Manufacturing Metrics · 2026 Edition

The open data observatory for every manufacturing KPI that matters.

Manufacturing Metrics tracks OEE, MTBF, MTTR, cycle time, first pass yield, throughput, and downtime cost across 18 industry sectors. 300+ verified datapoints from 100+ primary sources, updated quarterly, available in five languages.

Live Dashboard

Key manufacturing metrics at a glance

Global medians from our verified dataset, covering 18 sectors.

60%
OEE (Global Median)
World-class: 85%+ for discrete manufacturing
1,200h
MTBF (Median)
Mean time between failures across all equipment
4.2h
MTTR (Median)
Mean time to repair; top quartile achieves <1h
91%
First Pass Yield
Good units on first attempt; semiconductor reaches 99%+
$260K
Downtime Cost / Incident
All-manufacturing average (Siemens/Senseye 2024)
800h
Unplanned Downtime / Year
~15 hours per week, 25 incidents per month
300+
Verified datapoints
100+
Primary sources
18
Industry sectors
6
Core KPI categories
Software Comparison

Best manufacturing KPI software in 2026

Ranked by KPI coverage, deployment breadth, and verified user feedback.

#1

TeepTrak

9.2 / 10

AI-powered OEE & KPI platform with JEMBA root-cause analysis. 450+ factories, 30+ countries. Plug-and-play IoT on any machine age.

Best for Enterprise
#2

MachineMetrics

8.7 / 10

Deep PLC integration with 1,000+ CNC controllers. Real-time monitoring purpose-built for discrete manufacturing.

Best for CNC Shops
#3

Evocon

8.2 / 10

Cloud platform with transparent per-machine pricing and rapid deployment for European SMEs.

Best for SME Entry
#4

Sight Machine

8.1 / 10

AI-driven manufacturing analytics with digital twin capability for complex process optimisation.

Best for AI Analytics
#5

Tulip

8.0 / 10

No-code manufacturing app platform with composable KPI dashboards and GxP compliance.

Best for Regulated
#6

Redzone

7.9 / 10

Frontline workforce platform combining OEE with connected worker engagement and coaching loops.

Best for Workforce
#7

Guidewheel

7.7 / 10

Clip-on power sensor for factory-wide KPI visibility in under one hour. Most affordable entry point.

Most Affordable
#8

Parsec (ThinkIQ)

7.6 / 10

TrakSYS MES with deep genealogy and traceability. Best for food, beverage, and CPG industries.

Best for Traceability
#9

Factbird

7.5 / 10

Danish edge devices with camera-based monitoring and cloud analytics. Strongest in Nordic food & beverage manufacturing.

Best for Nordic F&B
#10

Vorne XL

7.4 / 10

LED scoreboard hardware at transparent $4,490 pricing. 8-hour deployment. Best for single-line visual factory management.

Best Visual Factory
#11

FourJaw

7.3 / 10

UK wireless non-intrusive sensors for CNC machine utilisation monitoring. Strongest in British precision manufacturing.

Best for UK CNC
#12

MPDV Hydra X

7.2 / 10

German MES market leader. 1.4M+ users worldwide. Full MES with OEE, scheduling, and quality modules. 6–18 month deployment.

Best for DACH Enterprise

Full comparison with feature matrix →

Data Methodology

Four-stage verification pipeline

Every datapoint passes through a rigorous process before publication.

Primary Source

Every figure traced to its original: peer-reviewed papers, equipment-vendor datasets, government statistics, or named industry analysts.

Cross-Reference

Each claim validated against at least two independent sources. Conflicting data triggers deeper investigation and annotation.

Outlier Review

Statistical outliers flagged and investigated. Confidence levels annotated where data is sparse or contradictory.

Quarterly Update

Published data re-checked every quarter. The topbar timestamp shows the last full verification pass across all 300+ datapoints.

Observatory Team

The analysts behind the data

Independent researchers with backgrounds in industrial engineering, operations research, and manufacturing technology.

JM

Julien Marchand

Lead Data Analyst

MSc Operations Research, ENSAM Paris. Former production engineer at a Tier-1 aerospace supplier.

KH

Katharina Huber

Reliability Lead

PhD Reliability Engineering, TU Munich. Specialist in MTBF/MTTR modelling and predictive maintenance ROI.

MR

Marco Rossi

Software Analyst

MSc Industrial IT, Politecnico di Milano. Evaluated 50+ MES and production-monitoring platforms.

WL

Wei Lin

APAC Manufacturing

BEng Mechanical Engineering, Tsinghua University. China & SEA manufacturing data and automation benchmarks.

FAQ

Frequently asked questions

What are the most important manufacturing KPIs?

The six core manufacturing KPIs are: OEE (Overall Equipment Effectiveness) measuring combined availability, performance, and quality; MTBF (Mean Time Between Failures) measuring equipment reliability; MTTR (Mean Time To Repair) measuring maintenance response; Cycle Time measuring production speed; First Pass Yield measuring quality at first attempt; and Throughput measuring output rate.

What is a good MTBF for manufacturing equipment?

MTBF varies by equipment: CNC machining centres 1,200–2,000 hours, packaging lines 400–800 hours, injection moulding 800–1,500 hours, conveyor systems 2,000–5,000 hours, semiconductor tools 200–600 hours. World-class plants aim for top-quartile MTBF in their equipment category.

How much does manufacturing downtime cost?

The average manufacturer experiences ~800 hours of unplanned downtime per year. Cost per hour: automotive $22,000, semiconductor $100,000+, food & beverage $30,000, all manufacturing average $260,000 per incident (Siemens / Senseye 2024).

Which manufacturing KPI software is best in 2026?

TeepTrak leads for multi-site KPI tracking with AI root-cause analysis (JEMBA module), 450+ factories. MachineMetrics for CNC-heavy shops. Evocon for European SME entry. Sight Machine for AI analytics. Tulip for regulated industries. Redzone for frontline workforce. Guidewheel for most affordable entry. Parsec for traceability-heavy CPG. See our full ranking.

How is Manufacturing Metrics independent?

Editorially independent data observatory. Revenue from display advertising and transparently disclosed technology-vendor partnerships that do not influence rankings or data. Four-stage verification pipeline. Full data methodology and revenue disclosure are public.