When it comes to the future trend of artificial intelligence (AI), Germany is lagging well behind nations such as China and the USA. This was the finding of a recent study by the World Intellectual Property Organization (WIPO) of the United Nations. This development is being driven by the concern of many companies in Germany that the use of artificial intelligence-based technologies is too complex, expensive or difficult for them. Omron wants to alleviate these concerns and has put together a guide on how manufacturing companies and SMEs can get started with AI in five simple steps.

The discussion about artificial intelligence in the factory is currently gathering pace due to ever-increasing computing power, growing data volumes and the increased use of sensors. Adaptive algorithms offer enormous potential in the context of the further developments required by Industry 4.0, such as predictive maintenance and networked production. In this context, AI can help to increase overall equipment effectiveness (OEE), thereby reducing costs and increasing productivity.

High demands on infrastructure and IT
The problem: many of the often cloud-based AI solutions advertised on the market place enormous demands on infrastructure and IT. What's more, these solutions work with a huge amount of data that is laborious to prepare and process. In addition, system concepts for mechanical engineering are often complex and specially tailored to the respective requirements. Reliable use of typical AI algorithms is only possible through extensive testing, constant optimization and often over-dimensioning - a major effort that many shy away from.

Omron advises: Don't be afraid! There are already AI solutions that are well suited for use in industry. These include open source software and applications that rely on machine learning. Robotics and automation providers such as Omron are currently developing AI integrators that support small and medium-sized companies in particular in using artificial intelligence practically and efficiently.

The following tips will help you get started with AI:

Expanding data expertise
Manufacturing companies are often rather cautious when it comes to new technologies. This is because they work with machines that have to run for 20 years or more. But that doesn't mean they have to be left behind when it comes to AI. It's time for them to overcome their shyness and take a closer look at the opportunities offered by innovative technologies. To make the most of these opportunities, companies should ensure that they can work with big data and advanced algorithms - the two cornerstones of artificial intelligence. Both company managers and employees have a duty to educate themselves in this regard.

Clarify strategic questions
The key questions at the start of an AI project are: What challenge should be tackled? Which strategy and technology are best suited and are they adaptable? Which managers and employees should be brought on board? Is the necessary expertise available within the company or do external experts need to be involved? How can a new machine with an integrated data science approach be planned and implemented?

Measurable improvement in OEE
The primary goal of using AI is to increase quality and process efficiency, for example through improved predictive maintenance to avoid machine downtime. The AI-based solution should therefore aim to achieve measurable and tangible improvements in OEE. Even an optimization of just a few percentage points can lead to significant increases in efficiency and cost reductions. AI in machine maintenance helps to reduce the risk of equipment damage and downtime: Problems can be detected at an early stage and immediate action can be taken to rectify them. Without automation, machine developers and operators would have to create their own analysis and optimization solutions or use costly cloud solutions.

Relying on "AI at the Edge"
Instead of laboriously searching through a huge amount of data for patterns, a technology is needed that approaches things differently: Ideally, the required algorithms are integrated into the machine control system and thus create the framework for real-time optimization "at the Edge" (at machine level). Production lines and machines are monitored with real-time sensors, the data is collected and checked for anomalies. No internet connection is required and IoT protocols are reliably integrated. Companies are no longer dependent on cloud computing. One example: Edge computing is used to analyze individual production lines or locations with limited computing power.

However, with its AI controller integrated into the Sysmac platform, Omron's AI controller has an adaptive intelligence that is closer to the action. It also learns to distinguish between normal and abnormal patterns. The AI controller is a complete solution for factory automation with modules for control, motion and robotics, image processing and machine safety. It is suitable for companies of various industries and sizes.

Easy implementation
The AI solution should also be easy and quick to implement. With "AI at the Edge", production line control functions are combined with AI-based data processing in real time. Companies can recognize unforeseen situations reliably and always up-to-date and react quickly. This technology also makes it possible to increase quality, improve maintenance cycles and the life cycle of machines and scale up as required. The processes gain intelligence based on previous findings and improvements. They also drive the holistic optimization of the entire manufacturing process.

With Omron's AI controller, the raw data is recorded fully automatically. In addition, the controller automatically creates data models from correlation analyses and monitors the machine status based on these models. The hardware is based on the Sysmac NY5 IPC and the NX7 CPU. It uses Omron AI components and a comprehensive library of pre-programmed function blocks for predictive maintenance. These were developed and created based on typical applications. as