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AWS Manufacturing Japan

OMRON

Under its long-term vision, a project called "Shaping the Future 2030 (SF2030)", OMRON aims to “continue to create innovation driven by social needs through automation to empower people.” In the midterm management plan for the fiscal years 2022–24, the company is working on transforming two things: its business and its organizational capabilities.

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“We were able to reduce the time it takes from procurement to construction from months to 5 minutes, and this has had a dramatic effect,” says Tsuda.

Reported impact

Business outcome

By using AWS, the time to build an HPC environment has been greatly shortened, and it is now possible to respond quickly to researchers’ requests.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

OMRON manufactures four types of products: industrial automation, healthcare equipment, socialsystems, and electronic components. The company’s research and development (R&D) department builta high performance computing (HPC) environment used for things such as machine learning (ML) andcomputer-aided engineering (CAE) analysis on Amazon Web Services (AWS). It achieved theconstruction of research environments in a short period of time and a reduction in analysis time. "In the sensing and healthcare fields, there is an increase in research on image recognition using ML and simulating human movements. Complex simulation models are also increasing in the fields of data and CAE analysis,” says Manabu Tsuda from the business process platform design department, the technology and intellectual property headquarters at OMRON.

Data flow

The design policy was finalized from April to July 2022, and after constructing a production environment, it was released as the first step in October 2022 to two divisions: the department that researches ML and image recognition within the technology and intellectual property division, and the department that researches CAE and data analysis.

Solution architecture

3 components · 2 layers
  1. Compute
    • Amazon SageMaker Supports training, fine-tuning, and managed inference for machine learning workloads.
    • Amazon EC2 Provides elastic compute capacity for application, analytics, and infrastructure workloads.
  2. Governance
    • AWS Control Tower Sets up and governs multi-account environments with centralized guardrails.

Architecture clues

  • OMRON configures a multi-account environment according to each research theme, such as confidential research, HPC computation, and co-creation with other companies, and uses AWS Control Tower for overall account control.
  • The existing R&D and HPC environments on AWS are connected using AWS Direct Connect , a service that creates a dedicated network connection to AWS, to verify security.
  • “The decisive factors for choosing AWS were that it is used by financial institutions and government agencies with strict security requirements and that there are plenty of features to protect confidential information.” In addition to the abundance of managed services and the high degree of freedom of development, another factor was that OMRON SOFTWARE (OSK), OMRON’s software development company, began training its engineers on AWS in 2019, so they had accumulated knowledge on in-house production development.

Evidence from the source

"In the sensing and healthcare fields, there is an increase in research on image recognition using ML and simulating human movements.
Complex simulation models are also increasing in the fields of data and CAE analysis,” says Manabu Tsuda from the business process platform design department, the technology and intellectual property headquarters at OMRON.
However, in the existing on-premises environment, OMRON felt limited in procuring computational resources.
To achieve the goals of SF2030, OMRON needed to provide an R&D environment that met the needs of developers.
Under its long-term vision, a project called "Shaping the Future 2030 (SF2030)", OMRON aims to “continue to create innovation driven by social needs through automation to empower people.” In the midterm management plan for the fiscal years 2022–24, the company is working on transforming two things: its business and its organizational capabilities.
Transparency Evidence & methodology 85/100 reference value · 87% 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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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
    5 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    OMRON customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/omron-case-study/
    Source type
    Vendor-published customer story
    Published
    Feb 6, 2024
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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