Manufacturing 2030: Humanoid robots and autonomous factories

Humanoid robots on the rise

In a recent analysis, NTT Data, a provider of AI, digital business, and technology services, identified two technological megatrends that could significantly change industrial manufacturing by 2030: humanoid robots and autonomous factories. These developments promise efficiency, flexibility, and new business models - but also present enormous challenges for the industry.

Humanoid robots represent a new stage of automation. Unlike traditional industrial robots, which rely on manually programmed routines and only work reliably in strictly controlled environments, humanoid systems make independent decisions and adapt to changing situations. They are modeled after the human body in form and movement and act accordingly flexibly.

This is made possible by advances in multimodal AI models that combine vision, hearing, language, and motor skills. These systems recognize objects, understand verbal instructions, plan complex sequences, and break them down into precise subtasks. The learning process of the robots is also fundamentally changing:

A three-stage development model forms the basis today. Large AI models first analyze vast amounts of real and synthetic movement and sensor data. From this, they derive basic rules for grasping, balancing, or navigating. In a second step, the systems refine their skills in digital twins - physically precise simulations that depict even complex scenarios like climbing stairs, varying weights, or unforeseen events. Finally, the learned skills are transferred to powerful onboard computers to link perception, planning, and motor skills in real time.

The current generation of humanoid robots is limited to simple tasks such as grasping, sorting, or moving materials. Complex assembly processes, fine motor tasks, or dynamic human-machine interactions remain a technological challenge for the time being. Advances in sensor technology, safety mechanisms, and real-time planning are indispensable here.

Potential applications and limitations

Humanoid robots are particularly of interest where manual work steps dominate, there are personnel shortages, or ergonomically demanding tasks hinder efficient production. Demographic change further amplifies this need. Interest is growing, especially in manufacturing areas with high variance, limited space, and continuous material flows.

In automotive production, humanoid robots are already taking on tasks in the logistics and material flow environment almost independently. Nevertheless, it is true: "These robots can perform tasks that previously could only be carried out by trained specialists. However, their human body structure is not the right solution for all industrial applications." Often, specialized systems or alternative automation concepts are better suited - according to the principle "form follows function".

Steps to successful implementation

A well-thought-out approach is essential to meaningfully integrate humanoid robots into existing production structures. First, companies should analyze which processes can realistically be automated and where human fine motor skills or experience are still required. Based on this, pilot projects with clear success criteria can be defined.

A central issue is data harmonization - from motion and quality data to machine states and real-time information from the material flow. Heterogeneous data formats often hinder efficiency and lead to wrong decisions. In addition, robust IT/OT security mechanisms are essential. Secure communication channels, segmented networks, clear approvals, and transparent audit logs form the foundation.

Since the market for humanoid systems is currently not very standardized, a modular architecture with open protocols and interchangeable AI modules is recommended. This can minimize technological dependencies. A KPI-based approach regarding cycle time, availability, and error rates also helps to critically assess economic efficiency and avoid inflated expectations.

Autonomous factories as the next stage of evolution

Even more profound than the use of humanoid robots is the step towards the autonomous factory. The vision: production facilities that completely self-organize - machines that control, coordinate, and optimize processes without human intervention. This concept is often summarized under the term "dark factory" - a production environment that theoretically no longer requires light, as no humans are physically present. In reality, lighting remains necessary for cameras and optical sensors.

Unlike Industry 4.0, where only partial processes are automated and human decisions remain essential, the autonomous factory gradually transfers these tasks to intelligent systems. The term "autonomy" thus describes a higher level than mere automation.

The backbone of this development is a closely networked infrastructure: the Internet of Things (IoT) acts as a nervous system that connects machines, workpieces, means of transport, and sensors. Data continuously flows into central systems, where AI-based platforms act as decision-making instances. They recognize patterns, predict disruptions, adjust production plans, and dynamically control entire processes.

A key element is the so-called "autonomous production twins" - digital twins of the real factory that can not only simulate but also actively make decisions. This includes delayed deliveries, machine failures, or short-term prioritization of orders. The advantages are obvious: higher productivity, fewer errors, and reduced operating costs.

Prerequisites for the transformation

The implementation of an autonomous factory requires a profound infrastructural transformation. Existing Industry 4.0 lines cannot simply be "upgraded." Especially older systems with proprietary controls or complex histories require enormous integration efforts.

A methodical approach is crucial. The basis is a reliable data foundation on machine conditions, material flows, quality, energy consumption, and logistical movements. Uniform data models across machines, ERP, and MES systems, edge architectures with latencies under ten milliseconds, and unique identities for workpieces and transport units form the foundation.

In terms of security, companies must rely on segmented OT networks and zero-trust principles, implement secure APIs, establish OT protocol monitoring, and define governance rules that determine when AI systems can act autonomously and when human approvals are necessary.

A central risk is the fragmentation of factory IT. To avoid dependencies, open communication standards such as OPC UA or MQTT, interoperable digital twin models, containerized AI workloads, and modular control technology are recommended. Only in this way can later supplier changes, new functions, or scaling be realized flexibly.

Outlook and strategic importance

“Humanoid robots and autonomous factories are still more of a vision with initial pilot projects than a widespread reality today. However, industrial value creation will change significantly when these robots become the link between humans and machines and factories independently control entire production systems. The advantages are obvious: overall equipment effectiveness increases, as do quality and flexibility. This makes completely new business models possible. However, manufacturers should not underestimate the risks. Technical hurdles, lack of standardization, and missing governance can lead to bad investments, failures, and disappointments in ROI,” emphasizes Jochen Gemeinhardt, head of production & supply chain at NTT Data DACH.

He adds: "Therefore, a pragmatic approach is crucial: data-driven pilot projects, robust IT/OT integration, transparent security and decision rules, and a modular architecture that reduces technological dependencies and scales gradually. Companies that invest today will secure competitive advantages in a manufacturing landscape that will fundamentally change in the coming years."

Edited by Stefan Weinzierl, based on "NTT DATA: Humanoid robots and autonomous factories - how two megatrends will redefine industrial manufacturing by 2030"