Artificial Intelligence Efficiency, Innovation And Cost-Effectiveness: AI Agents in the Smart Factory

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More efficient data management, fewer production downtimes and greater competitiveness—artificial intelligence is revolutionizing the smart factory. However, the success of these technologies requires clear goals.

What sounded like a vision of the future a few years ago is already a reality today. In close connection with the Industry 4.0 concept, the first smart factories emerged back in the 2010s. These intelligently networked factories combine data, people and machines to create an efficient overall system. This results in concrete efficiency gains, cost savings, automated processes—and therefore real competitive advantages.

Ever shorter product cycles, the collapse of established supply chains and customers' desire for ever more individualized offers: The challenges facing industrial companies are manifold. At the same time, weakening sales, high transformation costs and fierce competition in sectors such as the automotive industry are making it necessary to cut costs on a massive scale.

One solution for increasing efficiency, profitability and innovative strength at the same time is the smart use of data and the use of artificial intelligence. Whether in the area of production, digitalization or the entire corporate and core structure: the strategic use of data, for example in the use of AI agents, is increasingly permeating all business areas and forms the basis for more efficient processes, well-founded decisions, better planning and the development of new digital business models.

When Data Takes on Substance: The Foundation of the Smart Factory

The first step towards becoming a data-driven company and using artificial intelligence is to ensure the required data quality. If the quality of the data is only determined after the start of the AI initiatives, many expensive AI projects can no longer be saved.

This is often caused by years of failing to tackle fundamental data (quality) problems. The technical debts accumulated as a result significantly jeopardize any AI ambitions. So it's high time to put the issue of data at the top of the agenda.

This first requires a standardized database that is accessible to everyone. The diverse data sources of industrial companies—from production machines to customer portals and industrial plants—provide operating data, which is often available in varying quality and stored decentrally in different systems. In order to make this data usable for AI agents and very specific use cases such as predictive maintenance, it must be standardized and organized in a central repository.

One of the key aspects of good data management and a clean database is the introduction of a "single source of truth"—a central, reliable data source that enables data to be exchanged in real time across all levels of the company. This prevents data silos and promotes well-founded decision-making through high-quality and meaningful data analyses.

In order to communicate data in real time, it is necessary to integrate large volumes of data from a wide variety of sources, overcome fragmented systems and break down silos. This is the only way to create a uniform view of business processes and to optimize processes effectively. And this is precisely where modern smart factory concepts come in.

Event-based data streams, data brokers and standardized data models such as Unified Namespace (UNS) are the components of the factory of the future that will enable more informed decisions and ensure data democratization and scalability.

The advantages for companies are obvious: the integration of a wide variety of data sources and formats on a central platform improves interoperability between departments and systems.

• Firstly, the system enables more informed decisions by providing real-time data and contextual information.
• It reduces the complexity of data systems by standardizing data management and communication.
• The democratization of data access and the creation of a shared database also promote innovation throughout the company.

On their way to the smart factory, companies should first define clear goals for data management and use. Technologies make sense where they contribute measurably to the achievement of corporate goals and should not be an end in themselves.

A gradual and targeted implementation facilitates initial success and creates trust. Continuous monitoring also makes it possible to make adjustments and ensure the relevance and effectiveness of the system in the long term.

AI Agents in Action: Machines that Repair Themselves

AI agents are no longer science fiction, but are already in use in many companies today, whether in quality control, the coordination of complex supply chains or the automation of customer service processes.

One of the most exciting areas of application for AI agents in industry is predictive maintenance. Downtime not only impairs production processes and extends delivery times, but also causes immense costs—the damage for manufacturers runs into billions every year.

This is where AI agents come in: They analyze real-time machine data such as temperature, vibrations and usage patterns in order to detect impending failures at an early stage and automatically initiate appropriate measures. This proactive approach pays off for companies: For example, an AI-driven maintenance system saves a US car manufacturer 2.5 million dollars a year by avoiding unexpected breakdowns and optimizing repair schedules.

However, AI agents are not only able to avoid production downtimes. AI can also significantly optimize processes in the area of quality inspection. For example, one of the world's leading manufacturers of components for vehicle interiors laid the foundation for a completely new way of working with Visual Inspection AI.

With the ability to identify potential defects and anomalies automatically, quickly and accurately, the company has been able to increase overall quality and create a cycle of continuous improvement through quality metrics. Thanks to this solution, the teams can focus on more strategic tasks, which not only contributes to employee retention, but also makes it easier to recruit new specialists.

Strengthening Understanding And Trust: AI is Also A Question of Culture

In addition to a clean database and use cases that generate as much added value as possible, one thing should not be forgotten on the road to the smart factory: The human factor. Keyword AI skepticism—a lack of trust and a lack of technical knowledge are the main reasons why people do not use AI and still have reservations, according to a recent survey by bitkom. However, for a new technology to become established and be used as efficiently as possible across departments, it needs to be understood and accepted within the workforce.

Companies are therefore not only required to provide the technological prerequisites, but also to take care of change management as well as training and further education. Transparently communicating the goals of data and AI use to the teams and equipping them with the necessary expertise should be part of every company's AI agenda.

Dr. Markus Müller is Managing Director Industry Germany at GFT Technologies

AI in the industry

ETMM Dossier – Artificial Intelligence