All Foundries

商品管理と取引

データに基づくインサイトと、セキュアで機動性の高いプラットフォームにより、グローバルな商取引を支えるトレーディングエコシステムを変革します。

顧客とマーケティング戦略

パーソナライズされたつながりある体験を通じて顧客エンゲージメントを再構築し、信頼と長期的な価値を築きます。

デジタルエンジニアリング

アイデアをスケーラブルなデジタルソリューションへと転換し、デザイン・テクノロジー・エンジニアリングの力で加速させます。

ステーション・スタジオ

デジタルイノベーションでモビリティハブを再定義し、移動中の人々と企業に、よりスマートでサステナブルな体験を創出します。

サプライチェーン＆インダストリー5.0

変化を予測し、目的を持って効率性を実現する、強靭で知的な供給ネットワークを構築します。

サステナビリティ

責任あるイノベーションを推進し、組織がネットゼロ目標を達成し、将来の世代に良い影響をもたらすよう応援します。

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Next-Generation Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

Utility engineers testing industrial network and protection equipment in a controlled commissioning workspace beside a digital substation.

OT Firewall Validation for Digital Substations

In a digital substation, a firewall rule is never just a cybersecurity setting. It can determine whether protection, control, engineering and supervisory systems continue to communicate as intended. This case establishes a disciplined validation approach that tests security policy, segmentation and operational communication flows before changes are accepted in live operations.

OT Firewall Validation for Digital Substations

Transmission cybersecurity engineers test protection, communications and monitoring equipment inside a substation control room while the live switchyard remains visible through the doorway.

OT Cybersecurity Monitoring for Transmission Networks

Transmission networks cannot trade operational availability for cyber visibility. This engagement combined OT event monitoring, centralized security analytics, industrial network testing, vulnerability assessment and incident response preparation to give transmission stakeholders a stronger basis for understanding cyber activity, validating controls and protecting service continuity.

OT Cybersecurity Monitoring for Transmission Networks

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Vision based Robotic Picking

Small parts and endless variation leave little room for a missed pick. Simulation, vision and control turn a two robot cell into one reliable process.

会議のスケジューリング

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Small, irregular parts leave no margin for unreliable picks Industrial scale demands more than motion

Why this "Vision based Robotic Picking" challenge?

A scalable pick and place process for small, highly variable metallic components needs robust pose estimation and reliable grasping

Simplifying two robots into one cycle called for simulation before any shop floor integration.

主なメリット

Simpler cells start with smarter perception

Bring new variants in faster

Model tuning and parameterization cut the effort and lead time needed to introduce new part references.

Test virtually before touching production

Simulation first validation reduces physical iterations, commissioning effort and disruption during deployment.

Do two steps with one robot

A single robot architecture lowers integration and maintenance complexity while improving cell availability.

Make every pick count

Stronger perception and control reduce mispicks, improve consistency and open the door to lower scrap.

NTTデータのサポート体制は？

Build the process before the cell

NVIDIA Omniverse creates a high fidelity simulation of the robot, gripper and target parts with realistic behavior.

Teach vision to read variation

NVIDIA Foundation Pose models get finetuned on target components, using a one shot approach to adapt to new variants.

Engineer the full execution cycle

Task execution and cycle management get developed while control logic tunes toward overall pick and place efficiency.

Strengthen grasping for harder parts

The solution extends to components such as the slider body and puller, refining gripper strategy for reliability.

Industrial robotics platform assigning different robots to handling, assembly and inspection tasks across a manufacturing environment, enabling dynamic task allocation, coordinated automation and more efficient use of robotic resources.

Validate before industrializing

The solution gets integrated, tested and tuned in a lab environment before scaling toward the shop floor.

Industrial robot operating alongside a digital twin simulation that mirrors its movements, enabling teams to test robotic trajectories, validate behavior and optimize automation before deployment in the physical production environment.

実証済みの効果

Faster onboarding, steadier picking Simulation first design shortens variant onboarding and lowers deployment risk across the cell.

重要な結果

Operational gains from a simulation first cell

Faster variant onboarding

Model tuning and parameterization replace full reengineering when a new part reference comes in.

Shorter time to deploy

High fidelity simulation cuts physical iterations and commissioning effort before shop floor integration.

Better asset efficiency

Moving from two robotic arms to one cycle lowers integration and maintenance complexity on the floor.

Steadier picking quality

Improved pose estimation and control consistency reduce mispicks and support lower scrap rates.

Scale Robotic Picking Without the Guesswork

Ignite the conversation that turns intelligent vision into a more reliable picking operation.

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