Artificial Intelligence and Manufacturing Companies
Artificial intelligence (AI for short) is a key technology that can handle a wide range of complex tasks. AI is also changing all areas of the value chain in logistics and production. What opportunities and risks does AI hold for manufacturing companies?
AI in production enables a decisive step towards operational excellence. Through the intelligent use of AI technologies, production processes can be controlled more efficiently, resources can be better utilized and costs can be significantly reduced. In the supply chain, AI ensures greater transparency, more precise forecasts and faster responsiveness. At the same time, employees benefit from a noticeable reduction in repetitive tasks, which increases their satisfaction and focus on value-adding activities. AI thus becomes the key to flexible, sustainable and competitive production.
More Efficient Production Processes
Optimized Use of Resources
Cost Reduction
Greater Transparency
More Precise Forecasts
Improved Responsivness
AI Applications In The Industry
- AI-supported production:
AI optimizes production processes through intelligent control in real time – for example by automatically adapting to demand, material availability or machine utilization. This increases efficiency, lowers reject rates and measurably reduces operating costs.
- Predictive Maintenance:
AI uses sensor data to detect signs of potential machine failures at an early stage. Planned maintenance replaces unplanned downtime – this saves costs, improves system availability and reduces the workload of the maintenance team.
- Smart Logistics:
AI ensures dynamic route and warehouse optimization, analyses supply chain risks and accurately forecasts material requirements. This reduces storage costs, avoids bottlenecks and makes the supply chain more robust and flexible.
- Topfloor – Decision-making Level:
AI provides decision-makers with data-based, simulation-supported insights in real time – from production bottlenecks to capacity planning. This improves strategic management and promotes a fact-based corporate culture.
- Sustainability:
AI actively supports sustainability goals through energy consumption analyses, resource efficiency and minimizing waste. At the same time, it helps to meet regulatory requirements and reduce CO₂ emissions in production.
Introduction of AI in the company
In addition to technological feasibility, a responsible, strategically well thought-out approach is crucial when introducing AI in production. Companies should clarify at an early stage which processes are suitable for AI, how data quality and availability can be ensured and which skills need to be built up internally.
In addition to the great opportunities offered by AI, key risks and challenges must also be kept in mind:
Data Quality
One of the biggest hurdles is data quality: AI systems are only as good as the data they are trained with – incomplete, incorrect or biased data can lead to incorrect decisions. It is therefore important to ensure that the data used for AI models is complete, consistent and accurate, is not outdated and adequately represents the population – or the use case. Without high-quality data, even the most advanced AI system cannot deliver meaningful results.
Transparency of AI Algorithms
The transparency of AI algorithms also plays an important role, particularly in safety-critical applications or when employees need to understand decisions. In this context, the EU AI Act becomes relevant, which places strict requirements on documentation, human supervision, explainability and data protection, depending on the risk category. A careful risk assessment and governance structure is mandatory, especially for “high-risk” applications – such as safety-relevant production processes or automated personnel decisions.
IT Security
Another important aspect is IT security to ensure protection against manipulation.
Acceptance
It is also crucial to consciously deal with potential acceptance problems within the team. It is therefore important to understand AI not as a pure IT project, but as a holistic transformation with a clear ethical, legal and organizational framework.
What’s next for AI in the industry?
The use of AI in the logistics and production industry is only just beginning – far-reaching changes can be expected in the coming years. Advances in generative AI, autonomous systems and edge computing will make production lines even more flexible and self-controlling. In logistics, AI-supported simulations, route optimization and real-time forecasts will enable a fully networked, resilient and sustainable supply chain. At the same time, collaboration between humans and machines will continue to develop – through assistance systems, voice control and intelligent automation. Although it is not yet possible to predict in detail how these developments will actually take shape, there are many indications that AI will fundamentally change the industrial world – and our entire working environment – in the long term. Companies that invest in data-based processes, AI expertise and secure infrastructures at an early stage can secure decisive competitive advantages in the long term.
Artificial Intelligence at concircle
AI product integrations
We have recognized that AI creates competitive advantages and therefore want to offer our customers state-of-the-art applications. Examples of the efficient integration of AI include our innovative ERP add-ons for detailed planning: conOperationScheduling (conOS) and the conOperationScheduling CLOUD (conOS.CLOUD) version developed for Public Cloud.
These applications can visualize and analyze data with AI support, automate planning, analyze errors, etc. Watch two short demo videos:
AI Showcases
Artificial intelligence is also used in augmented reality to improve these experiences. In manufacturing, augmented reality is used to visualize data, for example by displaying work instructions. We deal with augmented reality and AI as part of our research.
Our developers also like to look for potential AI use cases internally. For example, this is how our dashboard for expected coffee consumption at the Vienna site was created.
As playful as this example may seem, it shows very concretely how data, sensor technology and AI can also be combined in industrial scenarios – for example to predict machine or material consumption, personnel deployment or maintenance requirements. The showcase makes it clear that AI does not have to be abstract – it can be used in a practical, tangible way and with direct benefits.
Herbert Kaintz
Team Lead Digital Enterprise Core Software Engineering
Herbert leads the software engineering team in the Digital Enterprise Core area. He deals with a wide range of technologies and application areas – from ABAP to Java and web programming.
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