What is the future of US manufacturing
Processes in production: outlook for a bright future
Free or not free? Have technical innovations Production processes often sustainably influenced and well-known and often established processes completely turned upside down. Such changes and innovations are so profound that they are observed and analyzed by science and society, and their possible effects in utopias, but also dystopias, have been and are being treated - sometimes a rosy future beckons in which people can live freely and individually Sometimes we steer into an uncontrollable system of monitoring and control of the algorithms.
Production processes are always changing
Whether the invention of the steam engine, the discovery of electricity, the industrial introduction of mass production or the development of the computer - these are all milestones in industrial history. The interaction between humans and their tools has always changed massively and rapidly. What changes in particular is the role and the relationships between the “actors” - which are dealt with in so-called actor-network theories.
Anyone who wants to look into the future of production must therefore keep a close eye on the past and present. Because technological innovations designed to make processes more effective and efficient have always existed. Often it is further developments or links to existing tools that ultimately trigger a change.
While we have previously understood tools, machines and apparatus as aids, digitization and networking as well as artificial intelligence have given them an independent and self-learning role as actors. AI systems draw knowledge from large amounts of data, recognize patterns and regularities in them and develop - step by step, via trial and error - a certain degree of autonomy. In the context of the manufacturing industry, artificial intelligence is therefore a crucial component of the smart factory.
From big to smart data
Due to the digital transformation, which is inexorably affecting logistics and industry, a lot of data is already accumulating. Because if every process in production is monitored with modern technology, then a corresponding number of signals arrive at the control center. IT solutions such as a Manufacturing Execution System (MES) generate useful information from this. This creates a digital image of production that reveals potentials and weak points and helps the user to improve production processes.
Today this software is designed for data acquisition, evaluation and visualization. In the future, it will be expanded to include AI in order to automate the analysis and control of manufacturing processes in a self-regulating manner. But for this, the data must be structured systematically - the step from big data to smart data is crucial for the success of such a system.
The aim is for the system to recognize patterns and regularities, provide specific solutions or recommendations for action or even implement them independently and learn from the following results and further optimize the processes.
The foundation of AI
The foundation is a clean and detailed recording of all relevant data - in addition to signals from machines, this also includes energy systems and processes, material flow data and the like. If such information is already available, an ideal basis has been created for the introduction of digital production management.
At the same time, this database also allows the training of AI-controlled systems, as reference values and key figures from ongoing production are available. In this way, ideal conditions and malfunctions can be defined so that the AI system can be geared towards a goal - because if no data is available or if initial training is not possible, the system has to test and analyze itself over a longer period of time in order to work effectively can.
The next level of digital transformation
While current systems are already working effectively and sustainably optimizing entire productions, KI is taking digital production management to a higher level: For example, a personnel requirement assessment and personnel deployment planning can be enriched with smart data, which means that future personnel planning can take significantly more factors into account than today common. Which production order needs which resource? At what time, with a view to other orders, delivery times and staff roster requests, can this resource be made available?
The AI system networks all relevant areas and data, learns the relationships and dependencies in the value chain and finds patterns and regularities based on them. The AI reacts more finely with every planning and feedback loop, its forecasts become more precise and the planning more effective.
Processes in production: Maintenance can also be digitized
The same applies to maintenance. The AI uses the immense data from production to identify relationships that lead to malfunctions. In this way, maintenance measures can be planned earlier and integrated into the order planning.
When using an AI, it makes sense to define a kind of target state. Then the system “knows” what it has to work towards and does this independently. If implemented correctly, this leads to high cost savings through increased effectiveness and the best possible use of the available resources.
Machine and operating data from production play an elementary role here, as they are recorded in a structured manner with the help of Manufacturing Execution Systems. They form the basis for initially training the AI system and setting the actual status of a production facility - historical data from production processes, transport and set-up times, energy data and the like are used by the AI system.
Working towards the target state of the processes in production
An AI-based production control works effectively and actively to achieve the company's goals. In order to implement such projects, MES providers such as GFOS mbH work together with highly specialized companies that conduct production-centered AI research and development. Such partnerships are the guarantee for exponential corporate success.
There are promising pilot projects, especially in the area of detailed planning, with great potential for further optimization. Because the fine-grained interplay of logistics, in-house material transport, equipping and retooling of machines and workplaces is crucial for time and quality-optimized production. The real-time monitoring of such complex processes through professional MES solutions as well as the integration of machine learning and AI creates great flexibility in everyday production.
Deviations from the detailed planning are recognized automatically and processes are corrected autonomously in order to proactively prevent bottlenecks, malfunctions, inferior batches and the like.
However, humans always have decision-making power over production management, but artificial intelligence makes their tools significantly more effective. The arrival of AI applications in the production halls is a decisive step towards realizing literal, smart factories.
Has Industry 4.0 come?
The research is still in an early phase and will develop significantly in the coming years. However, the basics for this already exist. Digital data acquisition and the use of a manufacturing execution system offer great potential for process and production optimization as well as for sustainable cost reduction and resource conservation. Pilot and sub-projects offer the opportunity to build up elementary knowledge and to get to know and optimize the functionality of the AI systems. Simply put, artificial intelligence is a system of systems, a strongly networked interaction of all processes in production and thus a highly complex structure that aims for an autonomous, situationally self-controlling and configuring, database and knowledge-based as well as sensor-supported control.
The fourth industrial revolution is in full swing and more and more companies are relying on the potential of digital, smart and, in some cases, AI-based production control. Artificial intelligence is more than just a digital helper - in-depth production and data analysis is only made possible by AI systems. That is a success factor.
Burkhard Röhrig is the managing director of Gfos.
Also read: Digitally strengthened from the crisis: This is how companies are now driving digitization projects forward
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