Artificial Intelligence

Recognizing and exploiting the potential of AI

Learning and Qualification 4.0

Making employees fit for Industry 4.0

International activities

Benefiting from international cooperations

Best Practices

In our competition, we award innovative Industry 4.0 solutions

Industry 4.0

The fourth industrial (r)evolution

STARTUP THE FUTURE

The matchmaking format for companies and startups

Our partners

Industry 4.0

The networking of all activities in the value chain

What does Industry 4.0 mean?

People, machines and industrial processes are intelligently connected – that is Industry 4.0. Digitalisation has already arrived in industry and is developing rapidly. Increasingly it is finding its way into all sectors. This is accompanied by enormous opportunities and chances, but also risks and challenges. There ar megatopics such as artificial intelligence and cybersecurity. Training, further education, work and leadership are equally subject to the extensive changes and require new concepts.

Industry 4.0 – simply explained

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Industry 4.0 made in Baden-Württemberg

Competition "100 Places for Industry 4.0 in Baden-Württemberg"

In this competition, we were looking for outstanding solutions from industry and scientific research that are already being implemented. 200 projects from various sectors have been awarded. These best practices demonstrate how Baden-Württemberg is driving digitalization forward. At the same time they are a source of inspiration for other companies. Learn more

What innovations were awarded?


(1) new products, components, machines and plants,
(2) software and networking solutions,
(3) production and organisational processes, and
(4) new service offerings for industry and private customers.

More use cases and services

Industrie 4.0 map

The "Industrie 4.0 Map" of the Industrie 4.0 Platform depicts Industry 4.0 best practice examples, test centers, and information and support services throughout Germany.

Map "Wirtschaft digital"

On the map of the chambers of commerce, companies will find best practices as well as suitable support and consulting services.

There are neither universal "recipes" nor "blueprints" for digital transformation. Every company must find its own approach, answering the following question: How can we use digitalization to make the company, its employees, products and services better? With a digitalization strategy, new technologies, processes, and forms of work can be transformed from a secondary task into a core topic.

Jana Eiser-Mauthner

Project Manager

Artificial Intelligence Machine Learning Deep Learning Data Mining Neural Networks

Classification and terminology

FAQ on
Artificial Intelligence

AI map

Contact

Recognizing and exploiting the potential of Artificial Intelligence

Introduction to the topic of AI

Artificial Intelligence (AI) is currently attracting a great deal of attention in society and business worldwide. It provides companies with both the opportunity to offer new products and services on the market and to implement developed solutions in their own processes. At the same time, AI poses a challenge for companies and raises various questions: How and where can AI be used in a meaningful way? How and by whom does the implementation succeed? How can employees be involved in the process and how can they be trained?

FAQ AI

FAQ about artificial intelligence within the framework of digital transformation for manufacturing companies

FAQ

Häufige Fragen

AI is a sub-area of computer science. As there is no clear definition for the term “intelligence” itself, there are various definitions for the term AI. In addition, understanding of AI has been developing on an ongoing basis. In general, AI engages with tasks or problems which human intelligence is normally required to solve. These processes aim to create self-learning intelligence in the form of a system that can work independently to develop solutions for a problem. In this regard, AI includes a range of methods from mathematics, statistics and computer science, which reconstruct particular aspects of human intelligence, for example cognitive or sensorimotor intelligence. This reconstructed human intelligence is only limited to a very specific area of application.

It is important to note that AI does not only relate to simple automation in which an application is programmed to make a particular decision in the event of a particular input.

Read more:

See Question 3: What other terms relating to AI do I need to know and where can I find a clear explanation of the relevant terms?

AI in manufacturing is a key technology, making it part of digital transformation and therefore a significant element of Industry 4.0. Within the framework of discussions relating to Industry 4.0 and the respective application scenarios, AI plays a key role, for example in the analysis of large data volumes and in automated decision-making. Thanks to comprehensive networking and the collection of large volumes of data, which are developments associated with Industry 4.0, AI solutions are improving in terms of performance and may be used in the manufacturing environment.

An example of this is predictive maintenance: Manufacturing systems and machines will be maintained in a predictive manner on the basis of intelligent data analyses instead of at fixed intervals. An application based on AI provides additional opportunities here in the field of data analysis and pattern recognition, which enables maintenance times to be optimised.

Read more:

PWC-study: Predictive Maintenance 4.0

Huber: Case studies on Predictive Maintenance (in the Data Science book)

Data Mining refers to the use of algorithms for the analysis and processing of large, complex and heterogenous data sets (“big data”) to generate new information. In this regard, individual data components are brought together, in particular, in order to obtain new findings.

Machine learning is a sub-area of AI. The focus is on the development of algorithms that imitate the learning behaviour of the human brain for machines. The algorithms learn from experiences and can find solutions for new problems or evaluate unknown data sets.

Deep learning is a sub-area of machine learning that is characterised by complex solution methods. It uses artificial neural networks, in which multiple data processing levels can be combined.

Models that are based on artificial neural networks  constitute the networking of nerve cells from a brain in an abstract form for information processing.

Weak artificial intelligence solves problems using methods that have been developed and optimised for the specific purpose (e.g. spam filters).

Strong artificial intelligence, also known as superintelligence , is superior to humans in terms of intellectual abilities. On the basis of the current knowledge, strong artificial intelligence does not yet exist.

For some of the terms that are listed, a concrete definition has not been established.

Read more:

Federal government: Artificial intelligence strategy of the federal government

Start-up scene: Artificial intelligence glossary

Elektronikpraxis Vogel: Artificial intelligence specialist terms

VDMA: Machine learning in machine and systems engineering

Fraunhofer Society: Artificial intelligence trends

Fraunhofer-Allianz Big Data: 2017 potential assessment for AI

AI is no longer just something for the future. AI is already making it possible to achieve significant progress in various areas of applications, for example in industry, the transport sector, healthcare and other areas of life. For AI applications it is important to identify solutions from the perspective of need and to evaluate AI as a tool for optimisation or an addition to business plans. In some cases, the optimisation may also be achieved using traditional processes or statistical methods. If a clear benefit can be identified for the company, applications may create a competitive edge now, e.g. thanks to an extended product portfolio or cost reductions as a result of a reduced level of material stock. In addition, the framework conditions have developed in favour of AI applications in recent years: Cloud solutions allow data to be made available almost anywhere, the computing capacity of hardware continues to increase and the costs for data storage are significantly lower now than in the past.

Read more:

VDMA: Artificial intelligence position paper

In our personal lives, AI-based apps and assistants have been well-known for a while now (e.g. digital voice assistants on smartphones/smart home). Currently, typical areas of application for AI in the industrial environment are primarily in places where lots of data is (or could be) collected and analysed. The evaluation of large data volumes is one of the areas in which AI provides new opportunities in comparison with traditional business analytics solutions. In the field of quality assurance, optical inspections using AI applications are popular, which enable error or process analyses using image recognition. An energy management system that is equipped with AI is capable of analysing and predicting energy intake and distribution in the factory on a continuous basis in order to then adapt the manufacturing process and optimise the energy costs. Thanks to predictive data analysis, machines and systems can be maintained in a manner that is more well-suited to requirements and therefore reduce downtime. Outside of the company, the manufacturers of the machines and systems can provide additional services that are fed with the customers’ machine data on-site.

AI provides companies with support, in particular in areas in which humans and systems had previously reached their limits. In terms of analysis, AI applications generally use very large data volumes, which makes the identification of anomalies and the prediction of faults more accurate. With error reporting, AI makes it possible to identify causes more quickly and ascertain correlations that may have previously not been identified. AI can also help to plan manufacturing processes and capacities better, which has a positive effect on costs. Self-learning algorithms can make existing technologies even better. In this way, driverless transport systems can find the best path through the manufacturing facility at any time.

A BMWi projection indicates that an increase in the gross value added of EUR 31.8 billion for manufacturing businesses in Germany can be expected from 2019 to 2023 thanks to AI, which constitutes an enormous benefit for companies. A PWC study predicts a rise in the overall gross value added of EUR 430 billion.

Read more:

VDMA: Artificial intelligence position paper

BMWi study: The potential of AI in the manufacturing industry in Germany

PWC study: Effects of the use of AI in Germany

In most cases, from a medium-term to long-term perspective, business models without data components are hardly conceivable. The company structure is more virtual and the importance of platform-based business models in manufacturing (e.g. Bosch IoT Suite or Virtual Fort Knox) and in sales is on the rise. AI will help companies to manage the flood of data that is continuously increasing and use this. In this regard, AI can influence business models on various levels: Thanks to optimised resource and capacity planning, AI can make company processes faster and more efficient and therefore influence time, costs, quality and flexibility. Optimisation of the existing business model takes place on this level. As an additional service, AI can enhance the business model in the form of a data-based, intelligent addition to the product range. An example of this would be that of a mechanical engineer who, in addition to the machines that are sold, also offers a predictive maintenance service or even the machine run time as a service.

Read more:

PWC: White paper on the topic of artificial intelligence for accelerating innovation

In principle, all companies can profit from AI. AI is not comparable with high-performance computers or expensive robots because there are also small-scale and cost-efficient solutions (e.g. from Amazon or Google) and tools to try out (e.g. rapidminer or Knime). Most providers offer various services in the form of AI technologies but connect this with the use of their own cloud solution.

Read more:

See Question 21: What typical providers for AI solutions for medium-sized industrial companies exist?

The first step is for each company to ascertain how the potential of AI can be used for them on an individual basis. In order to do so, companies can make use of the Scoutingservice by Allianz Industrie 4.0 and get inspiration from practical examples. On that basis, the next step is to determine whether there is concrete added value in process optimisation for the company in question and/or whether, in principle, the company’s own business model could be affected. In the event of process optimisation, a pilot or application field should be identified, as should suitable experts with knowledge of the processes and technical data understanding, either internally or externally. In general, you need to identify what data can be collected and is permissible to collect and which benefits this provides. Companies should develop the necessary data science expertise, consisting of a combination of practical knowledge and the fields of mathematics, statistics and IT, and look for (development) partners in their network for additional services, e.g. within the framework of events such as STARTUP THE FUTURE. Furthermore, it is advisable to inform the works council of the planned activities at an early stage as the works council has joint decision-making power in many fields, such as IT systems.

The CRISP model also provides orientation on the operative level for the development of an AI application scenario. This cross-industry process model describes a standardised procedure for implementing data mining projects in six phases.

Read more:

In order to successfully use AI algorithms, the data quality and quantity from the field of machine learning is important. An AI application yields better results if more data is available for its training and operation and if the quality of the data is better and/or more suitable. If the quality of the data is insufficient, this increases the inaccuracy of the results. Other sub-areas of AI, for example logic, can manage with little data.

Often, data that is collected by companies is provided in an unstructured format, for example text, video or images, as it is not collected for the purpose of evaluation. Data must be structured in order to make it useable. Outdated or incorrect master data constitutes a particular problem. The specific data that is required depends on the application and the business case.

Administrating and managing data is a clear prerequisite for the implementation of AI application scenarios. In order to try out initial scenarios and small AI-based applications, however, smaller data volumes are sufficient.

In order to successfully implement an AI application scenario, the existing data is generally divided into three data sets. One is for training in order to provide the AI application with information at the initial stage. The second data set is used to test the AI application and, finally, this is validated using the third data set.

Within the framework of GDPR, the requirements for the protection of personal data became more stringent. For this reason, an expert should be consulted if personal data is used.

The required infrastructure for an AI application is determined by the purpose and the nature of the desired AI application. The more complex the AI application is and the more data that is to be processed, the higher the requirements in terms of hardware too. For simple image recognition applications, often the computing power of existing normal end devices is sufficient. If an application case involves high-resolution processing of camera data in real time, more computing power is required and processing should then run on a server or a Fog computer (cloud structure with decentralised computing power). Depending on the computing power that is required, a computer centre may even be necessary. In addition, cloud solutions can be interesting for SMEs because, depending on the type of cloud, no hardware investment is necessary and integration can be fast. When selecting a suitable cloud provider, the business conditions should be analysed in detail as some are not compliant with the European GDPR and data access regulations. In addition to the storage infrastructure of the cloud, some system providers also offer applications for data preparation and evaluation, which may be useful within the framework of AI applications.

Security concerns of companies are still amongst the greatest obstacles with regard to Industry 4.0 and cloud solutions. In addition, AI technology developments can also create entirely new security risks. The CyberProtect project predominantly supports SMEs in selecting secure software. Also, companies that develop their own software receive support to help them improve the security of their products and services. In addition to sophisticated technical security concepts, companies also need to keep in mind the organisation and employees as some employees may constitute a risk themselves, for example as a result of insufficient knowledge. Furthermore, current events and studies provide information and deliver the latest findings relating to the topic of cybersecurity.

Read more:

Funded project CyberProtect

McAfee: BigData Insider: White paper Big Data Security and Artificial Intelligence

BigData Insider: Whitepaper Big Data Security und Artificial Intelligence

Allianz Industrie 4.0: Events calendar

VDMA: Industrial Security

There is no clear answer to this question. Depending on the purpose of an AI application, the reliability can and may vary. The reliability of an application depends on various factors, for example the (Training) data that is used, the technology that is used etc. The algorithms that are used may also influence the reliability of the result.

Read more:

See Question 10: How much data and what data is required to use an AI application?

In Baden-Württemberg, as well as all over Germany, there are various laboratories and testing environments that conduct research on AI and in which you can experience live AI application cases (e.g. the Centre for Cyber Cognitive Intelligence, Future Work Lab). Furthermore, studies reference concrete application fields (e.g. robotics, quality management) and application scenarios (e.g. predictive maintenance, process control), that show initial potential. In addition, some companies and research organisation have also published the AI applications that they have implemented online and therefore made these accessible, at least in part.

Read more:

Allianz Industrie 4.0: 100 Places for Industry 4.0 in Baden-Wurttemberg

Learning systems platform: AI map with application scenarios

Fraunhofer-Allianz Big Data: Analysis of the future market for artificial intelligence – potential and applications

DFKI: Artificial intelligence – summit paper

Centre for Cyber Cognitive Intelligence CCI

Innovation laboratory Future Work Lab

The published cases in which an AI application has been unsuccessful are generally from the consumer sector. One example of unsuccessful use of a chatbot application is the e-assistant. After a user asked it to “call me an ambulance”, the chatbot referred to him as “an ambulance” from then on. Other well-known examples is that of Google image recognition, which incorrectly recognised people with dark skin as gorillas or the fatal accident caused by a Tesla Autopilot.

Read more:

University of Louisville/Microsoft Corporation: Collection and timeline of unsuccessful application scenarios

Over the course of the industrial revolution, workplaces changed significantly in the field of manufacturing. In recent years, a significant trend towards assisted workplaces has emerged, in which information is displayed in a context-sensitive and digital format. Physical assistance takes place using robots or exoskeletons, in particular in the field of monotonous or highly repetitive activities. In addition, there is an increasing level of individualisation of workplaces that had previously been homogenous. Much like the change from cubicles with dividing walls in large open-plan offices to individualised office rooms that are adapted to the needs of individual employees, a similar trend can be identified in areas that are closely related to manufacturing. Only part of the activities (e.g. simple data analyses, predictable physical activities) can be supported or carried out by AI assistants. AI systems must be understood as assistance systems for humans, not as systems that entirely substitute work being carried out by humans.

Read more:

VDMA: Artificial intelligence position paper

There are various predictions relating to how many and which workplaces will be made redundant or changed by AI. However, no definitive conclusion can be drawn from this as some of the results differ to a large extent. There is a prediction that workplaces staffed by those with few qualifications, and those that involve routine manual and cognitive tasks, will be particularly threatened. At the same time, there are also discussions suggesting that, at the same time, new jobs will be created with cognitive/interactive and creative tasks. However, the skills and competencies required from employees will change drastically. In addition, statements regarding which roles will be affected varies in the projections because, in most cases, individual activities and not the entire role are affected.

In manufacturing work, simple, repetitive tasks or the acquisition and collection of data are considered to be more vulnerable to change or substitution than tasks which require the use of experience or human interaction.

Read more:

See Question 19: Where and how can companies train their employees on the topic of AI?

See Question 20: Can companies develop AI applications in their operations themselves or do they need to buy in AI specialist skills?

PWC: Study on the effects of the use of artificial intelligence in Germany

WEF: Report Future of Jobs

IAB: Discussion paper Promise or threat – The effects of a fourth industrial revolution on the labour market

IAB: Short report Few roles are keeping up with digitalisation

IAB: Job Futuromat

Open communication about upcoming changes at an early-stage is crucial for acceptance. In this regard, it is important to prepare the information for different interest groups within the company in terms of content, language and level of detail. In addition, explicit and implicit concerns need to be addressed openly. Communication must be honest and between equals. Involving employees in the new technologies (e.g. robots, digital assistance) can help to deal with rejection and spark interest. In addition, it is advisable to introduce AI applications to support work processes in a pilot area and to support other work steps (where possible) at a later point in time. If possible, participation in the project can take place at various levels. This contributes to encouraging the employees to identify with the solution. Visits to a laboratory with practical application scenarios helps to make AI more tangible, explain results from AI applications in a better manner and use examples to have an open discussion about the advantages and disadvantages. 

Read more:

Centre for Cyber Cognitive Intelligence CCI

Innovation laboratory Future Work Lab

There are various formats for providing employees with further training. Talks and conferences make it easier to get involved in the field and provide orientation for the executive board and management. For developers, there are courses and further training opportunities (for example at the IHK), which provide a general overview with concise practical knowledge. Furthermore, innovative training providers offer further training courses to acquire methodical and industry-specific specialist knowledge, for example Fraunhofer-Allianz Big Data (data science training offered by Fraunhofer-Allianz Big Data). If a detailed basic training programme is required, special courses provide the necessary specialist knowledge in the fields of mathematics and computer science. Users can get to know the topic of AI on neutral ground in laboratories (e.g. Centre for Cyber Cognitive Intelligence or the Future Work Lab) and obtain some initial practical insights. In addition, there are free webinars online, for example on the topic of machine learning.

Read more:

Mooc.HOUSE: Machine learning

BITKOM Academy: Digital transformation seminar

Fraunhofer-Allianz Big Data: Data science training

Centre for Cyber Cognitive Intelligence CCI

Innovation laboratory Future Work Lab

Allianz Industrie 4.0: Events calendar

In principle, AI applications can be developed independently by companies if they have qualified employees and/or they can provide their employees with further training. In particular, a combination of process and data knowledge is required. In the event external experts are commissioned, it is still essential to develop expertise within your own companies, as the applications must be managed and “maintained” within the company. Employees that work directly with AI applications in the value creation chain do not need to have any detailed knowledge with regard to the application in most cases. If companies are not able to provide the relevant qualifications as a result of their size, then developing a qualified AI application network, e.g. with research organisations, AI application providers or in the context of research projects, is worthwhile. Further, the addition of a data specialist (“data scientist”) to selected project teams can help to realise potential within the company.

For medium-sized industrial companies, both well-known and experienced providers in the field of software and business intelligence can be contacts for applications (e.g. SAP, Microsoft), as can established start-ups (which, for example, provide innovative solutions). Depending on the scope and field of application of the required solution, cooperation with application-oriented research institutions may also be advantageous (e.g. for the method-based introduction of AI technologies). SMEs in particular can profit from start-ups, which can keep up well with the rapid developments in the field of AI thanks to their increased flexibility.

Read more:

Learning systems platform: AI map with application scenarios

Topbots: Map of providers of AI solutions

Fraunhofer-Allianz Big Data: General explanatory notes and information about platform providers (list of providers of various technologies, as well as research institutions that conduct research in this field)

At a state and national level, there are various funding services that specifically focus on providing support and financing for cooperation between companies and science. Furthermore, there are specific funding opportunities that are exclusively aimed at companies, such as the Digitalisierungsprämie Plus or the Innovationsgutschein Hightech Digital. Those who are interested can find informational and specialist events in the Allianz Industrie 4.0 events calendar, at which companies can make contact with the most important players in this field. Furthermore, the Federal Agency for Civic Education provides interesting expert contributions on its website. The IHK and some private providers are other points of contact for further training and support services. Companies can obtain support for their specific questions at independent advisory centres.

Read more:

Allianz Industrie 4.0 Baden-Württemberg events calendar

DE-HUB website

IHK Stuttgart website

Learning systems platform website

AI association (KI-Verband) website

Website of the Federal Agency for Civic Education on the topic of AI

There’s no change without employees! With regard to AI, it is important to help employees deal with their concerns and get them involved. For companies, this means: providing information, enabling dialogue and encouraging people to talk about and deal with their concerns. Further training opportunities at an early stage make a positive contribution to creating an optimistic environment for employees and getting them involved in the change. In order to do so, the employee must have an understanding of the future requirements for employees (e.g. skills, qualifications).

Managers should deal with employees whose workplace will change in a focused manner. In doing so, satisfactory answers to any questions should be found and alternatives offered.

In the context of AI, the media often presents an exaggerated image with lots of negative impressions and suggestions of omnipotence, which creates a pessimistic view. Ethical use of AI is often deliberately played down. Pioneer companies have already created their own ethical guidelines for researching and developing AI in this regard. The final version of the EU Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is expected to be published in April 2019. The Bundestag has also set up a Commission of Enquiry [Enquete-Kommission] in order to assess social responsibility, as well as the economic, social and environmental potential of AI.

Read more:

European Union: Preliminary version of the Ethics Guidelines

Germany

AI map

The AI map of the Plattform Lernende Systeme clearly depicts users and providers of artificial intelligence throughout Germany. It is constantly being expanded. Companies and institutions can register their AI solution.

Artificial Intelligence

Click here for innovative applications and projects

We can only make efficient use of the mass of data available to us if we apply intelligent processes to it. Only then will we be able to understand what a treasure this data holds.

Lukas Schleicher
Project manager for artificial intelligence and cybersecurity

Learning and qualification 4.0

Introduction
and questions

Best Practices

Learning Factories 4.0

Competition
Industry 4.0-Talents

Making employees fit for Industry 4.0

A changed working environment

Digitalization has far-reaching effects on industry: changes to processes, networking within the value chain, new technologies etc. This inevitably affects work; activities and entire workplaces are changing. Employers must make their employees fit for the new conditions.

That is why trainees and students are being prepared for a digitalized working world with new learning formats. Tailored qualification concepts must be developed for employees of all ages. After all, Industry 4.0 only works with people.

Many companies in Baden-Württemberg have long since recognized this and are specifically promoting the development of Industrie 4.0 skills.

Preparing for the digital future

Questions on the subject

What skills will employees need in the future?

According to the VDMA study „Qualification 2025„, there are rarely clear statements about required competencies for the skilled workers.

For this reason, the Allianz Industrie 4.0 initiated a project group to address this issue.

Which learning formats are suitable for preparing employees for the digital transformation?

Supporting work tools and communicable forms of work increase teaching and learning productivity.

Training and continuing education programs with an increasingly high proportion of IT skills are emerging.

New learning environments (e.g., learning factories) convey Industry 4.0 content and processes in a very practical way.

There are already many best practices that have taken up the new needs and developed solutions.

Translated with www.DeepL.com/Translator (free version)

What conditions must be in place for successful qualification?

A strong learning culture must be the framework for in-house training. This is the only way to ensure that all employees are involved in the rapid process of change.

Baden-Württemberg

Best Practices Learning and Qualification 4.0

Many companies and institutions have already developed and implemented different concepts to prepare tomorrow’s employees for new activities and work environments.
Discover more best practices at the „100 Places for Industry 4.0“ competition in the „Learning and HRM 4.0“ category.

Roadshow-Truck Discover Industry

Baden-Württemberg Stiftung gGmbH

DISCOVER INDUSTRY invites you into the world of Industry 4.0. At various workstations, students learn about the product creation process in industry through practical tasks. They also learn how engineers think and work in practice and what computer scientists have to do with the digitalized production of today and tomorrow.

Learn more

EduKit goes IoT Kit

Adiro Automatisierungstechnik GmbH

The „EduKit PA goes IoT“ serves as a learning system for companies and institutions. The demonstrator in compact table format consists of an intelligent bottling plant that is networked with suppliers and customers via internet. This shows how industrial business processes are changing through information technology networking.

Learn more

Industry 4.0 for everyone - The Learning Factory 4.0

Hohentwiel-Gewerbeschule Singen

The Learning Factory Singen regularly offers courses for industrial companies. Employees can thus expand their competencies in a time-based concept tailored to their needs. Trainers also use these courses for further training. There are courses in the areas of CADCAM (Solidworks/Heidenhain), PLC (TIA Portal) and robotics.

Learn more

Industry 4.0 at vocational schools in Baden-Württemberg

Learning Factories 4.0 BW

To prepare skilled workers and junior staff for the requirements of Industry 4.0, the Ministry of Economics, Labor and Housing is promoting the establishment of Training Factories 4.0 at vocational schools. In the learning factories, students learn to operate equipment based on real industry standards.

The Learning Factory 4.0 is a laboratory that resembles industrial automation solutions in terms of its structure and equipment and in which basic principles for application-oriented processes can be learned. Mechanical and electrical engineering are linked by professional production control systems.

Target groups are

  • trainees in dual training courses in the fields of metal and electrical engineering
  • participants in technical schools and further education/training courses offered by medium-sized companies

as part of offers from the sponsoring associations of vocational schools and cooperative ventures with business organizations, applied Allianz Industrie 4.0 Baden-Württemberg.

Werner-von-Siemens-Schule Mannheim
Mannheim, Stadt Mannheim
https://www.wvss-mannheim.de


Friedrich-List-Schule Mannheim
Mannheim, Stadt Mannheim
https://www.fls-mannheim.de

Technische Schule Heidenheim
Heidenheim, Landkreis Heidenheim
www.heid-tech.de


Kaufmännische Schule Heidenheim
Heidenheim, Landkreis Heidenheim
www.ks-heidenheim.de

Technische Schule Heidenheim
Heidenheim, Landkreis Heidenheim
www.heid-tech.de


Kaufmännische Schule Heidenheim
Heidenheim, Landkreis Heidenheim
https://www.ks-heidenheim.de

Technische Schule Aalen
Ostalbkreis, Aalen
www.ts-aalen.de


Gewerbliche Schule Schwäbisch Gmünd
Ostalbkreis, Schwäbisch Gmünd
www.gs-gd.de

Gewerbliche Schule Ehingen
Ehingen, Alb-Donau-Kreis
https://www.gbs-ehingen.de

Robert-Bosch-Schule Ulm
Ulm, Stadt Ulm
https://www.rbs-ulm.de


Friedrich-List-Schule Ulm
Ulm, Stadt Ulm
https://www.fls-ulm.de

Karl-Arnold-Schule Biberach
Landkreis Biberach, Biberach
www.kas-bc.de

Geschwister-Scholl-Schule Leutkirch
Leutkirch, Landkreis Ravensburg
www.gss-leutkirch.de

Elektronikschule Tettnang
Bodenseekreis, Tettnang
www.elektronikschule.de

Hohentwiel Gewerbeschule Singen, in Kooperation mit Zeppelin-Gewerbeschule Konstanz
Landkreis Konstanz, Singen
www.hgs-singen.de

Gewerbeschule Villingen-Schwenningen
Schwarzwald-Baar-Kreis, Villingen-Schwenningen
www.gewerbeschule-vs.de

Ferdinand-von-Steinbeis-Schule Tuttlingen
Tuttlingen, Landkreis Tuttlingen
https://steinbeisschule.de

Philipp-Matthäus-Hahn-Schule Balingen
Zollernalbkreis, Balingen
www.gsz-zak.de

Gewerbliche Schule Tübingen
Tübingen und Rottenburg, Landkreis Tübingen
https://www.gs-tuebingen.de


Berufliche Schule Rottenburg
Tübingen und Rottenburg, Landkreis Tübingen
https://www.bsrottenburg.de


Wilhelm-Schickard-Schule Tübingen
Tübingen und Rottenburg, Landkreis Tübingen
www.wss.tue.bw.schule.de

Ferdinand-von-Steinbeisschule Reutlingen
Landkreis Reutlingen, Reutlingen
www.steinbeisschule-reutlingen.de

Friedrich-Ebert-Schule Esslingen
„Multilabor 4.0“ für das Handwerk
Landkreis Esslingen
www.fes-es.de

Gewerbliche und Hauswirtschaftliche Schule Horb am Neckar
Horb am Neckar, Landkreis Freudenstadt
https://www.bs-horb.de

Berufliche Schulen Bretten
Bretten, Landkreis Karlsruhe
www.bsb-bretten.de


Albert-Einstein-Schule Ettlingen
Ettlingen, Landkreis Karlsruhe
www.aesettlingen.de

Gottlieb-Daimler-Schule 1 Sindelfingen
Sindelfingen, Landkreis Böblingen
https://www.gds1.de


Gottlieb-Daimler Schule 2 Sindelfingen
Sindelfingen, Landkreis Böblingen
https://www.gds2.de

Max-Eyth-Schule Kirchheim unter Teck
Kirchheim unter Teck, Landkreis Esslingen
www.mesk.de


Friedrich-Ebert-Schule Esslingen am Neckar
Esslingen am Neckar, Landkreis Esslingen
https://www.fes-es.de


Phillip-Matthäus-Hahn-Schule Nürtingen
Nürtingen, Landkreis Esslingen
www.pmhs-nuertingen.de

Werner-von-Siemens-Schule Mannheim
Mannheim, Stadt Mannheim
https://www.wvss-mannheim.de


Friedrich-List-Schule Mannheim
Mannheim, Stadt Mannheim
https://www.fls-mannheim.de

Gewerbliche Schule Geislingen
Geislingen, Landkreis Göppingen
https://www.gewerblicheschule.de


Kaufmännische Schule Geislingen
Geislingen, Landkreis Göppingen
https://www.ksgeislingen.de

Gewerbliche Schule Göppingen
Landkreis Göppingen, Göppingen
www.gs.gp.bw.schule.de

Grafenbergschule Schorndorf
Schorndorf, Landkreis Rems-Murr
https://www.gsso.de

May-Eyth-Schule Stuttgart
Stadt Stuttgart, Stuttgart
www.mes-stuttgart.de


Werner-Siemens-Schule Stuttgart (Federführung)
Stadt Stuttgart, Stuttgart
www.wss-stuttgart.de


Robert-Bosch-Schule Stuttgart
Stadt Stuttgart, Stuttgart
www.rbs-stuttgart.de

Hubert-Sternberg-Schule (Federführung)
Rhein-Neckar-Kreis, Wiesloch
www.hss-wiesloch.de

Ehrhart-Schott-Schule
Rhein-Neckar-Kreis, Schwetzingen
www.esss.de


Friedrich-Hecker-Schule
Rhein-Neckar-Kreis, Sinsheim
www.friedrich-hecker-schule.de


Theodor-Frey-Schule
Rhein-Neckar-Kreis, Eberbach
www.tfse.de


Hans-Freudenberg-Schule
Rhein-Neckar-Kreis, Weinheim
www.hfswe.de

Gewerbeschule Mosbach
Neckar-Odenwald-Kreis, Mosbach
www.gewerbeschule-mosbach.de


Zentralgewerbeschule Buchen
Neckar-Odenwald-Kreis, Buchen
www.zgb-buchen.de

Christian-Schmidt-Schule Neckarsulm
Neckarsulm, Landkreis Heilbronn
https://www.css-nsu.de


Andreas-Schneider-Schule Heilbronn
Heilbronn, Landkreis Heilbronn
https://www.ass-hn.com

Wilhelm-Maybach-Schule Heilbronn
Heilbronn, Stadt Heilbronn
www.wms-hn.de


Gustav-von-Schmoller-Schule Heilbronn
Heilbronn, Stadt Heilbronn
www.gvss-hn.net

Gewerbliche Schule Öhringen
Öhringen, Hohenlohekreis
www.gsoe.de


Gewerbliche Schule Künzelsau
www.gwkuen.de
Künzelsau, Hohenlohekreis

Gewerbliche Schulen
Landkreis Schwäbisch Hall, Schwäbisch Hall
gbs-sha.de


Kaufmännische Schulen
Landkreis Schwäbisch Hall, Schwäbisch Hall
www.kbs-sha.de


Gewerbliche Schulen
Landkreis Schwäbisch Hall, Crailsheim
www.gscr.de


Kaufmännische Schulen
Landkreis Schwäbisch Hall, Crailsheim
www.kscr.de

Gewerbliche Schule Bad Mergentheim
Bad Mergentheim, Main-Tauber-Kreis
www.gsmgh.de


Gewerbliche Schule Tauberbischofsheim
Tauberbischofsheim, Main-Tauber-Kreis
www.gstbb.de


Berufliches Schulzentrum Wertheim
Wertheim, Main-Tauber-Kreis
www.gstbb.de


Kaufmännische Schule Bad Mergentheim
Bad Mergentheim, Main-Tauber-Kreis
https://www.ks-mergentheim.de

Berufliches Schulzentrum Bietigheim-Bissingen in Kooperation mit Carl-Schaefer-Schule Ludwigsburg
Landkreis Ludwigsburg, Bietigheim-Bissingen
www.bsz-bietigheim.de


Berufliches Schulzentrum Bietigheim-Bissingen
Film über die Lernfabrik 4.0

Heinrich-Hertz-Schule
Stadt Karlsruhe, Karlsruhe
www.hhs.karlsruhe.de


Carl-Benz-Schule
Stadt Karlsruhe, Karlsruhe
www.cbs.ka.bw.schule.de

Carl-Benz-Schule Gaggenau
Landkreis Rastatt, Gaggenau
www.carl-benz-schule-gaggenau.de

Lernfabrik 4.0 Gaggenau

Josef-Durler-Schule Rastatt
Rastatt, Landkreis Rastatt
https://jdsr.de


Handelslehranstalt Rastatt
Rastatt, Landkreis Rastatt
https://www.hla-rastatt.de


Carl-Benz-Schule Gaggenau
Gaggenau, Landkreis Rastatt
https://www.carl-benz-schule-gaggenau.de

Gewerblich-Technische Schule
Ortenaukreis, Offenburg
www.gs-offenburg.de

Richard-Fehrenbach-Gewerbeschule Freiburg
Freiburg, Stadt Freiburg im Breisgau
www.rfgs.de

Gewerbliche Schulen Donaueschingen
Donaueschingen, Schwarzwald-Baar-Kreis
www.gsdonau.de


Kaufmännische und Hauswirtschaftliche Schulen Donaueschingen
Donaueschingen, Schwarzwald-Baar-Kreis
www.khs-donaueschingen.de


Berufsschulzentrum Bietigheim-Bissingen
Bietigheim-Bissingen, Schwarzwald-Baar-Kreis
www.bsz-bietigheim.de

 

Hans-Thoma-Schule Titisee-Neustadt
Titisee-Neustadt, Landkreis Breisgau-Hochschwarzwald
www.hans-thoma-schule.de

Industry 4.0-Talents Competition

Industry 4.0-Talents

The digitalized processes in industry require corresponding skills and abilities of the employees. That is why Industry 4.0 skills must already be taught during training.

The „Industry 4.0 Talents“ competition is now making this visible:
We are looking for companies that have integrated Industry 4.0 projects into their training.

What are we looking for?


(1) innovative learning formats and training programs related to Industrie 4.0 in whose implementation or, where applicable, development trainees or students were actively involved
(2) innovative implementation projects related to Industrie 4.0 in which trainees or students were actively involved

Industry 4.0 requires new kinds of competencies in companies and adapted qualifications.

Jérôme Jetter
Project manager learning and qualification 4.0

FAQ

Häufige Fragen

AI is a sub-area of computer science. As there is no clear definition for the term “intelligence” itself, there are various definitions for the term AI. In addition, understanding of AI has been developing on an ongoing basis. In general, AI engages with tasks or problems which human intelligence is normally required to solve. These processes aim to create self-learning intelligence in the form of a system that can work independently to develop solutions for a problem. In this regard, AI includes a range of methods from mathematics, statistics and computer science, which reconstruct particular aspects of human intelligence, for example cognitive or sensorimotor intelligence. This reconstructed human intelligence is only limited to a very specific area of application.

It is important to note that AI does not only relate to simple automation in which an application is programmed to make a particular decision in the event of a particular input.

Read more:

See Question 3: What other terms relating to AI do I need to know and where can I find a clear explanation of the relevant terms?

AI in manufacturing is a key technology, making it part of digital transformation and therefore a significant element of Industry 4.0. Within the framework of discussions relating to Industry 4.0 and the respective application scenarios, AI plays a key role, for example in the analysis of large data volumes and in automated decision-making. Thanks to comprehensive networking and the collection of large volumes of data, which are developments associated with Industry 4.0, AI solutions are improving in terms of performance and may be used in the manufacturing environment.

An example of this is predictive maintenance: Manufacturing systems and machines will be maintained in a predictive manner on the basis of intelligent data analyses instead of at fixed intervals. An application based on AI provides additional opportunities here in the field of data analysis and pattern recognition, which enables maintenance times to be optimised.

Read more:

PWC-study: Predictive Maintenance 4.0

Huber: Case studies on Predictive Maintenance (in the Data Science book)

Data Mining refers to the use of algorithms for the analysis and processing of large, complex and heterogenous data sets (“big data”) to generate new information. In this regard, individual data components are brought together, in particular, in order to obtain new findings.

Machine learning is a sub-area of AI. The focus is on the development of algorithms that imitate the learning behaviour of the human brain for machines. The algorithms learn from experiences and can find solutions for new problems or evaluate unknown data sets.

Deep learning is a sub-area of machine learning that is characterised by complex solution methods. It uses artificial neural networks, in which multiple data processing levels can be combined.

Models that are based on artificial neural networks  constitute the networking of nerve cells from a brain in an abstract form for information processing.

Weak artificial intelligence solves problems using methods that have been developed and optimised for the specific purpose (e.g. spam filters).

Strong artificial intelligence, also known as superintelligence , is superior to humans in terms of intellectual abilities. On the basis of the current knowledge, strong artificial intelligence does not yet exist.

For some of the terms that are listed, a concrete definition has not been established.

Read more:

Federal government: Artificial intelligence strategy of the federal government

Start-up scene: Artificial intelligence glossary

Elektronikpraxis Vogel: Artificial intelligence specialist terms

VDMA: Machine learning in machine and systems engineering

Fraunhofer Society: Artificial intelligence trends

Fraunhofer-Allianz Big Data: 2017 potential assessment for AI

AI is no longer just something for the future. AI is already making it possible to achieve significant progress in various areas of applications, for example in industry, the transport sector, healthcare and other areas of life. For AI applications it is important to identify solutions from the perspective of need and to evaluate AI as a tool for optimisation or an addition to business plans. In some cases, the optimisation may also be achieved using traditional processes or statistical methods. If a clear benefit can be identified for the company, applications may create a competitive edge now, e.g. thanks to an extended product portfolio or cost reductions as a result of a reduced level of material stock. In addition, the framework conditions have developed in favour of AI applications in recent years: Cloud solutions allow data to be made available almost anywhere, the computing capacity of hardware continues to increase and the costs for data storage are significantly lower now than in the past.

Read more:

VDMA: Artificial intelligence position paper

In our personal lives, AI-based apps and assistants have been well-known for a while now (e.g. digital voice assistants on smartphones/smart home). Currently, typical areas of application for AI in the industrial environment are primarily in places where lots of data is (or could be) collected and analysed. The evaluation of large data volumes is one of the areas in which AI provides new opportunities in comparison with traditional business analytics solutions. In the field of quality assurance, optical inspections using AI applications are popular, which enable error or process analyses using image recognition. An energy management system that is equipped with AI is capable of analysing and predicting energy intake and distribution in the factory on a continuous basis in order to then adapt the manufacturing process and optimise the energy costs. Thanks to predictive data analysis, machines and systems can be maintained in a manner that is more well-suited to requirements and therefore reduce downtime. Outside of the company, the manufacturers of the machines and systems can provide additional services that are fed with the customers’ machine data on-site.

AI provides companies with support, in particular in areas in which humans and systems had previously reached their limits. In terms of analysis, AI applications generally use very large data volumes, which makes the identification of anomalies and the prediction of faults more accurate. With error reporting, AI makes it possible to identify causes more quickly and ascertain correlations that may have previously not been identified. AI can also help to plan manufacturing processes and capacities better, which has a positive effect on costs. Self-learning algorithms can make existing technologies even better. In this way, driverless transport systems can find the best path through the manufacturing facility at any time.

A BMWi projection indicates that an increase in the gross value added of EUR 31.8 billion for manufacturing businesses in Germany can be expected from 2019 to 2023 thanks to AI, which constitutes an enormous benefit for companies. A PWC study predicts a rise in the overall gross value added of EUR 430 billion.

Read more:

VDMA: Artificial intelligence position paper

BMWi study: The potential of AI in the manufacturing industry in Germany

PWC study: Effects of the use of AI in Germany

In most cases, from a medium-term to long-term perspective, business models without data components are hardly conceivable. The company structure is more virtual and the importance of platform-based business models in manufacturing (e.g. Bosch IoT Suite or Virtual Fort Knox) and in sales is on the rise. AI will help companies to manage the flood of data that is continuously increasing and use this. In this regard, AI can influence business models on various levels: Thanks to optimised resource and capacity planning, AI can make company processes faster and more efficient and therefore influence time, costs, quality and flexibility. Optimisation of the existing business model takes place on this level. As an additional service, AI can enhance the business model in the form of a data-based, intelligent addition to the product range. An example of this would be that of a mechanical engineer who, in addition to the machines that are sold, also offers a predictive maintenance service or even the machine run time as a service.

Read more:

PWC: White paper on the topic of artificial intelligence for accelerating innovation

In principle, all companies can profit from AI. AI is not comparable with high-performance computers or expensive robots because there are also small-scale and cost-efficient solutions (e.g. from Amazon or Google) and tools to try out (e.g. rapidminer or Knime). Most providers offer various services in the form of AI technologies but connect this with the use of their own cloud solution.

Read more:

See Question 21: What typical providers for AI solutions for medium-sized industrial companies exist?

The first step is for each company to ascertain how the potential of AI can be used for them on an individual basis. In order to do so, companies can make use of the Scoutingservice by Allianz Industrie 4.0 and get inspiration from practical examples. On that basis, the next step is to determine whether there is concrete added value in process optimisation for the company in question and/or whether, in principle, the company’s own business model could be affected. In the event of process optimisation, a pilot or application field should be identified, as should suitable experts with knowledge of the processes and technical data understanding, either internally or externally. In general, you need to identify what data can be collected and is permissible to collect and which benefits this provides. Companies should develop the necessary data science expertise, consisting of a combination of practical knowledge and the fields of mathematics, statistics and IT, and look for (development) partners in their network for additional services, e.g. within the framework of events such as STARTUP THE FUTURE. Furthermore, it is advisable to inform the works council of the planned activities at an early stage as the works council has joint decision-making power in many fields, such as IT systems.

The CRISP model also provides orientation on the operative level for the development of an AI application scenario. This cross-industry process model describes a standardised procedure for implementing data mining projects in six phases.

Read more:

In order to successfully use AI algorithms, the data quality and quantity from the field of machine learning is important. An AI application yields better results if more data is available for its training and operation and if the quality of the data is better and/or more suitable. If the quality of the data is insufficient, this increases the inaccuracy of the results. Other sub-areas of AI, for example logic, can manage with little data.

Often, data that is collected by companies is provided in an unstructured format, for example text, video or images, as it is not collected for the purpose of evaluation. Data must be structured in order to make it useable. Outdated or incorrect master data constitutes a particular problem. The specific data that is required depends on the application and the business case.

Administrating and managing data is a clear prerequisite for the implementation of AI application scenarios. In order to try out initial scenarios and small AI-based applications, however, smaller data volumes are sufficient.

In order to successfully implement an AI application scenario, the existing data is generally divided into three data sets. One is for training in order to provide the AI application with information at the initial stage. The second data set is used to test the AI application and, finally, this is validated using the third data set.

Within the framework of GDPR, the requirements for the protection of personal data became more stringent. For this reason, an expert should be consulted if personal data is used.

The required infrastructure for an AI application is determined by the purpose and the nature of the desired AI application. The more complex the AI application is and the more data that is to be processed, the higher the requirements in terms of hardware too. For simple image recognition applications, often the computing power of existing normal end devices is sufficient. If an application case involves high-resolution processing of camera data in real time, more computing power is required and processing should then run on a server or a Fog computer (cloud structure with decentralised computing power). Depending on the computing power that is required, a computer centre may even be necessary. In addition, cloud solutions can be interesting for SMEs because, depending on the type of cloud, no hardware investment is necessary and integration can be fast. When selecting a suitable cloud provider, the business conditions should be analysed in detail as some are not compliant with the European GDPR and data access regulations. In addition to the storage infrastructure of the cloud, some system providers also offer applications for data preparation and evaluation, which may be useful within the framework of AI applications.

Security concerns of companies are still amongst the greatest obstacles with regard to Industry 4.0 and cloud solutions. In addition, AI technology developments can also create entirely new security risks. The CyberProtect project predominantly supports SMEs in selecting secure software. Also, companies that develop their own software receive support to help them improve the security of their products and services. In addition to sophisticated technical security concepts, companies also need to keep in mind the organisation and employees as some employees may constitute a risk themselves, for example as a result of insufficient knowledge. Furthermore, current events and studies provide information and deliver the latest findings relating to the topic of cybersecurity.

Read more:

Funded project CyberProtect

McAfee: BigData Insider: White paper Big Data Security and Artificial Intelligence

BigData Insider: Whitepaper Big Data Security und Artificial Intelligence

Allianz Industrie 4.0: Events calendar

VDMA: Industrial Security

There is no clear answer to this question. Depending on the purpose of an AI application, the reliability can and may vary. The reliability of an application depends on various factors, for example the (Training) data that is used, the technology that is used etc. The algorithms that are used may also influence the reliability of the result.

Read more:

See Question 10: How much data and what data is required to use an AI application?

In Baden-Württemberg, as well as all over Germany, there are various laboratories and testing environments that conduct research on AI and in which you can experience live AI application cases (e.g. the Centre for Cyber Cognitive Intelligence, Future Work Lab). Furthermore, studies reference concrete application fields (e.g. robotics, quality management) and application scenarios (e.g. predictive maintenance, process control), that show initial potential. In addition, some companies and research organisation have also published the AI applications that they have implemented online and therefore made these accessible, at least in part.

Read more:

Allianz Industrie 4.0: 100 Places for Industry 4.0 in Baden-Wurttemberg

Learning systems platform: AI map with application scenarios

Fraunhofer-Allianz Big Data: Analysis of the future market for artificial intelligence – potential and applications

DFKI: Artificial intelligence – summit paper

Centre for Cyber Cognitive Intelligence CCI

Innovation laboratory Future Work Lab

The published cases in which an AI application has been unsuccessful are generally from the consumer sector. One example of unsuccessful use of a chatbot application is the e-assistant. After a user asked it to “call me an ambulance”, the chatbot referred to him as “an ambulance” from then on. Other well-known examples is that of Google image recognition, which incorrectly recognised people with dark skin as gorillas or the fatal accident caused by a Tesla Autopilot.

Read more:

University of Louisville/Microsoft Corporation: Collection and timeline of unsuccessful application scenarios

Over the course of the industrial revolution, workplaces changed significantly in the field of manufacturing. In recent years, a significant trend towards assisted workplaces has emerged, in which information is displayed in a context-sensitive and digital format. Physical assistance takes place using robots or exoskeletons, in particular in the field of monotonous or highly repetitive activities. In addition, there is an increasing level of individualisation of workplaces that had previously been homogenous. Much like the change from cubicles with dividing walls in large open-plan offices to individualised office rooms that are adapted to the needs of individual employees, a similar trend can be identified in areas that are closely related to manufacturing. Only part of the activities (e.g. simple data analyses, predictable physical activities) can be supported or carried out by AI assistants. AI systems must be understood as assistance systems for humans, not as systems that entirely substitute work being carried out by humans.

Read more:

VDMA: Artificial intelligence position paper

There are various predictions relating to how many and which workplaces will be made redundant or changed by AI. However, no definitive conclusion can be drawn from this as some of the results differ to a large extent. There is a prediction that workplaces staffed by those with few qualifications, and those that involve routine manual and cognitive tasks, will be particularly threatened. At the same time, there are also discussions suggesting that, at the same time, new jobs will be created with cognitive/interactive and creative tasks. However, the skills and competencies required from employees will change drastically. In addition, statements regarding which roles will be affected varies in the projections because, in most cases, individual activities and not the entire role are affected.

In manufacturing work, simple, repetitive tasks or the acquisition and collection of data are considered to be more vulnerable to change or substitution than tasks which require the use of experience or human interaction.

Read more:

See Question 19: Where and how can companies train their employees on the topic of AI?

See Question 20: Can companies develop AI applications in their operations themselves or do they need to buy in AI specialist skills?

PWC: Study on the effects of the use of artificial intelligence in Germany

WEF: Report Future of Jobs

IAB: Discussion paper Promise or threat – The effects of a fourth industrial revolution on the labour market

IAB: Short report Few roles are keeping up with digitalisation

IAB: Job Futuromat

Open communication about upcoming changes at an early-stage is crucial for acceptance. In this regard, it is important to prepare the information for different interest groups within the company in terms of content, language and level of detail. In addition, explicit and implicit concerns need to be addressed openly. Communication must be honest and between equals. Involving employees in the new technologies (e.g. robots, digital assistance) can help to deal with rejection and spark interest. In addition, it is advisable to introduce AI applications to support work processes in a pilot area and to support other work steps (where possible) at a later point in time. If possible, participation in the project can take place at various levels. This contributes to encouraging the employees to identify with the solution. Visits to a laboratory with practical application scenarios helps to make AI more tangible, explain results from AI applications in a better manner and use examples to have an open discussion about the advantages and disadvantages. 

Read more:

Centre for Cyber Cognitive Intelligence CCI

Innovation laboratory Future Work Lab

There are various formats for providing employees with further training. Talks and conferences make it easier to get involved in the field and provide orientation for the executive board and management. For developers, there are courses and further training opportunities (for example at the IHK), which provide a general overview with concise practical knowledge. Furthermore, innovative training providers offer further training courses to acquire methodical and industry-specific specialist knowledge, for example Fraunhofer-Allianz Big Data (data science training offered by Fraunhofer-Allianz Big Data). If a detailed basic training programme is required, special courses provide the necessary specialist knowledge in the fields of mathematics and computer science. Users can get to know the topic of AI on neutral ground in laboratories (e.g. Centre for Cyber Cognitive Intelligence or the Future Work Lab) and obtain some initial practical insights. In addition, there are free webinars online, for example on the topic of machine learning.

Read more:

Mooc.HOUSE: Machine learning

BITKOM Academy: Digital transformation seminar

Fraunhofer-Allianz Big Data: Data science training

Centre for Cyber Cognitive Intelligence CCI

Innovation laboratory Future Work Lab

Allianz Industrie 4.0: Events calendar

In principle, AI applications can be developed independently by companies if they have qualified employees and/or they can provide their employees with further training. In particular, a combination of process and data knowledge is required. In the event external experts are commissioned, it is still essential to develop expertise within your own companies, as the applications must be managed and “maintained” within the company. Employees that work directly with AI applications in the value creation chain do not need to have any detailed knowledge with regard to the application in most cases. If companies are not able to provide the relevant qualifications as a result of their size, then developing a qualified AI application network, e.g. with research organisations, AI application providers or in the context of research projects, is worthwhile. Further, the addition of a data specialist (“data scientist”) to selected project teams can help to realise potential within the company.

For medium-sized industrial companies, both well-known and experienced providers in the field of software and business intelligence can be contacts for applications (e.g. SAP, Microsoft), as can established start-ups (which, for example, provide innovative solutions). Depending on the scope and field of application of the required solution, cooperation with application-oriented research institutions may also be advantageous (e.g. for the method-based introduction of AI technologies). SMEs in particular can profit from start-ups, which can keep up well with the rapid developments in the field of AI thanks to their increased flexibility.

Read more:

Learning systems platform: AI map with application scenarios

Topbots: Map of providers of AI solutions

Fraunhofer-Allianz Big Data: General explanatory notes and information about platform providers (list of providers of various technologies, as well as research institutions that conduct research in this field)

At a state and national level, there are various funding services that specifically focus on providing support and financing for cooperation between companies and science. Furthermore, there are specific funding opportunities that are exclusively aimed at companies, such as the Digitalisierungsprämie Plus or the Innovationsgutschein Hightech Digital. Those who are interested can find informational and specialist events in the Allianz Industrie 4.0 events calendar, at which companies can make contact with the most important players in this field. Furthermore, the Federal Agency for Civic Education provides interesting expert contributions on its website. The IHK and some private providers are other points of contact for further training and support services. Companies can obtain support for their specific questions at independent advisory centres.

Read more:

Allianz Industrie 4.0 Baden-Württemberg events calendar

DE-HUB website

IHK Stuttgart website

Learning systems platform website

AI association (KI-Verband) website

Website of the Federal Agency for Civic Education on the topic of AI

There’s no change without employees! With regard to AI, it is important to help employees deal with their concerns and get them involved. For companies, this means: providing information, enabling dialogue and encouraging people to talk about and deal with their concerns. Further training opportunities at an early stage make a positive contribution to creating an optimistic environment for employees and getting them involved in the change. In order to do so, the employee must have an understanding of the future requirements for employees (e.g. skills, qualifications).

Managers should deal with employees whose workplace will change in a focused manner. In doing so, satisfactory answers to any questions should be found and alternatives offered.

In the context of AI, the media often presents an exaggerated image with lots of negative impressions and suggestions of omnipotence, which creates a pessimistic view. Ethical use of AI is often deliberately played down. Pioneer companies have already created their own ethical guidelines for researching and developing AI in this regard. The final version of the EU Ethics Guidelines for Trustworthy Artificial Intelligence (AI) is expected to be published in April 2019. The Bundestag has also set up a Commission of Enquiry [Enquete-Kommission] in order to assess social responsibility, as well as the economic, social and environmental potential of AI.

Read more:

European Union: Preliminary version of the Ethics Guidelines

FAQ

Häufige Fragen
Unter dem Begriff »Industrie 4.0« werden neue Technologien zusammengefasst, die es Menschen, komplexeren Maschinen und einfacheren Geräten ermöglichen, sich über das Internet zu vernetzen. Diese Vernetzung findet in Echtzeit und über Unternehmensgrenzen hinweg statt. Durch den Digitalen Wandel müssen auch die Kompetenzen und Lehrmethoden für Nachwuchskräfte angepasst werden. Diese weiterentwickelte Form der Ausbildung wird Ausbildung 4.0 genannt. Weitere Details zum Thema Industrie 4.0 finden Sie in unserem FAQ.
Etablierte Ausbildungsberufe bleiben im Kern erhalten und werden durch neu eingeführte informationstechnische Inhalte und Methoden ergänzt. Dies geschieht zum einen, weil sich Tätigkeiten der jeweiligen Berufe mit der Zeit verändern, zum anderen ergeben sich durch die zunehmende Einführung und Vernetzung von IT-Systemen neue Interaktionsschnittstellen, wie z. B.durch den Einsatz von Tablets am Arbeitsplatz. Auch die Weitergabe von Informationen von Mensch zu Mensch und Mensch-Maschine-Kommunikation bleiben vom Wandel nicht verschont. Welche Themen der Digitalisierung bereits in der Ausbildung intensiviert vermittelt werden, hängt stark von der Relevanz für den jeweiligen Beruf ab. Neben der Anpassung bestehender Ausbildungsberufe entstehen vereinzelt neue Berufsbilder. Ein Beispiel hierfür ist der Ausbildungsberuf „Produktionstechnologe/-technologin“. Dieser erfordert bereichsübergreifendes Denken und legt einen wesentlich stärkeren Fokus auf Schnittstellen und Analysetätigkeiten, für die bereits heute digitale Werkzeuge erforderlich sind. Aufgrund des immer schnelleren technischen Fortschritts ist davon auszugehen, dass sich auch die Entstehung neuer Berufsbilder beschleunigen wird.
Die Vorteile der Digitalen Transformation können am besten ausgeschöpft werden, wenn sich die Mitarbeitenden mitentwickeln. Am einfachsten ist dies in der Ausbildung möglich. FacharbeiterInnen 4.0 sind Personen, die bereits Industrie 4.0-Kompetenzen in der Ausbildung erlernt oder ihr Kompetenzprofil im späteren Berufsleben durch entsprechende Weiterbildungen erweitert haben. Sie können sich einfacher im modernen Unternehmen integrieren und flexibler eingesetzt werden. Die durch die Ausbildung geformte Offenheit gegenüber neuen Systemen und Anlagen erleichtert es, die Potenziale besser zu nutzen. Gleichzeitig stützt die Auseinandersetzung mit Innovationsthemen die Weiterentwicklung von Produktion und Prozessen im Betrieb. FacharbeiterInnen 4.0 werden dadurch zunehmend zu „Dirigenten des Maschinenparks“.

Insbesondere der Bereich der sozialen Kompetenzen nimmt einen zunehmenden Stellenwert für den Unternehmenserfolg ein. Während bisher aus methodischer Sicht der Aspekt der Routine wichtig war, müssen FacharbeiterInnen 4.0 in Zukunft verstärkt teamfähig sein, um sich untereinander in komplexeren Situationen zu koordinieren und effizient zu arbeiten (siehe „Kompetenz- und Qualifizierungsbedarfe bis 2030“, Bundesministerium für Arbeit und Soziales).

 

Als digital natives fällt Auszubildenden der Umgang mit digitalen Bedienschnittstellen meist leicht. Neben dem vermehrten Einsatz digitaler Medien, die sich aus diesem Grund anbieten, heben neue Formate großes Potential, Auszubildende noch besser auf die Tätigkeiten im Unternehmen vorzubereiten. Beispiele hierfür sind:

  • Frühzeitige Fähigkeits-Potenzial-Workshops: Diese erlauben es AusbilderInnen vor allem soziale Kompetenzen und Motivationen der Auszubildenden einzuschätzen. Hierfür bieten sich offene Problemstellung an, die in Teams gelöst werden. Dabei steht der Lernerfolg weniger im Vordergrund (ein außergewöhnliches Beispiel, das sich adaptieren lässt, ist der Eierwettbewerb). Relevanter ist die Beobachtung der Auszubildenden durch die AusbilderInnen. So werden vielversprechende Kommunikationsmuster für die jeweiligen Auszubildenden (z. B. vermehrtes Loben) sehr früh identifiziert, ggf. passende Ausbildungsformate gewählt oder bestehende Formate leicht angepasst.
  • Interdisziplinäre Workshops: In diesen Workshops arbeiten Auszubildende aus verschiedenen Fachbereichen zusammen an einer Problemstellung. Dies fördert die Vermittlung fachlich übergreifenden Wissens sowie die Teamfähigkeit. Auszubildende in höheren Lehrjahren können auch in Aspekten wie Problemlösungsfähigkeit, Stressresistenz und Selbstständigkeit gestärkt werden, indem das Umfeld von Workshops im geschützten Raum durch zunehmende Zielvorgaben (z. B. Zeitlimits) und ggf. deren Prüfung verändert wird.
  • Projektarbeit: Im Wesentlichen ähneln Projektarbeiten den oben genannten Workshops und vermitteln die gleichen Kompetenzen. Unterschiede sind die längere Dauer (bzw. zeitliche Verteilung der Tätigkeiten) und das geringere Maß an Interaktion mit den AusbilderInnen während der Projektarbeit. Welche Kompetenzen im Vordergrund stehen, kann bspw. durch Gruppengröße und Umfang bzw. den Anspruch des Projekts gesteuert werden.

Bei den Vorreitern der Digitalisierung ist der Einsatz von Smartphones, Tablets oder Laptops in der Ausbildung Alltag. Damit können z. B. Lernplattformen mit digitalen Lerninhalten sowie das digitale Ausbildungsheft aufgerufen werden. Viele Unternehmen gehen aber noch weiter. Im Folgenden werden einige Beispiele beschrieben:

Für die Ausbildung in der Zerspanung können virtuelle Anlagen die Ausstattung der Lehrwerkstatt ergänzen. Zum einen bieten diese Simulatoren die Möglichkeit, den Maschinenpark kostengünstig virtuell zu erweitern und damit mehr Auszubildenden zeitgleich Zugriff zu ermöglichen. Zum anderen werden Fehler bei der Einrichtung der Anlage angezeigt und verursachen keine Reparatur- und Ausfallkosten. Das Erlernen des Umgangs mit den realen Maschinen ersetzt der Simulator aber nicht vollständig und nach der Einlernphase erfolgt der Abschluss des Ausbildungsmoduls an der realen Maschine.

Auch der Aufbau einer Lern-Fertigungslinie mit Industrie 4.0-Inhalten ist sehr beliebt. Eine solche Fertigungslinie bestehend aus bspw. einem Förderband und einem Leichtbauroboter kann von den Auszubildenden in Projektarbeit aufgebaut und stetig weiterentwickelt werden. Dabei lernen sie etwas über den Umgang mit Robotern sowie über deren Steuerung und Programmierung. Ferner kann Auszubildenden die Möglichkeit gegeben werden, Erfahrungen mit der Prozesssteuerung und Fernüberwachung von Produktionslinien zu sammeln. Die reale Anlage steht bspw. in Berlin, sie wird aber von Auszubildenden aus Baden-Württemberg betreut.

Nicht immer muss es eine ganze Produktionslinie sein. Auch der Einsatz von Miniaturanlagen speziell für den Ausbildungsbetrieb erfreut sich zunehmender Beliebtheit. Diese Anlagen bilden den Stand der Technik modernster Anlagen ab, wie z. B. Fernüberwachung zu einem vergleichsweisen geringeren Preis, und ermöglichen das Erlernen unterschiedlichster Geschäftsprozesse eines Werkzeugmaschinenbauunternehmens. Zudem kann die Anlage natürlich auch für entsprechende Ausbildungsmodule (z. B. Zerspanung an einer Mini-CNC) eingesetzt werden.

Ein sehr beliebtes Format für kleinere Digitalisierungsprojekte ist die Vorbereitung von Aufbauten für lokale Ausbildungsmessen oder andere öffentliche Events, bei denen ein gewisser WOW-Effekt ausgelöst werden soll. Z. B. können mittels Augmented Reality-Brillen die Produkte des Unternehmens dargestellt werden. Gerade wenn gezielt für das Unternehmen als innovativer Ausbildungsort geworben werden soll, kann dabei Gamification (also ein spielerischer Charakter der Nutzererfahrung) mehr Aufmerksamkeit bei der Zielgruppe erzeugen.

Im Bereich der Lernformate und -methoden ist der Einsatz von Tablets oder Smartphones in Verbindung mit einer Lernplattform ein erster sinnvoller Schritt hin zur Digitalisierung. Hier gibt es zwei Ansätze. In „geschlossenen“ Lernplattformen ist der Lernrahmen sehr eng vorgegeben. Hier kann meist nur eingeschränkt Inhalt eingepflegt und die Lernmethoden verändert werden. Bei offenen Plattformen, wie der Plattform: „Mobile Learning in Smart Factories (MLS)“, kann dagegen deutlich stärker angepasst werden. Häufig gibt es aber weniger oder keinen Inhalt, mit dem direkt gestartet werden kann. Deshalb ist es sinnvoll, bei der Erstellung der digitalen Inhalte mit anderen Ausbildungsbetrieben zu kooperieren. Die Inhalte der Plattformen lassen sich auch nicht nur von AusbilderInnen, sondern auch von den Auszubildenden digitalisieren. So können bspw. Auszubildende verschiedener Fachrichtungen Lerninhalte für die nachfolgenden Jahrgänge gemeinsam erstellen, in dem sie ihr eigenes Wissen vertiefen und dann ein Lernvideo drehen. Dadurch werden sowohl die Inhalte digitalisiert als auch die Medienkompetenz der Auszubildenden gestärkt.
Kooperationen sind aber nicht nur bei den Lernplattformen sinnvoll. In Zusammenarbeit mit dem Bildungshaus der IHK Stuttgart haben sich bereits einige KMU zusammengeschlossen, um gemeinsame Projekte aus verschiedenen Fachrichtungen durchzuführen, auch zum Thema Digitalisierung. Dies kann ein sinnvoller Weg sein, die Digitalisierung in die eigene Ausbildung zu integrieren, wobei der Zusammenschluss nicht auf KMU begrenzt ist. Ein grundsätzlich wichtiger Schritt über den Lehrplan hinaus ist auch die Sensibilisierung der Auszubildenden zu den Themen Datensicherheit und IT-Landschaft. Möglich sind kurze Schulungen zu Gefahrenstellen und Negativbeispielen durch die Unternehmens-IT. Ebenso können Auszubildende ermuntert werden, selbstständig Digitalisierungspotenziale im Unternehmen aufzuspüren und Vorschläge zur Umsetzung machen.
Die Entwicklung und Verbreitung von Industrie 4.0 schreitet kontinuierlich voran. Deshalb ist es für ein Unternehmen umso wichtiger, Nachwuchskräfte auf ein anpassungsfähiges und flexibles Unternehmensbild vorzubereiten. Die betriebliche Ausbildungsorganisation erfordert ein weitaus höheres Maß an Interdisziplinarität über alle Berufsgruppen hinweg und muss eine stärkere Individualisierung aufweisen. Damit kann ein Kulturwandel für die betriebliche Ausbildung gelingen.
 
Eine wesentliche organisatorische Herausforderung ist die konstante Ausbildungszeit bei immer umfangreicher werdenden Lehrplänen. Trotzdem schaffen es einige Unternehmen, sehr individuell auf Auszubildende einzugehen. Dies kann es besonders den qualifizierten Auszubildenden ermöglichen, ihre Grundausbildung zu beschleunigen und dafür Zusatzqualifikationen zu absolvieren.
 
Aber nicht nur für die qualifizierten Auszubildenden kann eine Lösung zur Weiterbildung gefunden werden. Auch ein modularer, projektbezogener Ansatz in der Ausbildung kann den Auszubildenden einen Weg zu einem Verständnis von Industrie 4.0-Inhalten ebnen. Die Auszubildenden arbeiten dabei in kleinen Projekten für die Fachabteilungen und eignen sich die notwendigen Kenntnisse an,  wie z. B. die Nutzung additiver Fertigungsverfahren. Mit jedem weiteren Projekt, das in der Ausbildung durchgeführt wird, wird so der Wissensschatz, auch in Bezug auf Industrie 4.0-Inhalte, erweitert.
 
Um neue Ausbildungsinhalte zu erzeugen bzw. zu beherrschen, muss innerhalb des Ausbildungsteams klar geklärt sein, wer sich mit den neuen Thematiken befasst und sie ins Ausbildungsprogramm einführt. Hierbei sollten sich Unternehmen nicht einzig und allein auf die intrinsische Motivation der AusbilderInnen verlassen, sondern ausreichende Kapazität und eine einhergehende Erwartungshaltung an das Team schaffen. In der Praxis finden sich unterschiedlicheKonzepte. Einige Unternehmen verteilen die Themen innerhalb des gesamten Ausbildungsteams, während vor allem größere Unternehmen zum Teil neue Stellen mit spezifischem Fokus auf Digitalisierungsinhalten schaffen.
 
Neben der Organisation der AusbilderInnen ist auch die schnelle und erfolgreiche Integration der Auszubildenden ins Unternehmen ein wesentlicher Erfolgsfaktor. Ein möglicher Ansatz sind die sogenannten Ausbildungsbeauftragten. Diese MitarbeiterInnen, die keine AusbilderInnen im engeren Sinne sind, dienen als Anlaufstellen für die Auszubildenden in den Fachbereichen. Sie stehen mit Rat und Tat zur Seite und kümmern sich um verschiedene fachliche und nicht-fachliche Themen. Einige Unternehmen gehen noch weiter und stellen allen Auszubildenden eine/n eigene/n Mentor/in zur Seite. Diese/r ist die/der ganz persönliche Ansprechpartner/in für die Belange des einzelnen Auszubildenden. Sowohl die Ausbildungsbeauftragten als auch die Mentoren sind in vielen größeren Unternehmen bereits Standard. Aber auch kleine und mittelständische Unternehmen können hiervon profitieren.
Generell müssen skeptische MitarbeiterInnen sensibel und behutsam überzeugt werden, dass digitale Technologien einen Mehrwert darstellen und keine Gefahr für den eigenen Arbeitsplatz sind. Die leitende Ebene der Belegschaft muss klar kommunizieren, dass Digitalisierung angezielten Stellen zu einer Erleichterung der Arbeit führt, die den Arbeitsplatz durch Effizienzsteigerung sichert und damit neue und häufig interessantere (Teil-)Aufgaben für die Beteiligten bedeutet. Auf der Gegenseite müssen die Vorgesetzen hochmotivierten MitarbeiterInnen möglicherweise vermitteln, welche Geschwindigkeit der Digitalen Transformation für das Unternehmen erwartet werden kann.
 
Neben dieser „Top-Down“-Kommunikation sollte auch die Integration von FacharbeiterInnen 4.0 gefördert werden, die typischerweise eine positive Sichtweise vertreten. Die oben erwähnten Ausbildungsbeauftragten und MentorInnen sind dabei nur ein Pfad, um Wissen und das entsprechende Mindset über die Belegschaft zu verteilen. Bereits während der Ausbildung sollten Auszubildende zudem verstärkt in Optimierungsprojekte, wie z. B. den Kontinuierlichen Verbesserungsprozess (KVP) oder größere Digitalisierungsprojekte, integriert werden. So lernen sie den Transformationsprozess kennen und kommen häufiger mit fertig ausgebildeten Fachkräften in Kontakt, was das gegenseitige Verständnis innerhalb der Belegschaft verbessern kann.
Technologische Entwicklungen erfolgen in immer kürzeren Zyklen. Es ist für die persönliche und unternehmensweite Wettbewerbsfähigkeit daher wichtig, auf dem aktuellen Stand der Technik zu bleiben. Fortbildungen und Seminare für weitergehende methodische und fachliche Kompetenzen gehören für Fachkräfte dazu. Für alle MitarbeiterInnen bedeutet dies, dass lebenslanges Lernen notwendig ist.
 
Dieses Lernen kann sowohl innerbetrieblich als auch extern erfolgen. In der externen Weiterbildung bieten die Handels- und Handwerkskammern verschiedenste Zertifikats- und Meisterlehrgänge an, die im Kontext der Industrie 4.0 entwickelt wurden. Hier lohnt sich ein Blick auf die jeweiligen Webseiten der nächsten IHK oder Handwerkskammern.
 
Unabhängig vom Ort der Weiterbildung sollte der Fokus im Bereich der Digitalisierung bedarfsgerecht an MitarbeiterInnen und ihren Arbeitskontext angepasst werden. Dabei muss auf die persönlichen Wünsche und Ziele geachtet werden. Eine angepasste Weiterbildung stärkt die Unternehmensstruktur und das Individuum und führt zu einer offeneren Unternehmenskultur.
Trotz Lernformaten wie digitalen Lernplattformen haben AusbilderInnen auch in Zukunft die Schlüsselrolle für eine erfolgreiche Ausbildung 4.0 inne. Dafür müssen sie sich vor allem fachlich, aber auch didaktisch bzw. methodisch weiterentwickeln und die neuen Inhalte verinnerlichen. Bei den Vorreitern haben sich die AusbilderInnen eigenständig weitergebildet und sich die neuen Kompetenzen im Selbststudium angeeignet. Da dies nicht immer möglich ist, führt das Bundesministerium für Bildung und Forschung (BMBF) zusammen mit Partnern derzeit das Projekt Q 4.0 durch. Hierbei sollen so genannte „MIKA-Seminare“ und ein „Netzwerk Q 4.0“ entstehen. Partner sind das Bundesinstitut für Berufsbildung, die IHK Akademie Schwaben (MIKA-Seminar) sowie das Bildungswerk der Baden-Württembergischen Wirtschaft e.V. (Netzwerk Q 4.0).
 
Aber auch selbstverantwortlich gehen einige Unternehmen neue Wege, um AusbilderInnen weiterzubilden. So führen AusbilderInnen zum Teil eigene Projekte mit den Auszubildenden durch, in denen sie ihre Kenntnisse über das Vorgehen zur Wissensaneignung weitergeben. Die eigentliche Aneignung der technischen Fähigkeiten erfolgt dann im Tandem mit den Auszubildenden.

Für die Ausbildung 4.0 ist eine sehr enge Kommunikation zwischen LehrerInnen in Berufsschulen und AusbilderInnen in Industrieunternehmen notwendig, da in diesen Lernortkooperationen beidseitig positive Effekte entstehen. So können sich BerufsschullehrerInnen über aktuelle Themen in der Industrie informieren und diese in den Unterricht einfließen lassen. Die AusbilderInnen lernen im Gegenzug Veränderungen im schulischen Lehrplan besser kennen und können diese in die Planungen der Praxisteile einfließen lassen. Gerade im Aufbau von Workshops und Projekten kann dies hilfreich sein.

Ein weiterer Punkt ist die Nutzung von Lernfabriken in den Berufsschulen. Jede zweite Berufsschul ein Baden-Württemberg hat bereits eine Lernfabrik, in denen Industrie 4.0-Inhalte gelehrt werden können. Eine Kooperation zwischen der Vermittlung der Industrie 4.0-Inhalte in den Lernfabriken sowie praktischen Anwendungen in der Industrie ist für die Auszubildenden häufig förderlich.

Um diese engere Verknüpfung zwischen schulischer Ausbildung und Ausbildung im Unternehmen zuschaffen, müssen AusbilderInnen und BerufsschullehrerInnen proaktiv aufeinander zugehen und selbst Austauschformate, wie z. B. Unternehmensbesuche oder Themenabende, organisieren.

Gerade KMU stehen häufiger vor der Herausforderung, geeignete BewerberInnen für Ausbildungsplätze zu finden. In der Regel stehen sie mit größeren Unternehmen in Konkurrenz, die einen höheren Bekanntheitsgrad genießen. Sie müssen daher eher mehr Zeit und Mittel in die Suche investieren, wenn sie ihre freien Ausbildungsstellen besetzen möchten. Gerade in ländlichen Gegenden ist ein regionaler Fokus erfolgsversprechend. Wie in Frage 5 bereits erwähnt, sind innovative Stände auf lokalen Ausbildungsmessen eine gute Möglichkeit, auf sich aufmerksam zumachen. Dabei wird es von vielen Jugendlichen positiv und authentisch aufgenommen, wenn  Auszubildende am Stand vertreten sind. Neben diesen Messen kann aber auch der Besuch eines Betriebs helfen, Interesse und Begeisterung bei der Zielgruppe zu schaffen. Solche Veranstaltungen sind in der Regel etwas schwer zu organisieren bis man ein Beziehungsnetzwerk mit den Schulen aufgebaut hat. Sie setzen aber das eigene Unternehmen wesentlich besser in Fokus, als es auf Messen in der Regel möglich ist. Zudem ermöglichen sie den frühzeitigen Aufbau von Beziehungen mit aussichtsreichen KandidatenInnen für das Unternehmen. Darüber hinaus kann die Attraktivität des Unternehmens durch das Angebot von Unterstützungsleistungen wie Firmentickets gefördert werden. Letztere erhöhen zudem die Mobilität der Auszubildenden und vergrößern damit die Einzugsreichweite möglicher Bewerber.
 
Von vielen Kammern, wie z. B. die IHK Stuttgart, werden regelmäßig Azubi-Speed-Datings veranstaltet. Hierbei treffen BewerberInnen in virtuellen oder realen zehnminütige Bewerbungsgespräche auf VertreterInnen von Unternehmen, die einen Ausbildungsplatz anbieten. Fallen diese positiv aus, erhalten die potenziellen Auszubildenden eine Einladung zu einem Vorstellungsgespräch, Probetag oder Praktikum im Betrieb.
 
Soziale Medien haben zwar etablierte Methoden der Jobsuche, wie z. B. Jobbörsen (Monster, StepStone u. a.), und regionale Ausbildungsmessen nicht komplett verdrängt, den sozialen Online-Netzwerken kommt aber eine zunehmend wichtige Rolle bei der Bewerbung von Stellen zu. Berufserfahrene oder StudienabgängerInnen informieren sich auf Networking- und Career-Plattformen wie Xing oder LinkedIn. Die Zielgruppe der potenziellen Auszubildenden findet sich dagegen häufiger auf Bild- und Videoplattformen wie Instagram und TikTok. Die Inhalte auf Letzteren haben einen visuellen und weniger formellen Charakter. Neben der Vorstellung des Unternehmens und möglichen beruflichen Weiterentwicklungschancen können gerade dort  Ausbildung 4.0-spezifische Projekte vorgestellt werden, die die Jugendlichen besonders interessieren könnten. Beim Einsatz sozialer Online-Medien sollte dabei immer die Datensicherheit beachtet werden.
Im Rahmen des Wettbewerbs werden herausragende Projekte und Initiativen ausgezeichnet, die die berufliche Bildung in Unternehmen im Zusammenhang mit Industrie 4.0 fördern. Hierzu zählen in diesem Kontext Auszubildende, Duale Studierende und längerfristig gebundene WerksstudentInnen (keine Bachelor, Master- oder Studienarbeiten) aus Universitäten und Fachhochschulen.
 
Auszeichnungsfähig sind dabei innovative Lernformate, strukturierte Ausbildungsprogramme und Umsetzungsprojekte mit Bezug zu Industrie 4.0, in deren Entwicklung und ggf. Umsetzung Auszubildende oder Studierende aktiv eingebunden waren bzw. durch deren Einsatz eine Verbesserung der Ausbildung oder des Studiums erreicht werden konnte. Auch unternehmensübergreifende Projekte und Initiativen können ausgezeichnet werden.
Bewerben können sich Industrieunternehmen mit Sitz, Niederlassung oder einer Betriebsstätte in Baden-Württemberg. Das Projekt/die Initiative, mit der sich das Unternehmen bewirbt, muss in Baden-Württemberg ausgeführt worden sein und einen Industrie 4.0-Bezug haben. Geplante Aktivitäten werden nicht ausgezeichnet.
 
Die Gewinner werden durch das Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg feierlich ausgezeichnet und mit Ihren Projekten auf der Website der Allianz Industrie 4.0 Baden-Württemberg vorgestellt. Darüber hinaus gibt es in jeder Auszeichnungsrunde Preise für die Auszubildenden. Das Bewerbungsformular und weitere Informationen zur aktuellen Bewerbungsrunde finden Sie hier.

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