AI in production

AI as a turning point in the manufacturing landscape: Manufacturing companies are facing a new era in which artificial intelligence not only optimises processes but also lays the foundation for innovative production methods.

This guide provides insight into the transformative power of AI in manufacturing: Learn how AI increases efficiency, drives the development of new products and services, and what it takes to successfully integrate this key technology into your business.

Artificial intelligence (AI) refers to the creation of computer technologies that are capable of simulating human thought and decision-making processes. It is therefore much more than just software, which works according to a predefined pattern without deviation. AI, on the other hand, is characterised by its ability to adapt flexibly to new information and circumstances. It can:

• learn independently from data,
• make decisions and
• solve complex problems on its own.

AI thus opens up revolutionary possibilities in almost all areas of our society and economy: as the interface between human thinking and the impressive power of computers.

Artificial intelligence works on the basis of a process commonly referred to as machine learning.

1. This begins with AI systems being fed examples and empirical values. They analyse this information using a learning algorithm.
2. They can then independently recognise connections in this data and identify patterns.
3. Over time and with increasing amounts of data, these systems continuously improve and eventually reach a point where they can continue learning independently. A key element of this learning process are artificial neural networks, which are modelled on the biological neural networks of the human brain.

Machine learning (ML) is a discipline within artificial intelligence that enables computers to learn from data. This allows them to recognise patterns and make decisions largely autonomously, i.e. without extensive manual control.

Deep learning is a subfield of machine learning that uses artificial neural network models, which are modelled on the structure of the human brain, to discover even highly complex relationships in large data sets.

Natural Language Processing (NLP) is a technique based on machine learning that gives computers the ability to understand and process human language. This enables applications such as automatic translation, analysis of sentiment in texts and much more.

The use of artificial intelligence can create significant added value for manufacturing companies. This is particularly evident in a noticeable increase in efficiency in various areas of application of AI and the associated work processes:

• Identification of the causes of errors in the manufacturing process
• Knowledge management
• Maintenance forecasting (predictive maintenance)
• Quality assurance
• Automatic maintenance planning
• Making confident decisions based on sound data
• Adaptability and scalability

Artificial intelligence is no longer a futuristic concept, but has established itself as the driving force behind numerous innovations and efficiency gains in production. Its use could not only significantly increase the productivity and competitiveness of the German economy, but also optimise production processes in the long term.

In the following, we therefore explain the advantages of AI in manufacturing:

Artificial intelligence is revolutionising the way manufacturing companies handle their data. This is because one of the most fundamental capabilities of AI is its ability to analyse large amounts of data in a very short time and identify irregularities and patterns in the data.

But AI can do much more than that: it can draw conclusions from the data, predict future results and recommend actions to be taken. This AI application is based on three aspects that build on each other:

• The analysis of current data in real time to identify anomalies is called descriptive analysis.
• The insights gained from this form the basis for predictive analysis. This makes it possible to predict future events or states with a certain degree of probability based on historical and current data.
• Prescriptive analysis goes one step further by not only predicting what might happen, but also providing recommendations for actions that should be taken to achieve optimal results. In an even more advanced approach, it can even execute the recommended actions itself to ensure the best possible results.

Through these three levels of analysis – descriptive, predictive and prescriptive – AI provides a comprehensive view of operational data and promotes a proactive maintenance and operational strategy that significantly increases efficiency and productivity in manufacturing.

The use of AI in manufacturing brings even more advantages. AI opens up new opportunities for innovation and the development of new products and services.

This works because AI can analyse large amounts of data, providing insights that were previously difficult to obtain. These insights can lead to the development of products that are more precisely tailored to customer needs and allow companies to dynamically adapt their offerings to market conditions.

In addition, AI significantly accelerates research and development processes. It enables rapid simulation and testing of new products, shortening time to market. This increase in efficiency helps companies respond agilely to change and secure a decisive competitive advantage.

AI also contributes significantly to the optimisation of existing processes. By automating repetitive and time-consuming tasks that previously could not be performed cost-effectively by humans, AI conserves valuable human and financial resources. Companies benefit from increased cost efficiency, which allows them to free up funds and invest in research and development of new solutions.

However, the challenge lies in scaling and systematically implementing these technologies. Precise application of machine learning operations and the use of AI platforms are crucial to realising the full potential of AI.

The introduction of artificial intelligence in companies is a decision that needs to be carefully considered. While a survey by Statista from 2022 shows that 54% of companies already use AI in production, the move towards AI requires an individual assessment of your own company.

The advantages have already been explained: from increased efficiency to the development of new products. However, implementation also comes with challenges, which we would like to examine in more detail at this point:

A key challenge in introducing AI into businesses lies in the need to tailor each solution specifically to the unique needs and processes of the respective company.

This requires not only technical modifications, but also careful integration into the existing system environment in order to achieve significant improvements in efficiency and benefits. Identifying relevant applications and estimating the associated costs can be challenging.

Nevertheless, examining existing AI use cases that are similar to your business context can be extremely helpful. It provides an opportunity to learn from the experiences of others and gain insights into how AI can solve industry-specific problems and potentially create competitive advantages through early implementation of AI technologies.

The effective application of AI technologies depends not only on the quantity of data, but above all on its quality. The succinct principle of ‘shit in, shit out’ makes it clear that the quality of the data fed into AI systems directly determines the quality of the results.

Insufficient or irrelevant data leads to disappointing outcomes. It is therefore important to check the existing data for its suitability for specific AI applications before starting to implement an AI solution.

The introduction of AI requires high initial investment, not only in the technology itself, but also in the necessary infrastructure and skilled personnel. A thorough assessment of costs and benefits is crucial to ensure that the necessary resources are available for successful implementation.

The success of AI integration in your company depends largely on how willing your workforce is to embrace new technologies. To ease this transition and ensure that employees actively and successfully participate in the new AI-supported processes, well-thought-out change management is crucial. It is important to

• address concerns,
• promote acceptance and
• adequately support employees.

When using AI, companies must ensure compliance with data protection regulations and ethical guidelines. This is crucial for building trust and acceptance. Ethical considerations include:

• Accessibility and barrier-free access,
• Non-discrimination,
• Inclusion,
• Sustainability, and
• Usefulness of AI use.

These aspects must be carefully considered to ensure the responsible use of AI.

It is necessary to understand AI systems as complex systems that require specific knowledge and tailored approaches. Careful assessment and development of internal IT and AI skills is therefore essential to ensure the successful integration and use of these technologies.

Investing in training and partnerships with AI experts can make a significant contribution to realising the full potential of AI and strengthening the company's innovative strength.

Comprehensive AI consulting is essential for the successful implementation of artificial intelligence in your business. It is important that this consulting covers the entire process – from the initial idea, through planning, to the ongoing operation of the solution.

The goal is not simply to introduce a new technology, but also to impart the necessary knowledge and skills so that your company can work independently and effectively with the AI solution even after the consulting phase.

At MaibornWolff, we place great value on this type of comprehensive consulting, which starts with a detailed analysis of your individual requirements and guides you through all project phases. Our approach is as follows:

With the help of our Data Thinking Workshops, we work with you to develop customised data products. In these workshops, which are based on an initial consultation and last two to three days, you work with our digital designers, data scientists and other experts to define concrete next steps and strategies for your AI projects.

These workshops can be held at our premises, remotely or directly at your location to enable optimal collaboration.

Through practical preliminary tests, known as proofs of concept, we work with you to determine the suitability of specific intelligent services for your company's needs. Once we have defined together what constitutes a satisfactory result, we test these services with real data to evaluate their performance. This enables us to quickly and reliably determine whether the tested services meet your application requirements.

As part of a maturity assessment, we evaluate how advanced your organisation is in terms of processes, data and tools. Working with you, we develop measures to improve your data infrastructure and build an effective data strategy, highlight best practices and identify key initiatives for your business.

Certain AI methods are particularly well suited to creating cross-platform solutions that can be used across industries and in different business areas. A good example of this are knowledge management systems that enable voice-controlled access to internal company data.

We help you understand how your company can benefit from building such AI-driven platforms and how they simplify the integration and scaling of AI applications.

We explain the importance of machine learning operations (MLOps) for the success of your AI projects. MLOps optimises the entire lifecycle of machine learning projects – from data processing to model deployment. We would be happy to show you how it works.

The introduction of AI in manufacturing is about optimising processes, reducing the workload on employees and thus making work more efficient. Below, we present an example of success in the implementation of AI in manufacturing:

For an industrial company like Siemens, storage costs are a significant factor in production. Siemens therefore wanted to reduce these costs by more accurately forecasting future demand for products.

To achieve this, we developed an AI demand prediction platform for Siemens that production planners can use as a self-service tool. The initial goal was to make accurate predictions for 100 different products in one plant, with a view to later expanding the platform to the entire product range and other plants.

The challenge was that each product has its own time series, which means that demand for each product is different and can change constantly. In addition, new products could be added or existing products discontinued. This made it difficult to find a single algorithm for all products. We therefore opted for automated machine learning (AutoML), which tests various machine learning algorithms and automatically selects the best model. This enabled us to find the right model for each product and achieve the goal.

Companies that want to take the plunge and integrate artificial intelligence into their production processes should seize the opportunity and explore the possibilities. The use of AI has the potential to significantly optimise business processes and take performance to a new level.

It is crucial to set clear goals and carefully select suitable areas of application. Whether artificial intelligence ultimately leads to a competitive advantage for your company will be determined largely by your individual requirements and strategic implementation.