Manufacturing Performance Guide | ATS Automation

The ATS Performance Blog is your navigational hub for practical, evidence-led guidance on industrial automation, reliability, and manufacturing performance. You’ll find clear definitions, benchmarks, toolkits, and decision frameworks used by operations leaders to raise OEE, reduce risk, and accelerate time-to-market. Use this page as a starting point to explore deep dives across life sciences, EV/battery, nuclear/energy, and discrete manufacturing.

This hub complements the ATS Automation blog, ATS Industrial Automation blog, and ATS Service blog by consolidating performance-focused content in one place. Throughout, we disambiguate “ATS.” We also provide calculators, checklists, and case metrics to help you move from concept to validated, production-grade systems without guesswork.

What ‘ATS Performance’ Means in Manufacturing

In manufacturing, “performance” is the measurable ability of a line, cell, or plant to deliver safe, compliant output at target throughput, yield, cost, and uptime. Core domains include:

• OEE/TEEP
• First-pass yield (FPY)
• Reliability (MTBF/MTTR)
• Quality (ppm/defects)
• Safety
• Energy intensity
• CO2e per unit

In regulated industries, performance also spans validation status and data integrity under GxP and ISO frameworks.

Typical KPI targets vary by industry, compliance burden, and risk profile. For example:

• Life sciences may target FPY ≥ 98–99.5% with OEE 60–80% on validated lines.
• EV/battery targets OEE 70–85% with aggressive EOL test coverage.

Anchor targets with context. Product mix, cycle time, learning-curve phase, and regulatory constraints all influence what “good” means.

Disambiguation: ATS (Automation brand) vs ATS (Applicant Tracking System)

If you arrived searching for “ATS” as an applicant tracking system, you’re in the right place for manufacturing performance—not hiring software. Here, ATS refers to ATS, a global automation and services leader across life sciences, EV/battery, and other advanced industries. We cover how to design, validate, and run production systems with repeatable performance.

To help, we clearly label content by sector and topic (OEE, reliability, digitalization, compliance). If you’re seeking recruiting tools, look for HR-focused “Applicant Tracking System” platforms. If you need industrial automation performance insight, continue below for benchmarks, templates, and decision playbooks.

Takeaway: clarity up front ensures you reach the right resources fast.

Start Here: Quick Guides by Goal

Use these outcome-based starting points to jump to the content that matches your immediate objective. Each section offers definitions, safe benchmark ranges, and links to detailed playbooks and toolkits.

Pick your goal, review the core levers. Then download the relevant checklist or calculator to shorten your path to results.

Increase OEE and First-Pass Yield

OEE measures equipment effectiveness (OEE = Availability × Performance × Quality). FPY is the share of units that pass all steps without rework.

Safe OEE ranges:

• Life sciences: 60–80%
• EV/battery: 70–85%
• Discrete assembly: 65–85%

FPY targets:

• Life sciences: 98–99.5%
• EV/battery: 95–99%
• Discrete: 92–98%

Gains often come from short changeover reduction, constraint balancing, and error-proofing.

Practical levers include:

• SMED for changeovers
• Takt/cycle-time balancing at bottlenecks
• In-station test with automatic containment
• Closed-loop parameter windows

Start with a 12-week sprint:

• Map top loss buckets.
• Run two kaizens per pillar (availability, performance, quality).
• Lock improvements with standardized work.

Takeaway: focus on your top loss bucket, not the averages.

Reduce Downtime and Improve Reliability

Reliability hinges on increasing MTBF (mean time between failures) and reducing MTTR (mean time to repair). Start by classifying failure modes with RCM.

Implement condition monitoring on failure precursors tied to predictive maintenance work orders. Focus on:

• Vibration
• Temperature
• Current draw

A fast win is to target “chronic minor stops,” which often account for 30–50% of performance loss.

Engineer for maintainability with:

• Tool-less access
• Modular spares
• Standardized sensors that your techs stock and understand

Use a simple reliability KPI set:

• MTBF by module
• Corrective-to-preventive ratio
• Schedule compliance

Takeaway: design reliability in, then sustain it with data-driven maintenance plans.

Accelerate Time-to-Market and De-Risk Scale-Up

Programs launch faster when pre-automation steps are sequenced and resourced. Start with proof of principle (PoP) on high-risk processes.

Drive design for manufacturing (DFM) and design for service (DFS) before freezing URS and acceptance criteria. Lock a validation plan (IQ/OQ/PQ) early if you operate under GMP or ISO 13485.

Typical critical-path risks include late requirements changes, unrealistic cycle-time budgets, and insufficient supplier PPAPs or golden-sample definitions.

Sequence your gating as:

1. PoP
2. DFM/DFS
3. URS/acceptance criteria
4. FAT plan
5. SAT/validation

Takeaway: a week spent clarifying acceptance criteria can save months at SAT.

Core Topics We Cover

This hub consolidates the performance content most requested by operations and engineering teams. Expect benchmarks, formulas, selection criteria, and validated practices. You’ll also find sector-specific considerations that influence targets, verification, and time-to-value.

Where helpful, we disclose calculator logic and provide downloadable templates. You can adapt the methods to your facility and governance model. Build a repeatable approach your team can sustain beyond the pilot line.

Manufacturing Efficiency and OEE: Benchmarks and Levers

OEE = Availability × Performance × Quality. TEEP = OEE × Utilization, where Utilization reflects calendarized capacity use.

Calculator logic:

• Availability = (Planned Time − Downtime) / Planned Time
• Performance = (Ideal Cycle × Total Count) / Run Time
• Quality = Good Count / Total Count

Always measure at the constraint first to avoid optimizing the wrong station.

Common levers include:

• SMED (<10-minute changeovers in discrete lines)
• Constraint-based scheduling
• Automated recipe management
• In-line vision for early defect detection
• Smart conveyance and high-speed assembly where product variability or precision is high

Takeaway: quantify losses, fix the biggest one, then error-proof the fix.

Reliability and Maintenance: MTBF/MTTR, RCM, and Data-Driven Uptime

RCM begins with a functional breakdown, failure modes/effects, and consequence ranking. Assign preventive, predictive, or redesign tasks based on risk.

Track MTBF at the module level (e.g., gripper, servo, vision station). In mature sites, trend a corrective/preventive ratio below ~0.4.

A CBM stack might include:

• Vibration
• Thermal
• Current signature
• Pneumatic leak analytics

Feed shop-floor data to a CMMS/EAM to trigger predictive work orders. Ensure parts availability based on lead times and criticality.

Quick wins include:

• Standardizing spares
• Visual PM standards
• Golden-parameter baselines per SKU

Takeaway: reliability is a design choice sustained by disciplined maintenance and clean data.

Digitalization and Validation: MES, Digital Twins vs System Twins, and Data Integrity

A digital twin simulates a product/process for design and optimization. A system twin mirrors a deployed system for operations, diagnostics, and performance tuning.

Use twins to de-risk cycle times, buffer sizes, and robot reach during design. Shift to a system twin for predictive maintenance and throughput optimization at scale. Integrate MES for genealogy, eBR/eDHR, and right-first-time release.

For validated environments, align with:

• GAMP 5
• 21 CFR Part 11 (audit trails, electronic signatures, ALCOA+ data integrity)
• User requirements and validation deliverables
• Cybersecurity controls (e.g., IEC 62443 zones/conduits, least privilege, patch governance)

Takeaway: model early, validate continuously, and secure by design.

Conveyance, Robotics, and High-Speed Assembly

Conveyance impacts cycle time, changeover, and quality stability. Smart conveyance excels where high mix, precise positioning, or asynchronous operations are required. Traditional conveyors fit stable, high-volume, low-variability flows.

Selection criteria include:

• Part families
• Pitch accuracy
• Station-to-station independence
• Future SKU roadmap

For robotics, consider:

• Payload/inertia
• Reach
• Cycle-time budget with path smoothing
• Maintenance access

High-speed assembly benefits from integrated vision, force/torque control, and in-station testing with automatic containment.

Takeaway: choose transport and motion to fit both today’s takt and tomorrow’s product mix.

Sector Playbooks: Life Sciences, EV/Battery, and Nuclear/Energy

Life sciences prioritize validation, traceability, and contamination control. Typical OEE is 60–80% with FPY 98–99.5% on mature lines. Design choices are shaped by GMP, ISO 13485, and 21 CFR Part 11, making standardized recipes and controlled changes critical. Expect longer change-control cycles and rigorous supplier documentation.

EV/battery emphasizes throughput, safety, and EOL testing coverage. OEE targets of 70–85% with FPY 95–99% are common on stabilized lines. Functional safety (ISO 26262) and process safety (IEC 61508/61511) influence cell, module, and pack operations.

Nuclear/energy demands conservative design, redundancy, and robust maintenance governance.

Takeaway: benchmarks and controls are sector-specific—design accordingly.

Decision Frameworks and Toolkits

Use these frameworks to move from learning to action. Align stakeholders on scope, risk, and time-to-value.

Each framework is paired with a checklist or worksheet so you can make transparent, auditable decisions during RFQ and program governance.

Pre-Engineered vs Custom Automation: How to Decide

Pre-engineered solutions fit stable volumes, standard processes, and tight lead times. Custom fits high novelty, tight tolerances, complex inspections, or regulated validation nuances.

Build a weighted matrix across criteria such as:

• Volume
• Variability
• Precision
• Validation burden
• Lead time
• Scalability
• TCO
• Consider pre-engineered when: process is common, takt ≥ cycle-time catalogs, and validation is straightforward.
• Consider custom when: process risk is novel, FPY demands are extreme, or product evolution is rapid.

Takeaway: let risk, variability, and validation—not preference—drive the choice.

Build In-House vs Turnkey Contract Manufacturing

Compare total cost of ownership (TCO) across:

• CapEx
• OpEx
• Labor
• Scrap
• Maintenance
• Floor space
• Energy
• Compliance overhead

Use payback = Initial Investment / Annual Net Benefit. For risk-adjusted ROI: (Expected Benefit × Probability of Success − Expected Risk Cost) / Investment. Model scenarios over 3–7 years to reflect upgrades and volume ramps.

Risk allocation is as important as cost. Turnkey can mitigate integration and schedule risk, while in-house preserves IP and flexibility.

Capacity planning should include:

• Ramp curves
• Training time
• Spare-parts strategy

Takeaway: decide with TCO plus risk, not unit price alone.

Acceptance Criteria and RFQ Checklist (Downloadable)

Strong RFQs and acceptance criteria de-risk delivery and speed FAT/SAT. Use this condensed checklist and download the full template and scorecard to standardize vendor responses.

RFQ essentials:

• URS with cycle time/takt, FPY, OEE targets, and product families
• Part specifications, tolerances, and golden samples
• Compliance: GMP/ISO 13485, GAMP 5, 21 CFR Part 11, functional safety
• Data: MES integration, traceability, audit trails, cybersecurity controls
• Test plans: FAT/SAT, IQ/OQ/PQ and acceptance metrics
• Spares lists, training, documentation, and warranty

Acceptance criteria:

• Throughput at spec across min/max tolerances and ambient ranges
• FPY and defect containment at station and line level
• Changeover time, recipe management, and audit trail verification
• MTBF/MTTR thresholds, PM routines, and parts availability
• EHS/failsafe behavior and recovery from faults

Takeaway: clear criteria upfront prevent surprises downstream.

Case Metrics: What Good Looks Like

Evidence matters, so here are anonymized snapshots of measurable outcomes achieved on recent programs. Each used standard governance (URS → FAT → SAT → validation) with transparent acceptance criteria and post-go-live sustainment plans.

Numbers vary by product, process, and maturity, but the patterns are instructive. Target top losses, validate early, and design for maintenance and data integrity. Use these ranges to sanity-check your own targets and timelines.

Life Sciences: +12% FPY, −40% Changeover Time

A sterile medical device line added in-station vision with forced containment. Standardized recipes tied to MES eDHR.

FPY rose from 87% to 99% within eight weeks. SMED reduced changeovers from 25 to 15 minutes, yielding −40% changeover time and +8% OEE. Validation followed GAMP 5 with 21 CFR Part 11-compliant audit trails.

Key enablers were golden-sample control, parameter windowing, and defect pareto containment to station of origin. Training used role-based competency matrices to stabilize gains across shifts.

Takeaway: pair SPC/vision with recipe control for fast, compliant FPY wins.

EV/Battery: −65% EOL Test Time, Improved Safety Margin

A module assembly line implemented model-based test sequencing and parallel diagnostics. A system twin pre-validated coverage.

EOL test time dropped 65% while maintaining coverage, improving takt alignment and buffering. Safety margin improved via enhanced insulation resistance and thermal monitoring checks.

Functional safety reviews under ISO 26262 and process safety references (IEC 61511) guided interlock design and fault recovery. The program shaved six weeks off ramp due to virtual commissioning and pre-validated test limits.

Takeaway: use twins to tune EOL coverage and takt before hardware freezes.

Asset Management: −18% Unplanned Downtime via Data-Driven Maintenance

A multi-line facility added condition monitoring (vibration, thermal, air leaks) on top-failure modules. Alerts tied to CMMS work orders with parts reservations.

Within three months, the corrective-to-preventive ratio improved from 0.9 to 0.5. Unplanned downtime fell 18%. MTBF increased 22% on the bottleneck station.

Sustainment came from standard PM libraries, technician training, and governance that reviewed MTTR outliers weekly. Inventory risk dropped as spares were right-sized based on criticality and lead time.

Takeaway: instrument critical modules and automate the path from alert to action.

FAQs

The most common questions we see from operations and engineering leaders focus on definitions, benchmarks, and decision tooling. These concise answers link to the logic used in our calculators and checklists so you can adapt them to your workflow.

What is the ATS Performance Blog?

The ATS Performance Blog is a hub for industrial automation performance—covering OEE/TEEP, FPY, MTBF/MTTR, digitalization, compliance, and decision frameworks. It consolidates resources from the ATS Automation blog, ATS Industrial Automation blog, and ATS Service blog. You get benchmarks, templates, and case metrics to help you design, validate, and scale production systems.

How do you calculate TCO and payback for automation?

TCO includes CapEx, integration/validation, labor, scrap, maintenance/spares, energy, software, floor space, and decommissioning over the asset life. Payback = Initial Investment / Annual Net Benefit.

For risk-adjusted ROI: (Expected Benefit × Probability of Success − Expected Risk Cost) / Investment. Model multiple volume and yield scenarios across 3–7 years.

What are typical OEE and FPY targets by industry?

Safe ranges:

• Life sciences: OEE 60–80% with FPY 98–99.5%
• EV/battery: OEE 70–85% with FPY 95–99%
• Discrete assembly: OEE 65–85% with FPY 92–98%

Early ramp and high-mix environments trend lower. Stabilized, validated lines trend higher. Always adjust for product mix, regulatory burden, takt, and inspection strategy.

Resources and Downloads

Get started faster with practical tools you can adapt to your program governance, validation model, and CMMS/MES stack.

• RFQ toolkit with URS template, acceptance criteria, and partner scorecard
• OEE/TEEP calculator with disclosed formulas and constraint-first worksheet
• Risk-adjusted ROI/TCO model with scenario planning inputs
• Maintenance maturity self-assessment and PM library starter set
• Digital twin vs system twin selection guide and validation checklist
• Smart conveyance vs traditional conveyor decision aid with lifecycle criteria

Editorial Standards and Update Cadence

Content is authored and reviewed by practitioners with credentials such as P.Eng/PE, PMP, ASQ CQE, and CMRP. It is aligned with relevant standards (GMP, ISO 13485, GAMP 5, IEC/ISO functional safety, 21 CFR Part 11). We cite formulas, disclose calculator logic, and provide anonymized case metrics to reinforce E-E-A-T.

We update benchmarks and templates quarterly or when standards change, with version notes and dates for transparency. Have a question or want a deeper dive by sector? Explore the category hubs (Life Sciences, EV/Battery, Reliability) and author bios to continue your evaluation journey.