Increased Efficiency by AI-Supported Loss Reason Entries

Time plays a crucial role in modern industrial manufacturing. Employees are focused on operating machines and systems continuously and efficiently. But lost productivity presents a significant challenge, especially when it comes to quickly identifying and resolving errors. This is where the innovative application of artificial intelligence (AI) comes in: By implementing an AI-based system to predict the reasons for losses, valuable time and resources can be saved. This article highlights how algorithms trained with the help of AI can not only increase efficiency, but also contribute to the continuous improvement of equipment diagnostics. We look at the process of data annotation, the challenges in identifying the cause of losses and how AI models for equipment can be trained. Join us to discover how AI increases productivity in industrial environments by allowing employees to focus on their core tasks while machine intelligence takes care of loss reason suggestions.

Challenges in troubleshooting in industrial manufacturing

At the center of industrial production are the employees, whose main task is to operate the equipment efficiently and trouble-free. But when disruptions occur, this process comes to a halt. As long as the equipment does not yet automatically recognize the causes, a sub-task in troubleshooting is to enter the reasons for the loss, which represents a distraction from the employee’s actual core task. If the loss reason catalog is extensive, employees have to search to select the appropriate entry, which can not only lead to frustration but also cost valuable production time. However, from the management perspective, careful annotation of the machine’s time series has great significance for future analysis and decision making. These challenges highlight the need for an efficient and user-friendly solution for loss reason entry in modern industrial production.

AI-based optimization of loss reason identification

The solution to the challenges mentioned above lies in the application of artificial intelligence. By developing an AI-supported algorithm, it will be possible to significantly increase the efficiency of entering the cause of the loss. This algorithm learns from historical data and is able to intelligently identify and suggest likely causes of losses. These suggestions are displayed directly and prominently on the employees’ terminal, which eliminates the need to search in the loss reason catalog. This type of predictive information allows employees to quickly and specifically focus on the most relevant reasons for losses, thereby minimizing the duration of operational disruptions and increasing productivity.

Image: AI-powered loss reason suggestions

AI support thus transforms the process of loss reason entry from a time-consuming task to an efficient, data-driven process that both reduces the workload of employees and improves the overall efficiency of equipment support.

Training process and self-learning mechanism of AI algorithm

The key to the effectiveness of the AI-supported loss detection system lies in its specialized training process. The algorithm is trained specifically for a specific system. A fundamental requirement for this is the availability of data: at least three months of data history is required to reliably train the algorithm. The following applies: the larger the data set, the more precise the predictions will be.

Another key aspect of this system is its ability for self-improvement. If the algorithm incorrectly predicts a loss reason and the employee instead selects another reason from the full catalog, the algorithm learns from this feedback. This self-learning process allows the system to continually improve and adapt to changes or new patterns in operations. Over time, the algorithm becomes more accurate, leading to a further reduction in input time and an increase in overall productivity. This combination of targeted training and continuous learning process makes the AI ​​algorithm an intelligent tool in modern industrial manufacturing.

Productivity and employee satisfaction

AI-supported loss reason annotation offers an innovative solution to the challenges of industrial manufacturing. It allows employees to focus on their main tasks while the algorithm efficiently suggests the most likely reasons for the loss. Through algorithm training and the ability to self-improve, this approach represents a pioneering innovation that further optimizes operational processes. Companies that take advantage of this technological advancement can not only increase the efficiency of their facilities, but also improve the job satisfaction of their employees.

Have we piqued your interest? Then do not hesitate to contact us. For more information about our AI solutions and how they can increase your production efficiency, reach us at We look forward to working with you to shape the future of your industrial manufacturing.

Author: Linus Steinbeck