Artificial intelligence is still little used in production – especially in medium-sized companies. These challenges still exist in companies.
In industrial companies, the expectations of artificial intelligence (AI) are high, but it is still not used much in production – especially in medium-sized companies. There is ambiguity, for example, in the assessment of the economic benefit, the applicability in one’s own production environment as well as in the collection and use of data. This is the result of an online survey conducted by the AI innovation project IIP-Ecosphere with the support of the project partner VDW. The Federal Ministry of Economics is funding IIP-Ecosphere as part of the AI innovation competition to accelerate the use of AI in production. The aim of the survey was to determine the current status and practical challenges of companies with regard to the use of AI and related topics such as data collection, cloud use, selection and framework conditions of AI solutions and Industry 4.0 platforms. “With the survey, we wanted to show necessary, real fields of action in which IIP-Ecosphere can now actively act as an accelerator for the utilization of AI methods,” explains Dr. Claudia Niederée from the L3S research center, project leader of IIP-Ecosphere. 75 companies took part in the survey, two thirds of them from the machine tool industry. The podcast episode with AI expert Markus Ahorner is also about artificial intelligence:
More than half have not yet had time to deal with AI
What is striking is the high participation of larger and very large companies, at 70 percent. The background could be a greater preoccupation with AI topics compared to medium-sized companies. Compared to other surveys, a relatively high proportion of over 37 percent of those surveyed stated that they were already involved in AI-based solutions in their company. In contrast, however, there are more than half of those surveyed who find the topic of AI exciting but have not yet had the time or opportunity to deal with it. Overall, the answers show high expectations of AI and its benefits for products and services. 60Percent of the respondents express themselves accordingly. Only 7 percent state that AI is overrated. Most of those surveyed agreed that AI should be used to support and not replace people in production.
Assessment of practical use difficult
The number of providers and solutions for the implementation of AI projects in production is growing steadily; the market is becoming increasingly confusing for users. What makes the selection even more difficult: Often several AI solutions have to be integrated into hardware components, such as machine controls. This complexity is reflected in the classification of solutions: the most common obstacle in identifying suitable AI solutions, at 65 percent, was problems in evaluating the economic benefit of an AI solution for their own application context. At 64 percent, in second place is the question of whether the respective AI solution can be used at all in its own context. “In order to offer companies a faster overview of available AI solutions and their applications, IIP-Ecosphere is developing an AI solution catalogue, which addresses the problems of those surveyed and includes aspects of benefit and applicability,”
Legal uncertainty on the subject of data sharing
The survey results show that over 90 percent of companies already collect production data. However, almost half of those surveyed say that collecting the data needed for AI solutions poses problems for their company. There is therefore a clear need to catch up when it comes to the needs-based collection of AI-relevant data. A mixed picture emerges when it comes to data sharing: On the one hand, 57 percent of those surveyed think that the Other companies’ data could benefit, but only 16 percent would purchase non-company data. 59 percent still see a need for clarification on the legal issues. “The responses of those surveyed also make it clear how necessary it is to understand the legal regulations in order to be able to clarify the framework conditions for the use of external data,” says Hans-Dieter Schmees, project manager for technology and standardization at the VDW. Almost half of those surveyed stated that they use cloud solutions to handle internal company data. Interestingly, however, around two thirds of the companies also agree that production data must not leave the company. Just under 10 percent of those surveyed who commented on cloud solutions primarily rely on an on-site solution and would not use a cloud solution.
IIoT platform mainly used by larger companies
Industrial production is increasingly using IIoT platforms that support the coordinated control of machines and the centralized collection of data. Almost a third of the companies surveyed are already using such a software solution, and almost 45 percent have not planned to use it. Almost 7 percent of the companies that use a platform use their own development. According to survey results, however, it is primarily larger companies that are already actively using IIoT platforms. dr However, Holger Eichelberger from the Software Systems Engineering working group at the University of Hildesheim estimates a trend towards the increased use of IIoT platforms, which is also confirmed by the survey: “In the medium term it can be expected that around half of those surveyed Companies from large corporations to SMEs will soon be using such software.” An obstacle to the use of AI, which was mentioned in particular in the free comments, is the age of the company’s own production machines, with which the necessary data cannot be recorded or can only be recorded with great effort. Concerns are therefore arising, especially among SMEs, that they could miss out on Industry 4.0 and AI. In addition, some companies are afraid that research and business development will lose sight of them or that they have no opportunities to (help) shape relevant developments. “Cooperation in a spirit of partnership across disciplines is therefore essential for future clout and for securing a competitive advantage, at least in the classic, economically strong disciplines,” says Dr. Alexander Broos, head of research and technology at the VDW. The open ecosystem approach of IIP-Ecosphere with many participation opportunities and offers offers companies the chances and opportunities they need to tackle challenges together and to develop technological alternatives that make AI accessible to as many companies as possible.
Artificial intelligence is currently a hot topic in all sectors – including laser technology. How to choose an AI solution for better processes.
How can data and algorithms be used to increase the quality and efficiency of processes in laser processing? Researchers, laser technology manufacturers and users are asking themselves this question at the same time, because with the emergingIndustry 4.0, with solutions artificial intelligence (AI) are also becoming increasingly important. The first solutions in the field of laser technology that uses artificial intelligence already exist.
How is artificial intelligence used in laser material processing?
For example, machine builder Trumpf uses artificial intelligence to control laser systems using voice commands. With the system equipped with a marking laser, the system operator can issue all relevant control commands such as ‘Open/close the door’, ‘Start the marking process’ or ‘How many products have you marked today?’ speak directly into a microphone. Thelaser systemresponds accordingly and executes the voice command. You can find out more about the machine in our article ‘Trumpf: AI moves into laser material processing ‘. However, significantly more researchers and manufacturers are dealing with AI systems in the field of quality assurance – some AI applications are already being developed, especially for laser welding. Most of the time it is about the quality control of the welds. The Fraunhofer Institute for Laser Technology (ILT) is also researching in this area. “We want to use our AI solution to recognize different quality categories in laser welding,” reports Christian Knaak. The scientific employee of the Fraunhofer ILT deals with Zero Defect Manufacturing and in this context with Deep Learning and spoke about it at the first ‘AI for Laser Technology Conference ‘ in Aachen. “We want to detect seam collapse, binding errors and incorrect seam widths,” Knaak continues. “And we also want to recognize when the seam is in order.” For this purpose, the measurement data from welding processes – in this example the camera images of the weld seams – are evaluated and the system is then supposed to make statements about the quality of the seams. There are two possible approaches: the classic machine learning approach and deep learning.
What do I need for a successful AI project?
“AI needs a sustainable database. And a team that implements the project together and in which the individual members have the necessary specialist knowledge from the departments involved.” Christian Kohlschein, Hotsprings GmbH
“Machine learning sometimes needs bad process results. If too many results are good, then the algorithm says that everything is good. But then the AI solution is useless.” Stephan Schwarz, Mercedes Benz AG
“The biggest challenge when introducing AI solutions is the cultural transformation of the company. Faster technology and better algorithms alone cannot make a difference.” Benjamin Kreck, Microsoft
“When selecting suitable algorithms for quality analysis, the domain knowledge of the employees is required. This is the only way to find suitable classifiers and algorithms.” Christian Knaak, Fraunhofer Institute for Laser Technology
What is the difference between classic machine learning and deep learning?
In machine learning, knowledge about the area of application (so-called domain knowledge) plays an important role. Image processing algorithms are used to extract and classify features from the camera images. “We have five classes,” explains Knaak. “These are typical images of welding tests with examples of lack of fusion, seam collapse, increased night width, OK seams and no seam.” The features of these images are then used as a ‘fingerprint’ for the various seam conditions to be recognized by an algorithm. The appropriate algorithm is selected by determining the recognition rates of various algorithms using a training data set. The algorithm with the best detection rate can then be used for the process data. Deep learning requires less domain knowledge but requires significantly more data. “The special thing about this method is that the task of extracting features, which was taken over by image processing algorithms in classic machine learning, is now handed over to a neural network,” explains Knaak. This neural network recognizes the features and processes the data in several layers. This makes it possible to analyze very complex image features. “The end result is the same as with the classic approach, where you need a relatively large amount of domain knowledge,” reports Knaak. Both methods are therefore suitable for analyzing the seam quality, only the prerequisites differ.
Whether in industry or in the private sphere – artificial intelligence is on everyone’s lips. But what does artificial intelligence mean? In this article, we answer the most important questions on this topic
Artificial intelligence is one of the really big future topics of our society
How intelligent is artificial intelligence really?
How does artificial intelligence work?
How do you program artificial intelligence?
What do we need artificial intelligence for?
Where is artificial intelligence used?
Smart machines: How is artificial intelligence changing our lives?
How is artificial intelligence changing our society?
What is artificial intelligence?
When developing systems with artificial intelligence (abbreviated: KI, English: Artificial Intelligence), researchers and developers try to emulate human perception and human action using machines. An official definition of the term Artificial However, there is no such thing as intelligence, which is partly due to the abstract nature of the term intelligence and the rapid change in the subject area. Therefore, AI is mainly defined by its properties and the associated sub-areas, such as speech recognition, image processing or machine learning. Properties that are characteristic of AI are autonomy and adaptivity. AI systems have the ability to perform tasks in complex environments without constant human guidance. They are also able to improve their performance independently by learning from their experiences. In general, the systems are divided into weak and strong AI:
⦁ Weak AI refers to machines that can replace a single human cognitive ability. Systems with weak AI perform a specific task and behave intelligently. ⦁ A strong AI would be a machine that has the same abilities as a human or even surpasses human abilities. The system with strong AI could thus fulfil any intellectual task. It would be intelligent (compared to the weak AI that just behaves intelligently) and would be conscious.
The AI solutions that exist to date all fall into the ‘weak AI’ category. Strong AI systems only exist in the field of science fiction. The term artificial intelligence includes many sub-areas, which in turn include numerous AI methods and AI applications, for example:
Machine learning or the special form of deep learning, in particular, are often regarded as the most important method of artificial intelligence, because learning from experience and the ability to generate new actions from it is evidence of intelligence.
How intelligent is artificial intelligence really?
The answer to this question is closely related to the definition of the word ‘intelligent’. Because the question is whether intelligence is the same as intelligent behaviour or whether intelligence requires a brain and a consciousness. Provided that intelligence equals intelligent behaviour, the English mathematician and logician Alan Turing developed a test to determine whether a machine is intelligent. In the so-called Turing test, a human investigator interacts with two chat partners – one of them is human, one is a computer. If the investigator is not able to distinguish artificially from human intelligence by exchanging written messages, the computer has passed the test and must therefore have reached the level of human intelligence. But even if a machine behaves intelligently and thus passes the Turing test, it still does not have a human brain. If this is assumed for real intelligence, then only strong AI systems would be really intelligent. For the areas in which AI is currently being used, the question of ‘real intelligence’ is not that important. What is far more important is how well a system can perform the task for which it is designed. In this context, the following often applies: artificial intelligence is only as smart as the data with which it is trained
How does artificial intelligence work?
The topic of artificial intelligence includes many AI methods and AI applications, each of which has different functionalities. In general, the intelligent behaviour of the technologies is simulated using computer science and mathematics/statistics. The computers are trained for specific tasks, often by processing large amounts of data and recognizing patterns in it. This requires certain skills that can be divided into four areas: perception, understanding, action and learning.
Perceive: This skill creates the data that an AI-powered system operates on. 1. Sensors of all kinds, including cameras and microphones, are used for this purpose.
Understanding: This part of the AI is the processing component from which the control panel emanates. The data obtained is processed using statistical analysis, speech/image recognition, algorithms that define special rules, or machine learning.
Action: This is the output component of the AI that issues commands to connected devices, triggers follow-up processes, or outputs other things such as images, translations, and the like.
Learning: This area is special about current AI technologies. You can learn from mistakes and feedback from the areas of ‘understanding’ and ‘acting’ during the training phase and also during operation.
Algorithms and neural networks play an important role in both ‘understanding’ and ‘learning’.
What are algorithms?
Algorithms are detailed and systematic instructions that define step by step how a mathematical problem can be solved. Algorithms are implemented, ie translated into a programming language, so that the computer/machine can generate the desired solution from the given information. There are, for example, sorting or search algorithms (these include Google’s algorithms), but also algorithms that can make complex decisions based on all relevant factors. To do this, an algorithm takes all the factors (which have to be defined beforehand by a human) and links them in all possible variants. He then goes through every possible combination and its consequences and compares them with the programmed specifications. From this, the algorithm calculates the answer that is most likely to be correct. The class of learning algorithms, which are summarized under the generic term machine learning, is particularly important for AI. You can learn from a large number of example cases and derive general rules. After the learning phase, you can apply these insights to real cases. Among the machine learning algorithms, there are also deep learning algorithms that are used to analyze and process particularly large amounts of data – in connection with neural networks.
How do neural networks work?
Artificial neural networks (ANN) are the attempt to artificially reproduce aspects of the human brain known from research. It is mainly about simulating and using the interaction of nerve cells (neurons) and their connections (synapses). The goals of artificial neural networks are, on the one hand, a better understanding of the human brain and the processes taking place there and, on the other hand, advances in machine learning by linking huge amounts of data (big data) and deep learning techniques. A single neuron is a simple information processor that can examine exactly one aspect of information. To process very complex information, many neurons are therefore connected to each other. These connections are called synapses, they form a complex network between the neurons. A neural network (regardless of whether it is biological or artificial) consists of a large number of neurons that can receive signals and transmit them to other neurons via connections. There are three types of neuron layers in an ANN:
Input Layer: The first layer of neurons for raw processing of the information. These neurons feed pre-processed information into the network.
Hidden Layer(s): One to a finite number of neuron layers with linked neurons. During the training, you will learn how to read patterns from the raw information using examples. From these, complex typical characteristics are worked out in order to solve the task.
Output Layer: The last neuron layer – the so-called output layer. It can represent any possible outcome.
The big advantage of artificial neural networks compared to ‘classical’ data processing is that they can consider several aspects at the same time. While a ‘simple’ algorithm analyzes each property of a data set one after the other, in the layers of the ANN all properties can be analyzed simultaneously. ANN is used, for example, for image processing, speech recognition, early warning systems or process optimization. It is precisely this process optimization or error detection (via image processing or audio analysis) that is already being used in companies as part of Industry 4.0.
How do you program artificial intelligence?
How a system is programmed with artificial intelligence, depends a lot on what you want the system to do. As with the programming of any computer program, the first priority is choosing the right programming language. The most commonly used programming languages for AI are:
Python has become the de facto standard for machine learning and big data analysis. It is very versatile and suitable for numerous fields of application, especially in the area of artificial intelligence and the Internet of Things.
Prolog is considered the most important logical programming language and is therefore particularly suitable for simulators, generators, as well as systems for diagnosis and prognosis – exactly what should often be created in AI projects. For example, the language processing components of IBM’s AI ‘Watson’ are written in Prolog. In addition to Prolog, Watson and the associated DeepQA software engine are also based on Java and C++, among other things.
find out why IBM is opening its Watson IoT headquarters in Munich.
As an object-oriented programming language, Java is very versatile and can therefore also be used well for AI programming. Java is particularly suitable for natural language processing, search algorithms and neural networks. Java is slower than C++ but easier to program.
C++ is particularly relevant for time-critical AI projects, as it is the fastest computer language. It is also particularly extensive and strong in statistics. This makes it ideal for machine learning and neural networks.
LISP is particularly strong in inductive logic and machine learning, making it well-suited for AI as well.
R is mainly used in the context of big data for data analysis. The programming language is mainly suitable for statistical methods and allows the free calculation of statistics and graphics.
In addition to an AI-friendly programming language, program libraries and frameworks such as TensorFlow, Torch, Keras or Caffe are required for AI implementation. The most important subprograms, routines and algorithms for AI development are already implemented here, which can then be transferred to your own AI solution.
What do we need artificial intelligence for?
Algorithmic decision-making systems are intended to support or replace people in making decisions. In the area of big data, in particular, this is necessary in order to make decisions possible at all, because here people would not be able to make any well-founded decisions based on a large amount of data alone. Processes can be accelerated with AI support, especially with the increasing computing capacity of computers. This makes it possible to carry out tasks that humans or machines without AI could not previously handle due to time constraints. In addition, AI-based systems, especially machine learning, make it possible to predict what will happen in the future. In general, artificial intelligence is one of many tools to solve problems. AI makes it possible to do things that were previously not possible. It allows overcoming long-standing borders. Therefore, AI systems make a strong contribution to competitiveness.
Where is artificial intelligence used?
The areas of application for artificial Intelligence are extremely diverse. They range from spam filters and product recommendations in e-commerce or streaming services to language assistants such as Alexa or Siri and chess computers to self-driving cars. With the digitization of industry, ie Industry 4.0, many companies are also adopting AI-based systems (although not all Industry 4.0 applications contain AI in the same way). According to the Federal Association of the Digital Economy, AI solutions are generally found in the area of data collection and analysis in the industrial environment. Examples for this are
optical inspections through AI applications in the field of quality assurance, which make error or process analyzes possible,
Process automation in manufacturing and assembly, which includes self-regulating adjustment of control parameters,
Robots with artificial intelligence, which can perform even better⦁ ‘bin picking’,
forward-looking data analysis in order to maintain machines and systems as required ( ⦁ predictive maintenance),
voice control of machines,
Chatbots for ⦁ virtual customer service other
ERP systems with AI strategies that optimize internal production structures and processes.
Smart machines: How is artificial intelligence changing our lives?
Spam filters, personalized advertising on the Internet, more relevant search results in online searches, chatbots for customer service inquiries and navigation devices that include traffic jams and construction sites in the route calculation – these are all systems that work with artificial intelligence and are changing our daily lives. In many cases, it ensures greater efficiency: while in the past you could spend hours waiting in the manufacturer’s queue because of every small question or problem with a product, today chatbots can provide quick and uncomplicated help in some cases. Cars park automatically, stays in lane and keep their distance, and recognize street signs. Our smartphones help us write messages, correct our spelling mistakes and even predict which words we will use. And you recognize our face and then unlock it automatically.
How does Face ID / facial recognition work?
What is certain is that artificial intelligence is and will be finding its way into many areas of life. Until autonomous driving spreads it will take a while, but smart home applications (eg for saving energy) and digital voice assistants are becoming more and more popular. While only 390 million people used AI-supported assistants in 2015, in 2019 there were almost 1.38 billion. According to a forecast by Tractica, the numbers will continue to rise; By 2021, voice assistants are expected to have 1.83 billion users. Not only are the users of language assistants such as Apple’s Siri, Amazon’s Alexa or Google Assistant increasing, more and more people are also using AI systems in general: 73 per cent of those surveyed in a Bitkom study stated that they had already used a simple application based on builds AI. And that’s no coincidence: The majority of Germans consider artificial intelligence to be useful – 88 per cent of the Germans questioned in a PwC survey stated that artificial intelligence will help to master the challenges of the future. They consider AI to be particularly helpful in the areas of cyber security (49 per cent), clean energy/climate change (45 per cent) and protection against diseases (43 per cent). So if AI researchers manage to develop systems that can master these very challenges, they will be a thing of the past. The technologies can help reduce traffic congestion, speed up administrative tasks, and improve medical diagnosis and treatment.
How is artificial intelligence changing our society?
Artificial intelligence has an impact on almost all areas of our society. It changes how we communicate, meet new people, consume news and do our work – in positive and negative ways. For example, many people consume News less via platforms aimed at the general public such as television, but in social networks, where they receive personalized content with AI. From 2013 to 2019, the use of social media as a news source in Germany has increased from 18 per cent to 34 per cent; This is according to the Digital News Report 2019 of the Reuters Institute for the Study of Journalism. According to a Kantar study, the main reasons for this are easy access to a wide range of news sources and the ability to comment on news and share it with others. While on the one hand-personalized messages are very pleasant, they also encourage the emergence of so-called ‘filter bubbles’ and the separation of different social classes. This can lead to societal problems. Autonomous means of transport and intelligent traffic control will not only support climate protection in the future but will also create more free time for people, which in turn can be used for work or hobbies. At the same time, people’s areas of responsibility are shifting to areas in which creativity and empathy are required.
How do self-driving cars work?
Likewise, artificial intelligence will transform policing and the justice system. Face recognition will become just as common as fingerprints. And analysis algorithms will also become more relevant: Courts in the USA, for example, already use software that calculates the risk of reoffending by criminals. The calculations are based on information about the person and their radius, but also on the Analysis of all similar crimes committed. The emergence of many new technologies is changing our society not only directly, but also indirectly. For example, according to the KI-Bundesverband, the compulsory subject ‘digital education’ or ‘computer science’ urgently needs to be introduced in German schools. Because it is irresponsible to release young people into the increasingly digitized world without active support.
This case study shows which criteria you should definitely know for the successful use of Robotic Process Automation
A manufacturer of machine systems and special machines ensures more efficient work processes in the company with Robotic Process Automation (RPA). Together with the management consultancy ROI-Efeso, it determines areas of application for RPA in eight business areas. After only three months, the software bots complete 14 processes independently and reliably. You can find out how the experts from ROI-Efeso proceeded here. The case study: initial situation and measures in brief Starting position:
⦁ Data maintenance and information exchange in different formats cost time and generate errors ⦁ The process landscapes in operations, quality management, controlling and other business areas are to be redesigned.
⦁ Determination of fields of application and tasks for software bots (Robot Process Automation) including detailed recording and development in all departments involved ⦁ Immediate use after testing in practice
⦁ Test phase with the real and the automated process running in tandem ⦁ Calculation of the ROI of the software bot for the sum of all use cases
Robotic Process Automation: What software bots can do
For work tasks that are very repetitive, time-consuming and error-prone, there are now smart technology assistants: In the context of digitization and Industry 4.0, software bots take on a wide range of tasks in the operations area. Under the term Robotic Process Automation, they provide relief for simple but time-consuming work processes. In this case study, one objective was to centralize bookkeeping from five locations at the company’s headquarters without hiring additional staff. Instead, bots should take over tasks such as invoicing, dunning or the preparation of data from different sources for reporting from the first day of the changeover. The focus in the selection was on regular activities which are on the one hand so specialized that their automation in systems like SAP is not worthwhile – but which on the other hand tie up considerable time resources.
Industry 4.0: Award from ROI-Efeso and the magazine Production
Digital assistance systems, data analytics, artificial intelligence or machine learning are changing the value creation processes in the manufacturing industry at breakneck speed. Companies that manage to successfully integrate these digitization technologies, tools and systems into their value creation processes are among the pacesetters of Industry 4.0. Together with the trade newspaper PRODUKTION, it has been honouring ROI-Efeso with the Industry 4.0 Award since 2013 – one of the most important benchmarks for digitization projects and Industry 4.0 best cases. The second objective and special challenge of the project was to expand the change from manual to RPA-controlled work processes in just a few months to include operations, R&D, production and logistics. Together with ROI-Efeso, the company quickly implemented the appropriate bots. Just two weeks after the start of the project, the first bot was already saving half an hour a day with a previously manual inventory correction of shortfalls in SAP.
Testing and deploying
The project kicked off with an information event at which all employees involved got to know the deployment options of RPA. ROI-Efeso explained the limits of the technology very clearly – after all, those involved should be able to develop a realistic picture of the effort involved in introducing RPA to its benefit. After this kick-off, the project team gathered ideas and suggestions on work processes that the employees believe could adopt RPA tools. In the days that followed, it selected 14 suitable processes from over 40 ideas in one-on-one discussions, and a detailed analysis was then carried out on each of them.
Industry 4.0 – An RFID module guides you through the Future Factory – Source: ROI-Efeso
On the basis of this knowledge, the project team created an automation concept for all areas involved, in which it prioritized the most attractive processes. The selection of the RPA software suitable for the company’s requirements and the assignment of authorizations were completed within two weeks in coordination with the IT department, so that implementation of the identified work processes could start immediately afterwards. The project team now brought the RPA into everyday life – department by department, process by process – always via the detailed recording, development, and test stations. In the test phase, the previous and the automated process ran simultaneously according to the “tandem” principle. In this way, errors could be corrected or hurdles overcome without delays in day-to-day business. Once handed over, every work process ultimately ran through the RPA, which immediately relieved the workload.
If you hate it – automate it!
In the project, three experiences proved to be particularly valuable for the implementation of RPA projects:
Identify “processes in the middle”! The maxim “If you hate it – automate it!” Is a good starting point for RPA projects. It is important to classify the intended use correctly: Which use cases are so simple that the programming effort for a system adaptation is not worthwhile – but automation? Software bots are intended for precisely these processes in the middle. In sum, this is where the greatest leverage for savings lies.
Exploit software bots 24/7! The license model of most RPA providers is designed in such a way that a software bot is available around the clock on every work and public holiday. As with employees, payment is made per bot. If the bot is not busy with tasks, the invoice is still due. Therefore, you should constantly look for other, suitable work tasks for the bot.
Make your expectations realistic! RPA is not a ready-made software that takes care of all extensions independently after purchase. If the process that the software bot has taken over changes, this change must be adapted. This should be clearly communicated in RPA projects in order to achieve realistic expectations of the possible uses of the bots.
4 consultant tips for your successful RPA use!
⦁ Operate targeted stakeholder communication! An RPA introduction is only successful if it is (pro) actively driven forward by the specialist department – in cooperation with IT. ⦁ Clearly define roles and responsibilities! Determine at an early stage who will monitor the bots, make adjustments or pay attention to compliance requirements. ⦁ Look beyond the existing rules and limits! RPA is a technology that helps with process optimization – but only for those who are able or willing to think process-oriented. ⦁ Build up internal RPA competencies! Nobody knows the processes as well as your employees. You should therefore create a Center of Excellence with internal consultants for the (further) development of RPA as well as for support with its application.
Careful testing saves expensive mistakes
In this as well as in other RPA projects by ROI-Efeso, controlling the previous manual process alongside the new automated process proved to be an important success factor. This is the only way to compare whether the desired results can be achieved. This also makes special cases and sources of error visible that were not yet present in the process recording. With this start-up support for each process, the RPA introduction takes a little longer – the transferred bot then works more reliably than with an implementation that is too fast. And that in turn saves time and money for troubleshooting later.
AI is playing an increasingly important role in mechanical engineering – innovation is mostly driven by startups. A VDMA study shows examples of successful cooperation. 42 per cent of the mechanical engineering-relevant AI startups come from Europe. – Picture: Blue Planet Studio – stock.adobe.com More and more companies in mechanical and plant engineering are relying on artificial intelligence (AI) to enrich their products with data-based added value. Start-up companies are playing an increasingly important role as cooperation partners. In an analysis of the past ten years, the VDMA, together with data specialist Delphi, identified a total of 825 startups in 46 countries that offer AI solutions for mechanical and plant engineering. 42 per cent of them come from Europe – so the continent trumps both North America (33 per cent) and Asia (24 per cent) in terms of the number of start-ups. The analysis also shows that more and more money is flowing into AI startups for mechanical engineering. Since 2015, the number of financing rounds has exceeded the number of start-ups. Almost 80 per cent of the total of 13.2 billion euros invested in AI startups worldwide from 2010 to 2020 was accounted for in the period from September 2017 to September 2020.
Europe as a startup forge for AI innovations
“Artificial intelligence for mechanical engineering will be the next stage in digital transformation. And our start-up scene in Europe is impressive. Due to the high degree of networking between regional start-ups and research networks with the vital mechanical engineering industry, Europe is a start-up forge for AI innovations in an industrial context. Germany is the centre of gravity in Europe with more than a third of the start-ups, ”explains Hartmut Rauen, deputy VDMA managing director. “Our strong mechanical engineering 4.0 domain attracts, creates dominance. As a user and provider of these startup solutions, we as a European mechanical engineering company make a significant contribution to bringing AI into the industry on a broad scale. We are therefore ideally positioned in the global race. “
AI is being used more and more in the industry
The new VDMA study “Startup Radar: Artificial Intelligence – Navigator through the global AI startup scene for mechanical and plant engineering” gives a deep insight into the AI startup scene for mechanical engineering. In addition, it uses practical examples to show the importance and application potential for the industry. It becomes clear that the market for AI startups for mechanical engineering is not only characterized by high investment and funding dynamics but that the solutions are also being used more and more in the industry. “Start-up activities are a strong indicator for the development of future markets. The investment dynamism reflects the high financial expectations that investors have of the automation of industry. With the key technology of AI, startups are taking the next level in networked production. And mechanical engineering also recognizes this and positions itself to take the threshold to this evolutionary stage, ”explains Dr Robin Tech, co-founder and managing director of the market intelligence company Delphi, which played a key role in preparing the study.
New services and business models
As the study also shows, artificial intelligence can be used to implement efficiency potentials in various fields of application of industrial value creation and to set up new services and business models. There are currently six innovation clusters that are particularly noticeable for mechanical engineering. These innovation clusters outline areas of application in which a high number of start-ups can currently be recorded and which are of high relevance for mechanical and plant engineering:
⦁ Process Monitoring & Operational Excellence: Data-supported monitoring and optimization of production and business processes using digital twins ⦁ Product Inspection & Quality Control: AI solutions for automating quality control and testing ⦁ Predictive Maintenance: AI-based predictive maintenance to reduce unwanted downtimes and extend the life cycles of operational systems ⦁ Autonomous Factory & Process Automation: AI-based automation solutions and robots in and outside the factory ⦁ Generative Design & Product Simulation: AI-supported creation of technical drafts or simulations of production and operational environments ⦁ Supply Chain Intelligence and Demand Forecasting: Optimizing supply chains and forecasting product demand using AI
Weaknesses in the investment ecosystem
Although Europe competes well in terms of the number of innovative AI start-ups for the industrial sector, it has weaknesses in the investment ecosystem. The study shows that the US and China are in number in terms of financing and investment volumes. Of the total of 22 financing rounds in the three-digit million range between 2010 and 2020, nine were made by Chinese companies, eight by American companies, two by Japanese companies, and one each by a German (Celonis) and an Israeli company ( Innoviz). Europe, therefore, needs more approaches to allow corporate venture capital to flow into its own promising start-ups and thus strengthen the European economy. According to the study, this requires both a different attitude in companies and government measures to promote the innovation and investment ecosystem.