. Its TEEP of 38% simply reflects that the scanner sits idle for 14 hours each day — a deliberate scheduling decision, not a performance failure.

The question OEE answers: How well are we running during the time we planned to run?

The question TEEP answers: How well are we using the full potential of this asset?

In manufacturing, OEE vs TEEP is not a choice between two competing metrics — they answer different questions and both belong in a complete equipment utilization picture.

Understanding the Four TEEP Components

Availability

Availability = (Planned Production Time − Downtime) ÷ Planned Production Time

Availability captures all time the machine was scheduled to run but didn't — unplanned breakdowns, emergency repairs, waiting for operators, material shortages. Planned maintenance windows are excluded from Planned Production Time entirely, which means scheduled shutdowns don't affect Availability — they reduce Utilization instead.

Example: A press is scheduled for 480 minutes in a shift. One breakdown costs 35 minutes; a material shortage costs 15 minutes. Total downtime: 50 minutes. Availability = (480 − 50) ÷ 480 = 89.6%.

World-class Availability is 90%+. Below 85% signals a maintenance reliability problem. Preventive maintenance and predictive maintenance are the primary levers for improving it — reducing both the frequency and duration of unplanned downtime events. Mean Time Between Failures (MTBF) tells you how often failures happen; Mean Time To Repair (MTTR) tells you how quickly you recover from them. Both feed directly into your Availability score.

Performance

Performance = Actual Output ÷ (Design Speed × Runtime)

Performance captures speed losses — running slower than rated speed, micro-stops (brief pauses too short to log as downtime), and idling. This is where some of the most significant and least visible losses hide. Equipment running at 70% of design speed all day, with no single stop long enough to trigger an alert, may not register as a problem in any other metric.

Example: A machine's design speed is 200 units/hour. Runtime after Availability losses is 430 minutes (7.17 hours). Actual output: 1,200 units. Theoretical maximum for that runtime: 200 × 7.17 = 1,433 units. Performance = 1,200 ÷ 1,433 = 83.7%.

World-class Performance is 95%+. Below 85% often indicates chronic speed throttling, frequent micro-stops, or a machine running in a degraded state. Log and track micro-stop events through work orders — even minor recurring stops that individually seem trivial can cumulatively cost more runtime than a single major breakdown.

Quality

Quality = Good Parts ÷ Total Parts Produced

Quality captures all output that cannot be shipped — scrap, defects discovered during or after production, and startup rejects (parts produced during warmup before the process stabilizes). Reworked parts count as Quality losses even if they are eventually shipped, because they consumed additional time and resources to produce.

Example: A machine produces 1,200 parts in a shift. 36 fail inspection — 28 scrapped, 8 reworked. Quality = (1,200 − 36) ÷ 1,200 = 97.0%.

World-class Quality is 99%+. Quality below 95% is a significant drag on both OEE and TEEP and typically points to process control issues, tooling wear, or raw material variation rather than a maintenance problem.

Utilization

Utilization = Planned Production Time ÷ Total Calendar Time

Utilization is the component that makes TEEP fundamentally different from OEE. It captures all calendar time when the machine was not scheduled to run — overnight hours, unworked shifts, weekends, holidays, and planned shutdowns. Unlike the other three components, Utilization is driven almost entirely by business decisions: how many shifts to run, how much demand exists, seasonal patterns, and strategic choices about asset deployment.

Example: A machine is scheduled for 2,000 hours/year (one 8-hour shift, 250 working days). Total calendar time: 8,760 hours. Utilization = 2,000 ÷ 8,760 = 22.8%.

This is a critical point: Utilization is largely outside the control of the maintenance team. Improving it requires either more market demand, a decision to add shifts, contract manufacturing to fill idle time, or reallocation of assets across sites. This is why TEEP manufacturing analysis is primarily a strategic tool for plant directors and operations leadership — not a day-to-day maintenance KPI. When used correctly, TEEP calculation reveals the true equipment utilization gap between scheduled capacity and total calendar potential. The Asset Utilization Rate metric covers the broader utilization concept for asset portfolios; TEEP formalizes it specifically for production equipment performance.

Who Needs TEEP and When

TEEP is a different kind of metric from OEE — primarily strategic rather than operational. The stakeholders who find it most useful tend to be further up the organizational hierarchy than those who use OEE day-to-day.

• Operations directors and plant managers — Quarterly, at budget cycles. Use TEEP to evaluate whether individual assets are being utilized in proportion to their capital cost, and to identify which machines have headroom before new equipment is justified. TEEP provides the data to take a capacity question to the CFO with confidence.
• CFOs and finance leadership — At capital expenditure decision points. Before approving a request for new production equipment, review the TEEP of existing assets in the same category. If a manufacturer requests funds for a second machine but the existing machine has a TEEP of 18%, the real question is whether adding a shift could close the capacity gap at a fraction of the cost. TEEP reframes every capital expenditure conversation around actual asset utilization, not assumed constraints.
• Maintenance managers — Monthly, as a context metric alongside OEE. Maintenance leaders primarily work with OEE, but tracking TEEP helps them understand how Availability losses affect the business's overall productive output — not just scheduled output. When Availability is 88%, the impact on TEEP (which uses calendar time as denominator) is proportionally more severe than the impact on OEE alone. This helps make the business case for maintenance investment in language that finance understands.
• Continuous improvement teams (Lean / Six Sigma) — At the start of improvement projects and quarterly thereafter. Lean focuses on removing waste and non-value-adding steps; Six Sigma is a data-driven methodology for eliminating defects and process variation. TEEP provides a "true north" for improvement initiatives. An OEE project that moves a metric from 68% to 75% is impressive in relative terms; the TEEP lens translates that into recovered calendar-time capacity and helps prioritize which improvements have the greatest strategic impact.
• Operations analysts and MES/ERP teams — Ongoing. Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) systems are typically the source of production schedule, runtime, and quality data that feed TEEP calculations. TEEP requires accurate data from multiple sources — production schedules, runtime and downtime logs, quality records — and must be calculated per asset, per line, and in aggregate. The teams who own manufacturing data systems are responsible for making TEEP visible, consistent, and trustworthy across the organization.

Real-World Examples

Example 1: The Hidden Shift — Avoiding a $1.2M CapEx Decision

A mid-size contract metal fabricator ran a laser cutting cell with two machines. As order volume grew, the operations manager recommended purchasing a third laser cutter at $600,000 per unit. Before approving the spend, the Chief Financial Officer (CFO) requested a TEEP analysis.

Current state:

Effective hours = OEE × Planned Hours. The plant needed 4,200 effective hours/year to meet demand. Current combined output: 2,900. Gap: 1,300 effective hours.

The alternatives:

The two-shift option on existing machines not only met the capacity target — it exceeded it by over 1,200 effective hours, creating room for further growth without additional investment.

Decision: The plant added a second shift. CapEx avoided: $1,200,000. Incremental operating cost: $110,000/year. TEEP improved from 16.5% to 31.0% per machine.

The TEEP analysis didn't tell the team to add a shift. It revealed that the capacity was already there — sitting idle every evening and weekend — waiting to be scheduled.

Example 2: When Good OEE Hides a Deeper Problem

A pharmaceutical packaging plant tracked OEE as their primary equipment performance KPI. Packaging Line 3 consistently reported OEE above 85%, which the plant manager considered excellent. When an operations analyst introduced TEEP into the reporting, a different picture emerged.

The numbers:

Line 3 was the plant's only line capable of running a high-margin specialty product. The plant had been turning away specialty product orders for months because "we don't have capacity." In reality, the line sat completely idle on Saturdays and Sundays — 104 days per year, 2,496 hours of unused calendar time.

What they changed:

The plant added a single Saturday production run dedicated to the specialty product, staffed at minimal levels (4 people vs. 12 on weekdays).

Results after 6 months:

OEE did not change because the quality of production during scheduled hours was already excellent. TEEP improved because the business made better use of the time the asset was available. "No capacity" turned out to mean "no scheduled capacity" — a business decision, not a physical constraint.

Example 3: TEEP in IT — Data Center GPU Cluster

TEEP is not exclusive to manufacturing. Any capital-intensive asset that sits idle for predictable periods can be analysed with the same framework. A managed IT services company operated a Graphics Processing Unit (GPU) compute cluster — 40 high-end nodes used for machine learning model training and rendering jobs for clients.

The situation:

The cluster was scheduled for use during standard business hours: 10 hours/day, Monday through Friday, 50 weeks/year. Jobs were queued and processed sequentially by the operations team during those windows. Outside of business hours the cluster sat powered down.

The cluster hardware cost $1.8M. At a TEEP of 22.4%, the company was extracting productive value from the asset for roughly 1,960 hours out of 8,760 available. Client demand for compute time was growing faster than budget allowed for new hardware.

What they changed:

Instead of approving a $900,000 expansion of the cluster, the IT director introduced automated overnight job scheduling — a queuing system that accepted low-priority rendering jobs from clients at a discounted rate and processed them between 22:00 and 08:00 automatically, without operator intervention.

Results after 4 months:

The cluster's physical performance (Availability, Performance, Quality) barely changed. TEEP more than doubled because Utilization improved — the same hardware now processed jobs around the clock instead of sitting idle every night and weekend. The overnight revenue stream covered the cost of the automation tooling within three months.

The TEEP framework translated a manufacturing concept directly into an IT asset decision: the cluster's "hidden factory" was 14 hours of overnight compute capacity, available every day, waiting to be scheduled.

Common Mistakes

1. Confusing TEEP with OEE. They measure different things and serve different audiences. OEE tells you how well the machine performed when it was scheduled to run. TEEP tells you how much of all possible time is being converted into productive output. Using one when you mean the other leads to wrong conclusions — particularly in capacity planning discussions where the distinction is everything.
2. Using TEEP to compare single-shift and multi-shift operations. A single-shift factory will always have a lower TEEP than a 24/7 operation, regardless of how well-run it is. Comparing TEEP across sites or asset categories with different shift structures is unfair and misleading. Compare within the same operating model, or normalize by Utilization to isolate OEE performance.
3. Setting TEEP improvement targets without considering market demand. TEEP can only increase if there is demand to fill the additional capacity. A TEEP target of 60% for a plant currently running one shift requires roughly tripling production volume. If the market isn't there, the target is meaningless — and pursuing it creates pressure to produce inventory nobody needs. Always anchor TEEP targets to demand forecasts.
4. Treating Utilization as a fixed constant. Many teams calculate TEEP once, note that Utilization is "just how we operate," and focus only on improving OEE. But Utilization is a strategic lever: adding a shift, running weekends, offering contract or toll manufacturing to fill idle time, or reallocating underused assets to higher-demand product lines are all real options. The hidden factory is real capacity.
5. Using the wrong denominator. Some teams calculate TEEP using "scheduled hours per year" rather than total calendar hours. This produces a number that is neither OEE nor TEEP — it's somewhere in between, and it's not comparable across periods or sites. TEEP's denominator is always the exact calendar hours in the measurement period (8,760 for a full year, or days × 24 for any other period).
6. Excluding planned downtime from Availability. Planned maintenance shutdowns, product changeovers, and tooling changes that occur within scheduled production time should be included in the Availability loss calculation. Teams that exclude them inflate their Availability — and by extension their OEE and TEEP — producing numbers that look better than reality.

How to Improve TEEP

Because TEEP = OEE × Utilization, improvement requires either raising OEE, increasing Utilization, or both. The right lever depends on which component is the binding constraint — and that diagnosis should come before any improvement initiative begins.

1. Diagnose first: decompose TEEP into OEE and Utilization. If Utilization is 22% (single shift, 5 days/week) and OEE is already 82%, the limiting factor is Utilization — no amount of maintenance improvement will move TEEP significantly. If Utilization is 65% and OEE is 58%, the maintenance and production team is where the opportunity lies. Split the problem before solving it.
2. Implement preventive maintenance to protect Availability. Every unplanned breakdown reduces Availability, which reduces OEE, which reduces TEEP. A structured PM program typically improves Availability by 5–15 percentage points within the first year, depending on baseline conditions. This is the most direct maintenance-side lever for improving TEEP.
3. Use predictive maintenance to catch failures before they cause downtime. Predictive techniques shift failures from unplanned breakdown mode into planned intervention mode — maintenance occurs in scheduled windows that don't consume production time. Every unplanned failure avoided is runtime recovered for Availability.
4. Evaluate shift expansion against the demand case. If Utilization is below 40% and market demand exists to justify it, model the economics of adding a shift. The incremental cost of an additional production shift is almost always a fraction of the cost of new equipment that would provide the same additional capacity. Use the format from Case 1 above to build the business case.
5. Investigate and eliminate chronic speed losses to improve Performance. Many machines are throttled below rated speed because an operator discovered years ago that full speed caused quality problems — problems that may have since been resolved. Audit whether equipment is actually running at design speed. Recovering speed is free capacity that requires no capital investment and no additional headcount.
6. Reduce changeover time to increase effective Utilization. Product changeovers and tooling changes consume planned production time without producing output. Applying Single-Minute Exchange of Die (SMED — a lean manufacturing method for minimising changeover time) or other changeover reduction techniques reduces non-productive scheduled time — effectively improving Availability and Utilization simultaneously.
7. Consider contract or toll manufacturing to fill idle calendar time. If a high-value asset has a low TEEP and internal demand isn't sufficient to justify additional shifts, one option is offering the asset's capacity to other manufacturers on a contract basis. This improves TEEP, generates revenue from otherwise idle equipment, and turns the "hidden factory" into a measurable business asset.

Best Practices

1. Calculate TEEP separately for each asset and operating context. A single TEEP number for an entire plant is nearly meaningless. Calculate it per machine, per line, and per asset category. The gaps between assets reveal the most actionable information — a bottleneck machine with a TEEP of 15% sitting next to a non-bottleneck with a TEEP of 24% is worth investigating before buying anything new.
2. Track TEEP quarterly, not daily. OEE is a daily or weekly metric — it tells you how a shift went. TEEP is a quarterly or monthly metric — it tells you about strategic capacity utilization patterns over time. Daily TEEP figures are too noisy to be useful. Quarterly reviews smooth the signal and reveal genuine trends. Pair every TEEP report with demand data from the same period.
3. Always show TEEP alongside OEE, not in isolation. TEEP without OEE is incomplete. A TEEP of 30% could mean OEE is excellent but Utilization is low (good performance, limited hours) or OEE is poor and Utilization is moderate (bad performance, moderate hours). The ratio of TEEP to OEE — which equals Utilization — tells you immediately which problem you're actually looking at.
4. Require a TEEP analysis in every CapEx request involving production equipment. Before any capital request for new production equipment reaches the approval stage, require a current TEEP analysis of existing assets in the same category. If TEEP is below 35% for a single-shift operation, the first question should be: "Could we add a shift instead?" Make this a standing item in the CapEx approval template. This practice alone can save significant capital expenditure annually.
5. Benchmark TEEP relative to your operating model, not abstract world-class targets. A world-class TEEP of 85% is achievable only in continuous 24/7 operations with exceptional OEE. For a five-day single-shift manufacturer, a realistic improvement target might be moving from 18% to 28% TEEP — which represents a 55% increase in productive output from the same assets. Set targets that reflect your operating context, not targets designed for a different kind of operation.
6. Automate the data inputs that feed TEEP. TEEP requires accurate data from three sources: production schedules (for Planned Production Time), runtime and downtime logs (for Availability and Performance), and quality records (for Quality). Manual entry into any of these streams will corrupt your TEEP calculation over time. Automate data capture through runtime sensors, production tracking systems, and quality management tools, and audit the inputs quarterly to catch drift before it invalidates your reporting.

Related Terms

• Mean Time Between Failures (MTBF) — MTBF drives TEEP's Availability component; equipment that fails less frequently has more runtime available, directly improving both OEE and TEEP
• Mean Time To Repair (MTTR) — MTTR determines how quickly Availability is recovered after a failure; faster repairs mean less downtime deducted from planned production time
• Predictive Maintenance — Shifts failures into planned repair windows, protecting Availability and recovering runtime that would otherwise be lost to unplanned breakdown
• Preventive Maintenance — The foundational maintenance strategy for keeping Availability consistently high and minimizing unplanned downtime events
• Asset Utilization Rate — The broader asset utilization concept that TEEP formalizes specifically for production equipment, measured against calendar time
• Work Order — Every downtime event captured in TEEP's Availability calculation should be recorded through a work order, creating the data trail needed to calculate, audit, and improve the metric
• Total Cost of Ownership — A machine with low TEEP has a high TCO per unit of output; the same asset costs far more per part produced at 20% TEEP than at 60%, because fixed ownership costs are spread over far fewer units

Conclusion

Total Effective Equipment Performance (TEEP) holds your assets to the most demanding standard possible: every hour that exists. A TEEP of 25% is not a failure — for a single-shift operation with limited demand, it might be exactly right. But it also means there are 6,570 hours of calendar time each year when that machine could be producing and isn't. Whether closing that gap is worthwhile is a business decision, not a maintenance decision. TEEP's unique value is that it makes this conversation concrete and numerical. Instead of debating whether to buy a new machine or add a shift, you can calculate exactly what each option delivers, at what cost, against what demand. That is the kind of strategic clarity that prevents million-dollar capital expenditure mistakes — and sometimes reveals that the factory you need is already sitting right in front of you, waiting to be scheduled.