GCRI · Advanced Industry and Robotics
Industrial automation
Industrial automation uses control systems, sensors, actuators and software to run machines and processes with less direct human intervention. It raises productivity and consistency, and it creates new dependencies on software, data, networks and skilled maintenance. This page sets out how automation works and fails, how its adoption and reliability are measured, and what evidence serves the plant operators, engineers and policymakers involved.
Industrial automation on the record.
Industrial automation: how the risk develops.
Automation and digitally managed production. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Control loops run processes
Programmable controllers and distributed control systems read sensors and adjust valves, motors and heaters continuously, keeping processes within set limits.
Integration adds dependencies
Connecting machines to plant and business systems improves coordination but means a network, software or data fault can stop production across a site.
Legacy systems linger
Control systems often run for decades. Obsolete hardware and unsupported software become hard to maintain, secure and replace without long shutdowns.
Skills keep systems running
Automated plants need technicians and engineers who can diagnose faults. Skill shortages lengthen downtime when automation fails.
Connected activity in Germany.
Night lights, population density and land cover over Germany, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Connected activity, for this page
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on Germany.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA/NOAA VIIRS · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Observed (EO): NASA/NOAA VIIRS (Suomi NPP). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
Automation changes how industry draws on energy, water and people.
Automation studies are designed for the Global Sector Risk and Resilience series. Functional safety follows IEC 61508; the Global Centre for Risk and Innovation (GCRI) assesses no product.
- Plant-level adoption studies.
- Safety incident analyses.
Plant operations and safety.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Plant operations and safety
Industrial automation · Plant 3 · Shift 2
- OEE, % of planned782Watch
- Safety-critical alarms active2Alert
- Robot cell stops this shift2
- Energy per unit1.6kWhNormal
Barriers
layers of protection
| High-pressure trip | SIS | Q3 | Healthy |
| Relief valve RV-12 | Mechanical | Q2 | Healthy |
| Gas detection zone 4 | Detection | Q2 | Bypassed |
| Robot cell light curtain | Safeguard | Q3 | Healthy |
Reactor pressure
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Overall equipment effectiveness | percent | monthly | Plant performance records | OBSERVED |
| Unplanned downtime | hours per period | monthly | Maintenance management systems | OBSERVED |
| Control systems past vendor support | percent of installed base | per assessment | Asset inventories | DECLARED |
| Mean time to repair automation faults | hours | monthly | Maintenance records | OBSERVED |
| Firms using advanced automation | percent of manufacturing firms | per survey | Business technology surveys | REPORTED |
| Automation technician vacancies | count | annual | Labour market statistics | REPORTED |
Model industrial automation as a system. Run the cascade.
Assembled from the Nexus Standards area profile Industry and manufacturing: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Natech event
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Chemical spill or leak (set off downstream)
- Emergency-response overload (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H50Hazard family: Industrial and process accidents (NHS-1 infra.industrial)NRS-H109Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)NRS-X10Chemical, industrial, nuclear and radiological safetyNRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-K14Domain pack profile: Manufacturing industrial (DOMAIN-014)
Observation
- eMARS Major Accident Reporting System (listed for reference)
- World Development Indicators (listed for reference)
- ILOSTAT (listed for reference)
- Manufacturing value added as a proportion of GDP and per capita
- CO2 emission per unit of value added
- Manufacturing, value added (% of GDP)
- Direct economic loss to all other damaged or destroyed productive assets attributed to disasters
Example geography
- The Strait of Malacca: 227 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Eurostat ICT usage in enterprises and advanced technology modules | Adoption of automation, robotics and data technologies | Open, Eurostat reuse policy |
| OECD studies on the diffusion of digital and automation technologies | Cross-country adoption evidence | Public, OECD terms |
| US Census Bureau Annual Business Survey technology modules | Firm adoption of automation and AI | Public domain, US government |
| ISA and IEC standards for automation (ISA-95, IEC 61131) | Architecture and programming standards | Licensed standards, purchase required |
| Industry association surveys on downtime and maintenance | Reliability practice | Public summaries, terms to check |
How industrial automation is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in industrial automation: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Advanced Industry and Robotics prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Plant operators and engineering managers | Which systems to modernise and how to plan downtime | Obsolescence reviews; reliability data |
| Industry ministries | How to support automation in smaller firms | Adoption profiles |
| Training bodies | Which technician skills to train | Skills gap analyses |
Outputs this topic produces
- Automation reliability and obsolescence reviews
- Adoption profiles by sector and firm size
- Maintenance and skills gap analyses
- Evidence notes for industrial policy
What experts ask.
- 01
What does automation change about risk?
Which hazards it removes, which it shifts and which it creates.
- 02
How do automated systems fail?
Sensor faults, software errors, unexpected interactions and conditions outside the design envelope.
- 03
What is the human role?
How operators supervise, intervene and keep the skills to take over.
- 04
How resilient is a highly automated plant?
Whether it can degrade gracefully or continue safely when systems fail.
What drives the risk.
Automation holds processes within limits more consistently than people can, and removes workers from repetitive and hazardous tasks. Safety instrumented systems shut processes down automatically when conditions become dangerous.
The ironies of automation, first described in human factors research, remain relevant: the more reliable automation becomes, the less practice operators have, yet they are expected to take over in the rare, difficult situations automation cannot handle. Alarm floods during upsets can overwhelm operators.
Complexity creates hidden interactions. Software updates, integration of new systems and data dependencies can change behaviour in ways that testing did not reveal. Common-cause failures, where one fault disables several protective layers, are a particular concern.
Connectivity extends the system boundary. Remote operation, vendor access and cloud analytics improve performance but bring external networks inside the plant, linking automation to cyber resilience.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Hazard and operability studies | Deviations and their consequences | Depend on team expertise and scope |
| Reliability analysis | Failure rates of components and functions | Data for new technologies is sparse |
| Simulation and digital twins | System behaviour under scenarios | A model is only as good as its assumptions |
| Incident databases | Patterns in automation-related events | Under-reporting and inconsistent categories |
How it is studied, and what the methods cannot say.
- 01Systems engineeringIntegrated safety and security requirements · Factory and site acceptance testingApplied here
- 02Sensors, IoT and observation networksCondition monitoring · In-line quality inspection · Securing connected sensorsApplied here
- 03Optimisation and operations researchProduction scheduling · Energy-aware operations · Buffer and spare sizingApplied here
- 04Robotics and automationRobot cell risk assessment · Vision-guided handling · Collaborative workstation designApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is industrial automation?
What is overall equipment effectiveness?
Why do old control systems matter?
Does more automation always mean more safety?
Does the platform assess automation vendors?
What is a safety instrumented system?
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