Don't be afraid of artificial intelligence: intelligent data analysis in production

In this article, we outline the areas in which AI can help to speed up processes on the shopfloor, increase reliability and quality and improve energy management in production. What requirements do companies need to fulfill in order to implement AI applications and what factors are hindering the use of artificial intelligence?

In principle, artificial intelligence algorithms work like the human brain. However, they have a much higher "computing power" and can therefore solve various tasks in production faster and better than humans.

• Field of action 1: Commissioning of machines and order processing

AI-based software helps customers in the mechanical and plant engineering sector to commission production machines and process orders. Various processes that guide employees through commissioning can be derived step by step from a digital representation of the production environment, the digital twin. In this way, AI also guides the workers through the entire order processing process, including set-up instructions and inspections. Because the software learns from all events on the shopfloor - assuming they are recorded digitally - companies optimize their production processes over time and can eliminate the causes of errors. Manufacturing companies not only become faster, but also improve their quality.

• Field of action 2: Increase productivity with predictive maintenance

The market already offers very sophisticated solutions for predictive maintenance. By analyzing machine data and deriving forecasts from it, AI enables employees in production to recognize at an early stage when the machine needs maintenance, for example when wearing parts need to be replaced. Regular maintenance intervals can also be adjusted based on algorithms. Companies reduce their costs if they increase the intervals without risking downtime. Anomalies are detected long before damage occurs and unplanned downtimes can be largely prevented. This enables companies to increase their productivity and machine availability.

• Field of action 3: Check and ensure quality better and with less effort

Quality inspection is indispensable in production. However, it is time-consuming and does not always deliver the desired results with conventional methods. AI expands the scope of action in quality assurance. Without having to damage the product, it can be checked for defects and production errors using visual inspection. Employees can use visual inspections to detect scratches, cracks, casting errors, incorrect positioning of parts and other defects.

It is now possible to carry out camera-supported visual inspections automatically. The software detects the slightest deviations from the good and bad images with which the system has been trained in real time and analyzes the causes. For example, it could be that employees regularly do not carry out a certain work step gently enough. By reporting the error to the employee, the software prompts them to rectify it. The employee learns how to carry out the work step correctly. At the same time, costs are reduced because the errors are not only detected during quality control of the finished part. As the overall product quality increases through the evaluation of the AI, customers have less cause for complaint - an important contribution to customer loyalty and cost reduction. The automated process increases productivity by reducing waste. In addition, skilled workers gain capacity for other demanding tasks.

• Field of action 4: Energy management

Companies are required to use energy sustainably. In doing so, they not only reduce their costs, but also align their processes with ESG goals, where ESG stands for environment, social and governance. With the sustainability reporting obligation and other requirements, legislators are setting standards that businesses must meet. It is therefore advisable to integrate energy management into the production environment. As there is often a lack of awareness of energy efficiency on the shopfloor, comprehensive, self-optimizing systems help to achieve a better energy balance. They often enable energy guzzlers and inefficient behavior to be identified and eliminated for the first time.

AI-based software offers a range of opportunities to improve manufacturing processes, increase efficiency and retain customers through high product quality. These steps will help you prepare for the introduction:

• Check how digital you already are and where you can further prepare manufacturing for the use of AI: What data do you already collect as standard? What does the hardware equipment with sensors, cameras and so on look like? Do you integrate the data collected on the shopfloor into the ERP and MES system? Are your employees connected to a connected worker platform via mobile devices?

• Identify a suitable field of action: an AI project needs to be well prepared and ties up personnel and IT capacities. Check in which area the greatest effect can be expected or where you want to improve.

• Prepare the introduction well: Scout the market and check whether you meet the data quality requirements of the respective provider. Take your time and only start a project once you have done your digital "homework". Also pay attention to the integration of the data into the business software. If necessary, hire specialist staff with AI skills and plan for sufficient capacity in your IT department.

• Take your employees with you: Involve your colleagues on the shopfloor in the planning. Take the time to explain the benefits of the planned project and allay any fears and concerns. Make sure that all employees are familiar with the new processes, understand the purpose of the project and are willing to support the change.