AllCloud’s new study “Machine Learning in Production” examines the potential of machine learning in manufacturing and production. According to this, almost every second company plans to optimise ML systems.
AllCloud, the provider of professional data analytics and machine learning services, has published the study ” Machine Learning in Production. ” The study clearly shows that manufacturing companies have recognized the great potential of machine learning in manufacturing and production. Almost every second company plans to optimise the ML systems used. Forty-one percent want to increase the use further and expand it to other areas. There are many reasons for this: ML models, for example, can significantly impact a company’s success and can be crucial for strategic orientation.
The main application areas are quality assurance and control (at 37 percent of the companies surveyed) and logistics and inventory expansion (at 25 percent). In addition, the optimization of the production process (at 24 percent) and predictive maintenance (also at 24 percent). The benefits of using ML are immense. A significant benefit is cost savings, which occurred with 45 percent of those surveyed. 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 relief for employees through machine learning.
The study also shows that companies‘ ambitious plans cannot be implemented without the help of implementation partners. Only two percent of production companies can independently implement their plans in ML technologies. Accordingly, 98 percent of those surveyed stated they depended on external service providers. This need is due to the lack of expertise in companies in the area of ML models and tools and the lack of skilled workers. The companies surveyed lack experts who know how to use the application fields and ML systems’ potential. And in this way, we can tap into it for the company. External service providers are also indispensable for technical requirements and the development of individual ML strategies.
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