OEE vs TEEP: What’s the Difference, How to Calculate, and When to Use

TL;DR

• 🎯 OEE measures how well equipment performs during scheduled production time; TEEP measures effectiveness against all calendar time (24/7/365)
• 🧮 Both use Availability × Performance × Quality, but TEEP’s Availability includes evenings, weekends, and planned downtime
• 📊 A line can post 85% OEE yet deliver only 30% TEEP if it runs just one shift
• 🔧 Use OEE to diagnose execution losses during scheduled hours
• 💰 Use TEEP to validate capacity, justify shift additions, and answer “do we really need another machine?” before writing the capex check

OEE (Overall Equipment Effectiveness) tells you how effectively a piece of equipment runs during its planned production time: the hours you scheduled it to produce. TEEP (Total Effective Equipment Performance) measures effectiveness against all time, every hour of the week, whether you’re running production, conducting maintenance, or the plant is dark on a Sunday.

Both metrics share the same three-factor structure: Availability × Performance × Quality. The critical difference lies in the denominator of the Availability calculation. OEE uses scheduled (planned production) time; TEEP uses total calendar time.

The distinction matters because a single-shift operation can post world-class OEE scores, demonstrating flawless execution during planned hours, while leaving two-thirds of each week’s capacity unused. Operations leaders rely on OEE to drive daily improvements: faster changeovers, fewer breakdowns, less scrap. Senior leadership and plant planners turn to TEEP when the question shifts to capacity investment: Should we add a second shift? Buy another line? Automate to run lights-out?

This article walks through the formulas, a worked example, benchmark ranges, and a practical decision framework so you know which metric answers which question and how to improve both without gaming the numbers.

OEE vs TEEP: the 30-second definition

OEE (Overall Equipment Effectiveness) tells you how effectively a piece of equipment runs during its planned production time: the hours you scheduled it to produce. TEEP (Total Effective Equipment Performance) measures effectiveness against all time, every hour of the week, whether you’re running production, conducting maintenance, or the plant is dark on a Sunday.

Both metrics share the same three-factor structure: Availability × Performance × Quality. The critical difference lies in the denominator of the Availability calculation. OEE uses scheduled (planned production) time; TEEP uses total calendar time.

Why does this distinction matter? Because a single-shift operation can achieve world-class OEE scores, proving flawless execution during the day shift, while leaving two-thirds of each week’s capacity on the table. Operations leaders rely on OEE to drive daily improvements: faster changeovers, fewer breakdowns, less scrap. Senior leadership and plant planners turn to TEEP when the question shifts to capacity investment: Should we add a second shift? Buy another line? Automate to run lights-out?

This article walks through the formulas, a worked example, benchmark ranges, and a practical decision framework so you know which metric answers which question and how to improve both without gaming the numbers.

The time model: scheduled time vs all time

The confusion between OEE and TEEP vanishes once you understand the time layers that make up a week of operations.

Calendar Time (All Time) is the total time available: 168 hours per week or 8,760 hours per year. Every minute counts, whether your plant is running, closed for the weekend, or down for annual maintenance.

Shift Time (Operating Time) is the subset of calendar time during which you staff the facility and intend to operate. For a single five-day, eight-hour shift, that’s 40 hours per week. For continuous operations, it equals calendar time.

Planned Production Time is shift time minus planned stops: scheduled maintenance windows, planned changeovers, team meetings, breaks. This is the denominator for OEE’s Availability.

Why TEEP is usually lower than OEE

Because TEEP divides actual productive time by a much larger number (all 168 hours), it will almost always be lower than OEE, unless you run continuous operations at near-perfect effectiveness. A two-shift, five-day schedule caps theoretical maximum TEEP at 48%, even if OEE during those shifts is 100%.

This is not a flaw. A high OEE with a low TEEP signals underutilized calendar capacity: a strategic choice (one shift may be the right labor-cost tradeoff) or a hidden opportunity (you could add Saturday shifts to clear a backlog without buying new equipment).

Where OOE fits

Some organizations track OOE (Overall Operations Effectiveness), which uses shift time (operating time) as the Availability denominator. OOE sits between OEE and TEEP: broader than scheduled production time, narrower than calendar time. It’s useful when you want to include unscheduled breaks or minor stoppages that fall outside formal “planned production” but still occur during staffed hours. For most teams, OEE and TEEP provide enough clarity; add OOE only if your definitions of planned vs shift time create a meaningful gap worth tracking.

Formulas: OEE vs TEEP

Both metrics multiply three factors, Availability, Performance, and Quality, but the Availability denominators differ.

OEE formula

OEE = Availability × Performance × Quality

Where:

• Availability = (Actual Production Time) / (Planned Production Time)
  Planned Production Time = Shift Time − Planned Stops (scheduled maintenance, breaks, changeovers)

• Performance = (Total Count × Ideal Cycle Time) / (Actual Production Time)
  Or equivalently: (Actual Throughput) / (Theoretical Maximum Throughput)

• Quality = (Good Count) / (Total Count)
  Good Count excludes scrap, rework, and startup rejects

TEEP formula

TEEP = Availability × Performance × Quality

Where:

• Availability = (Actual Production Time) / (All Time / Calendar Time)
  All Time is typically 24 hours/day, 7 days/week = 168 hours/week or 8,760 hours/year.

• Performance and Quality remain identical to the OEE calculation.

Alternative representation: TEEP = OEE × Utilization

If you define Utilization = (Planned Production Time) / (All Time), then:

TEEP = OEE × Utilization

This shortcut is valid when your plant uses consistent definitions. It breaks down if “planned production time” varies by line or site, or if different teams classify planned vs unplanned stops differently. Always validate denominators before relying on the multiplication shortcut.

Short glossary

• Runtime (Actual Production Time): Time the equipment was running and producing parts.
• Downtime: Time equipment was unavailable, split into planned (scheduled maintenance, changeovers) and unplanned (breakdowns, material shortages).
• Ideal Cycle Time: Theoretical fastest time to produce one unit under perfect conditions.
• Total Count: All units produced, including good and defective.
• Good Count: Units that meet quality standards on the first pass.

Worked example: how a great OEE can still mean low TEEP

Let’s walk through a one-week scenario for a packaging line running a single day shift, Monday through Friday.

Example inputs

• Calendar Time (All Time): 168 hours (7 days × 24 hours)
• Shift Time: 40 hours (5 days × 8 hours)
• Planned Production Time: 36 hours (40 hours shift − 4 hours planned breaks/maintenance)
• Actual Production Time (Runtime): 32.4 hours (36 − 3.6 hours unplanned downtime)
• Ideal Cycle Time: 30 seconds per unit (120 units/hour)
• Total Count: 3,744 units
• Good Count: 3,670 units

Calculate OEE step-by-step

Availability = 32.4 hours / 36 hours = 0.900 (90.0%)

Performance = (3,744 units × 0.5 min/unit) / (32.4 hours × 60 min/hour)
= 1,872 minutes / 1,944 minutes = 0.963 (96.3%)

Quality = 3,670 / 3,744 = 0.980 (98.0%)

OEE = 0.900 × 0.963 × 0.980 = 0.849 or 84.9%

This is a strong result, approaching the ~85% world-class OEE benchmark.

Convert to TEEP using calendar time

Now swap the Availability denominator to calendar time:

TEEP Availability = 32.4 hours / 168 hours = 0.193 (19.3%)

TEEP = 0.193 × 0.963 × 0.980 = 0.182 or 18.2%

Performance and Quality stay the same; only the time basis changed.

Interpretation: what actions does this result suggest?

The line executes beautifully during scheduled hours (84.9% OEE), but productive time represents less than one-fifth of the week (18.2% TEEP). If demand is strong and lead times are stretching, the plant has several options before buying a second line:

• Add a second or weekend shift to push TEEP closer to 40–50% without new capex.
• Move planned maintenance to off-hours (nights, weekends) to reclaim some of the 4 hours currently subtracted from planned production time.
• Automate selected operations to enable lights-out production during unstaffed hours.
• Reduce the 3.6 hours of unplanned downtime through preventive vs predictive maintenance strategies to lift both OEE and TEEP.

TEEP makes the capacity conversation concrete: you’re leaving 80% of the week on the table, and addressing that gap may cost far less than a new asset.

Benchmarks: what’s “good” for OEE and TEEP?

Context matters. An OEE of 60% might be excellent for a complex, high-mix assembly cell and unacceptable for a dedicated bottling line.

OEE benchmark ranges

Industry surveys suggest:

• Average OEE: ~40% across manufacturing sectors.
• Good OEE: 60–70%, indicating disciplined execution and moderate losses.
• World-class OEE: ~85%, achieved by plants with mature continuous-improvement programs.

Component-level targets are often used as diagnostic thresholds: Availability ≥ 90%, Performance ≥ 95%, Quality ≥ 99%. Falling short on one factor tells you where to focus (breakdowns, slow cycles, or defects).

TEEP depends on shift strategy

Because TEEP’s denominator is calendar time, “good” TEEP varies dramatically by operating model:

• Single-shift operations (40 hours/week): 25–35% TEEP is common, constrained by staffing.
• Two-shift batch manufacturers (80 hours/week): 40–60% TEEP is typical.
• Continuous process industries (24/7): 80–90% TEEP is achievable when planned maintenance is tightly managed.

Chasing an arbitrary TEEP target without considering shift economics can drive poor decisions (e.g., adding a graveyard shift that destroys labor-cost structure).

What to benchmark internally

Rather than comparing your TEEP to published averages, track:

• Trend over time: Is TEEP rising as you extend operating hours, reduce planned downtime windows, or improve OEE?
• Line-to-line or site-to-site comparisons: For similar assets and operating models, TEEP reveals which locations are better utilizing calendar capacity.
• Constraint analysis: TEEP is most meaningful at bottleneck resources; non-constraints may run lower TEEP by design (batching, upstream starvation).

When to use OEE vs TEEP

The right metric depends on the question you’re trying to answer.

Use OEE when the question is execution during scheduled time

OEE answers: “Are we getting the most out of the hours we’ve staffed and planned to run?”

Typical use cases:

• Daily/shift-level performance reviews with supervisors and operators.
• Root-cause problem solving for breakdowns, slow cycles, and quality losses.
• Continuous improvement programs (Lean, TPM, Six Sigma) focused on eliminating waste during production.
• Operator and line comparisons when shifts and schedules are identical.

Audience: Shop-floor supervisors, maintenance technicians, CI engineers.

Use TEEP when the question is true capacity and capex

TEEP answers: “Are we using all the time we own, or could we meet demand by running more hours instead of buying more equipment?”

Typical use cases:

• Capacity planning and capex justification: Before approving a new line, validate that existing assets are running enough hours.
• Shift-strategy decisions: Should we add a second shift, extend to weekends, or move to continuous operations?
• Maintenance scheduling optimization: Can we shift planned stops to off-hours and reclaim productive calendar time?
• Executive dashboards and board reporting: TEEP connects operations to financial outcomes (capital intensity, asset turns).

Audience: Plant managers, operations VPs, finance/planning teams, executives.

Common scenarios: demand spike, backlog, overtime, new equipment request

When a plant faces a capacity crunch, follow this decision tree:

1. Check OEE first. If OEE is below 70%, the problem is execution: eliminate downtime, improve cycle times, reduce defects. Adding shifts or equipment won’t fix poor execution.
2. If OEE is strong (>75–80%), check TEEP. A low TEEP with high OEE means you’re running well but not running enough hours.
3. Compare the cost of time vs the cost of equipment. Adding a shift or weekend overtime may cost 20–30% more per labor hour but requires zero capex and can be dialed back when demand softens. A new line locks in depreciation, maintenance, and floor space for years.
4. Model the TEEP impact. If you’re at 30% TEEP on one shift, moving to two shifts can push you to 55–60% TEEP, often enough to defer or eliminate a multi-million-dollar purchase.

Tell-tale signs you should look at TEEP:

• Frequent “we need another machine” requests when current equipment sits idle nights and weekends.
• High overtime costs driven by demand, not breakdowns.
• Long customer lead times despite acceptable OEE.
• Planned maintenance consuming large blocks of prime-shift time.
• Idle capacity on Saturdays/Sundays while orders stack up.

For teams managing asset utilization at scale in physical operations (ports, rail yards, logistics hubs), the same logic applies. The right metric distinguishes between “our cranes aren’t productive when dispatched” (an OEE problem) and “our cranes sit idle half the week” (a TEEP/utilization problem).

Common pitfalls that make OEE/TEEP comparisons useless

Even the best formulas fail when the data underneath is inconsistent or biased.

Inconsistent definitions of planned time and downtime categories

If Line A classifies a 15-minute changeover as “planned downtime” (excluded from planned production time) and Line B calls it “unplanned” (counted against Availability), their OEE scores aren’t comparable. The same inconsistency across sites makes TEEP rollups meaningless.

Fix: Standardize your time definitions and loss taxonomy across all lines and facilities. Document what counts as planned vs unplanned, and train everyone (from operators to plant managers) to code events the same way. A CMMS or maintenance work order system can enforce category standards and maintain a single source of truth for downtime history.

Ignoring constraint resources and bottlenecks

OEE and TEEP are asset-level metrics. Optimizing a non-constraint can waste resources without improving throughput. A packaging line with 95% OEE still won’t increase output if the upstream filling machine (the bottleneck) runs at 60% OEE.

Fix: Pair OEE/TEEP with Theory of Constraints thinking. Measure and improve the bottleneck first; monitor non-constraints to ensure they don’t starve or block the constraint. Track system throughput alongside individual asset effectiveness.

Manual data capture and biased downtime coding

When operators log downtime manually at the end of a shift, memory fades, details blur, and operator behavior impacts the data. Micro-stops (brief slowdowns under 5 minutes) disappear entirely. Downtime reasons get coded to categories that deflect blame (“waiting on maintenance” vs “didn’t call maintenance promptly”).

Fix: Move to automated downtime logging wherever possible: machine sensors, PLC data, or simple tablet-based event capture during the stop, not hours later. Standardize reason codes, train on accurate classification, and decouple performance metrics from individual blame to reduce gaming.

Without clean, consistent data, OEE and TEEP become vanity metrics that drive the wrong behavior. Invest in data quality before you invest in dashboards.

How to improve OEE and TEEP: practical levers

Improving OEE and improving TEEP require different strategies, though many actions help both.

Improve Availability

Availability losses (unplanned downtime and changeover delays) typically offer the largest OEE gains.

• Reduce breakdowns through preventive maintenance schedules, predictive maintenance solutions that flag failures before they happen, and root-cause analysis (RCA) on repeat offenders.
• Speed changeovers using SMED (Single-Minute Exchange of Die) techniques: convert internal steps (done while the machine is stopped) to external steps (prepared while running), standardize setups, and practice.
• Improve maintainability by stocking critical spares, cross-training technicians, and designing quick-access panels for common repair points.

Improve Performance

Performance losses occur when equipment runs slower than ideal cycle time, often invisible on manual logs.

• Eliminate micro-stops (jams, sensor faults, minor adjustments) by fixing root causes: worn guides, inconsistent material feed, poorly tuned sensors.
• Optimize speeds through process tuning, better tooling, or automation that holds tighter tolerances than manual operation.
• Monitor in real time: Performance losses are hard to see in end-of-shift summaries; real-time dashboards reveal patterns (slow first hour after startup, degradation before breaks).

Improve Quality

Quality losses (scrap and rework) hurt both throughput and margin.

• Reduce startup scrap by improving changeover procedures, validating setups before full-speed runs, and using statistical process control (SPC) to detect drift early.
• Stabilize the process with designed experiments (DOE), preventive maintenance on quality-critical components (cutters, nozzles, sensors), and tighter incoming material specs.
• Implement first-pass yield tracking and fast feedback loops so operators catch defects in minutes, not hours.

Improve TEEP specifically

Because TEEP’s denominator is calendar time, increasing TEEP means running more hours productively or reducing the planned stops that consume calendar time.

• Extend operating hours: Add a second shift, weekend shifts, or move to continuous operations where demand and economics justify the labor cost.
• Reschedule planned maintenance to off-hours (nights, weekends) so prime-time shifts stay productive. This requires aligning operations and maintenance around shared planning calendars and cross-functional visibility.
• Automate low-supervision operations (packaging, material handling, inspection) to enable lights-out production during unstaffed hours.
• Right-size staffing models: Flexible labor pools, cross-trained operators, and remote monitoring can make adding hours economically viable.

Key principle: Improving TEEP without destroying cost structure requires strategic tradeoffs. Blindly adding shifts can inflate labor cost per unit if demand doesn’t sustain the extra capacity.

Combine the metrics: Use OEE for continuous improvement and daily execution discipline. Use TEEP for capacity planning, investment justification, and long-term strategy. Track both, but don’t conflate them.

How Opsima supports utilization KPIs in physical operations

OEE and TEEP concepts extend naturally beyond discrete manufacturing to asset-heavy physical operations: container terminals, rail yards, bulk ports, and logistics fleets, where equipment effectiveness and calendar-time utilization drive throughput and service levels.

In these environments, “planned production time” maps to dispatch plans and shift schedules: the hours you’ve assigned cranes, trucks, or locomotives to move cargo. “All time” remains the 24/7 calendar: every hour a crane could be lifting containers or sitting idle for lack of demand, maintenance, or operational bottlenecks.

Automated KPI capture from real operations data

Manual logs and end-of-shift spreadsheets fail in high-tempo, multi-asset environments. Opsima connects to real operations data (telematics, terminal operating systems, SCADA, IoT sensors) to calculate availability, performance, and quality metrics automatically, without operator input.

Automated KPI tracking removes the biases and gaps that plague manual systems: missed micro-stops, memory-driven reason codes, and data entered hours after the event. Real-time capture means supervisors see losses as they happen and can intervene before a minor issue cascades.

Standardized workflows for downtime and maintenance visibility

Reliable OEE and TEEP require consistent definitions and cross-functional transparency. When a crane stops, operations needs to know if it’s a mechanical failure (call maintenance), an operational constraint (no work assigned), or a planned inspection (scheduled and expected).

Opsima’s workflow engine closes the loop: turning insights into action by routing alerts to the right team, tracking resolution time, and linking downtime events to maintenance work orders. This shared visibility prevents finger-pointing and ensures that “planned” vs “unplanned” classifications reflect reality, not political convenience.

From metrics to action: alerts, prioritization, and planning

KPIs only matter if they drive better decisions. Opsima translates OEE and TEEP into actionable priorities:

• Alerts when availability drops below threshold, flagging both the asset and the likely root cause (recurring fault code, operator request, schedule conflict).
• Prioritization of maintenance tasks by impact: fix the crane that’s the bottleneck first, defer work on non-constraints.
• Capacity planning dashboards that show TEEP by asset class, shift, and time period, so planners can model the ROI of adding weekend shifts vs buying another crane.

For operations leaders managing dozens or hundreds of assets across ports, rail networks, or distribution centers, Opsima provides a single source of truth for equipment performance, bridging the gap between real-time execution (OEE) and strategic capacity decisions (TEEP).

Ready to see how automated KPI tracking and cross-team workflows improve utilization without adding headcount? book a 15-minute discovery call to explore how Opsima turns data into decisions for physical operations.

Conclusion

OEE and TEEP measure the same thing (equipment effectiveness) but against different time horizons. OEE focuses on execution during scheduled hours, making it the daily scoreboard for supervisors, operators, and continuous-improvement teams. TEEP measures effectiveness against all available calendar time, making it the strategic lens for capacity planning, shift decisions, and capex justification.

Both matter. A plant with poor OEE wastes the hours it runs; a plant with low TEEP wastes the hours it doesn’t. The worked example showed how a strong 85% OEE can coexist with an 18% TEEP, signaling excellent execution but massive underutilization of calendar capacity. Before writing a check for new equipment, validate that you’re running existing assets enough hours and addressing the losses that erode availability, performance, and quality.

Avoid the common pitfalls: inconsistent time definitions, ignoring bottlenecks, and manual data capture that injects bias. Standardize your taxonomy, automate event logging, and focus improvement efforts where they unlock real throughput.

For asset-intensive operations (ports, terminals, rail, logistics), these principles translate directly: measure effectiveness during dispatched time (OEE) and against calendar time (TEEP), then use the gap to drive smarter staffing, maintenance scheduling, and capital allocation.

Opsima makes it practical. Automated KPI capture, standardized workflows, and action-oriented dashboards give operations and maintenance teams a shared foundation for improving both execution and utilization, without spreadsheets, without guesswork, and without adding overhead.