All Foundries

Isometric blue-and-white model of an energy and logistics network with a docked cargo ship, solar panels, factories, office buildings, pipes, trucks, workers, and stacked materials.

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。

Isometric illustration of a bright modern office with glass meeting rooms, people working at desks, large data dashboards, presentation screens, plants, and studio lights.

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。

Isometric 3D smart city model with white-blue buildings, cranes, power grids, map pins, and people using tablets around connected infrastructure platforms.

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。

Isometric 3D model of a white-and-blue EV charging station with canopy, office buildings, small cars, delivery van, chargers, planters, and tiny people.

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。

Isometric 3D logistics campus with white modern warehouses, blue-green accents, workers, delivery vans, a loading dock, and stacked shipping containers.

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。

White-and-blue sustainable campus with solar panels, a wind turbine, EV charging stations, loading dock trucks, trees, and small groups of people.

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。

Isometric smart factory scene with white buildings, robotic arms on assembly lines, drones, autonomous carts, workers, control screens, and a fenced power substation connected by blue paths.

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.

Isometric 3D smart infrastructure scene with white roads and buildings, blue data lines linking control rooms, vans, a rail crossing, drone, workers, and trees.

Next-Gen Infrastructure

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

Robotics Cloud Platform

Manufacturers often run robots from several providers, and every brand demands its own specialist skills. NTT DATA built the Robotics Cloud Platform, an agnostic control layer for robotic arms independent of proprietary systems. ROS and a digital twin simulate each movement, and AI adds new skills before a robot reaches the production line.

Robotics Cloud Platform

Multi-UAV system for autonomous inspection

Critical infrastructure and private areas need constant watching, and fixed surveillance leaves gaps across large sites. NTT DATA applied artificial intelligence to build a multi-UAV system for autonomous patrolling. Reinforcement learning optimizes every flight path, and computer vision designed for bad weather keeps inspection results reliable.

Multi-UAV system for autonomous inspection

お問い合わせ

Robotics Cloud Platform

Robot brands: Multi-vendor

One control layer for arms from different manufacturers.

Line testing: Digital twin

Teams simulate the whole production line before go-live.

New capabilities: AI-based

Self-learning of movements and perception from camera and sensor data.

Task assignment: User-driven

Operators pick a robot and assign its task in the production chain.

Robotics Cloud Platform

Robot choice without proprietary lock-in

目的

Work with robots from different manufacturers without needing to know the specific characteristics of each one.

機会

Teams pick the robot best suited to each manufacturing need, whatever the brand, and test the line before production starts.

One brand, one skill set

Manufacturing keeps asking for more automation. Each robot provider brings its own control system, so the team needs product-specific knowledge for every brand on the floor.

主な課題

1: Mixed fleets. Companies work with several robot providers at the same time.

2: Product-specific skills. Each brand requires its own expertise to program and run.

3: Trials on live equipment. New movements had no safe space for testing before production.

4: Choices tied to the brand. Robot selection followed the installed system more than the manufacturing need.

解決策

An agnostic control platform with a digital twin

NTT DATA built the platform on the ROS framework and a digital twin, using NVIDIA Omniverse, MQTT, OPC UA and Python.

Vendor-agnostic control

One interface controls robotic arms from different manufacturers, on cloud and data center infrastructure NTT DATA designs, builds and operates.

Digital twin simulation

ROS and the digital twin reproduce robot movements so the team develops and tests new capabilities in controlled situations.

AI capabilities

Algorithms add self-learning of movements for new tasks and environmental perception based on camera and sensor data.

Task assignment

Users select the robot of interest and assign its tasks within the production chain.

影響

Lower production costs and more freedom of choice

Teams supervise, simulate and test the movements of the entire production line before go-live, saving time and cost. Manufacturers choose the robots best suited to their needs, whatever the producer’s brand, and NTT DATA assesses each option on neutral terms. The digital twin combined with Robotics Cloud reduces production costs and increases competitiveness.

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