NVIDIA Manufacturing Taiwan

Foxconn

Real-time Analytics

Foxconn needed a faster way to design, replicate, and optimize complex global factory operations because traditional facility planning, robotics programming, and environmental analysis were too slow, siloed, and expensive.

150x faster computational fluid dynamics (CFD) simulations
about 50%
minutes vs. hours

Reported impact

Business outcome

Foxconn reduced CFD simulation time by as much as 150x, expects factory setup and planning time to drop by about 50 percent, and gained faster production-line replication, KPI monitoring, and issue response across smart factories.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Foxconn's Fii Omniverse Digital Twin platform connects factory design data, production systems, robotics workflows, and operational telemetry so the company can simulate and optimize plants before and after equipment is installed. Built on NVIDIA Omniverse libraries and OpenUSD, FODT creates reusable virtual factory assets that Foxconn can migrate across global sites, while dashboards ingest MES, SFC, and automation data for real-time KPI monitoring. NVIDIA PhysicsNeMo accelerates CFD analysis inside the twin, shrinking thermal simulations from hours to minutes, and NVIDIA Isaac Sim, Isaac Lab, FoundationPose, and NVIDIA cuMotion are used to model robot workcells and AGV logistics before physical deployment. Foxconn also layers NVIDIA Metropolis video analytics agents onto the environment so managers can query factory footage, generate reports, and respond faster to issues across smart-manufacturing operations.

Data flow

Factory design assets are modeled in OpenUSD and Omniverse, MES, SFC, automation, and video feeds are connected into the digital twin, PhysicsNeMo and Isaac workflows simulate thermal and robotic behavior, and the resulting insights are pushed to managers for planning and real-time operations.

Solution architecture

8 components · 3 layers
  1. Compute
    • NVIDIA PhysicsNeMo Accelerates CFD and thermal analysis inside the digital twin.
    • NVIDIA Isaac Sim Simulates robot workcells and AI-powered robotics before physical deployment.
  2. Serving
    • FoundationPose Helps refine complex robotic perception tasks in simulated workflows.
    • NVIDIA cuMotion Plans collision-free robot motion for manufacturing tasks.
    • NVIDIA Metropolis Powers video analytics agents that monitor factory activity and generate operational insights.
  3. Orchestration
    • NVIDIA Omniverse Provides the digital-twin foundation for building and operating Foxconn's virtual factories.
    • Universal Scene Description (OpenUSD) Standardizes the reusable factory assets Foxconn migrates across production sites.
    • NVIDIA Isaac Lab Supports training and validation workflows for robotics inside the virtual factory.

Architecture clues

  • FODT embeds dashboards with KPIs from MES, SFC, and automation systems
  • Foxconn connects AI simulation, robot planning, AGV logistics, and video analytics inside one twin platform
  • The same approach is being used for factories producing NVIDIA GB200 Grace Blackwell Superchip systems

Evidence from the source

CFD simulations up to 150x faster than traditional methods.
The FODT platform connects virtual factories to real-world operational data, embedding dashboards with key metrics from manufacturing execution systems (MES), shop floor control (SFC), and automation systems.
The Fii Omniverse Digital Twin (FODT) platform utilizes NVIDIA Omniverse libraries to create comprehensive virtual replicas of its factories.
we believe that we can cut that down by about 50%
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    Foxconn customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins/
    Source type
    Vendor-published customer story
    Published
    Feb 4, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 4, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.