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Machine Learning on the rise

Companies have recognized the potential of machine learning. However, the necessary expertise and skilled workers are lacking.
E-3 Magazine
August 10, 2022
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This text has been automatically translated from German to English.

The study "Machine Learning in Production" by AllCloud clearly shows that companies have recognized the great potential of machine learning (ML) in the area of manufacturing and production. Almost every second production company plans to optimize the ML systems it uses. Forty-one percent want to further increase their use and expand it to other areas. There are many reasons for this: ML models, for example, can have a significant influence on a company's success and be decisive for its strategic orientation. The current main areas of application are quality assurance and control (at 37 percent of the companies surveyed), logistics and inventory expansion (at 25 percent), optimization of the production process (at 24 percent) and predictive maintenance (also at 24 percent). The benefits of using ML are immense.

What do companies need support with?
Basis: 202 companies, multiple answers possible. Source: AllCloud.

An important benefit is cost savings, which occurs for 45 percent of respondents. In addition, 42 percent state that production optimization has been achieved. Forty-one percent see an increase in productivity, 34 percent a process acceleration and 32 percent a reduction in employee workload through the use of machine learning. Only two percent of manufacturing companies can independently implement their future plans in the area of ML technologies. Accordingly, 98 percent of the respondents state that they are dependent on external service providers. This need is due to the companies' lack of expertise in ML models and tools and the shortage of skilled workers. The companies surveyed lack experts who are familiar with the use, fields of application and potential of ML systems and can develop them for the company. External service providers are also indispensable when it comes to technical requirements and the development of individual ML strategies.

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