|Theme||Production planning, Artificial Intelligence|
|Project title||Enabling SMEs to exploit the potential of machine learning in production and developing a deployment strategy (ML-Ready)|
|Project duration||01.03.2022 – 29.02.2024|
How can small and medium-sized enterprises (SMEs) in mechanical engineering be enabled to implement and use the potentials of Machine Learning to achieve an improvement in resource efficiency in production? This is what we are investigating in the ML-Ready research project together with IPRI.
Machine Learning is already being applied in many areas of everyday life, such as transportation or healthcare. The foundation for a successful application of machine learning is data and its availability as well as sufficient computing power to evaluate it. Nevertheless, Machine Learning has so far hardly been used to increase resource efficiency in production.
In the corporate environment, machine learning is mostly associated with predictive maintenance. In production, however, there are other versatile potentials for increasing resource efficiency:
- In production planning and control, Machine Learning can be used, for example, in order control for sequence determination (setup optimization, reduction of energy consumption or energy costs), for load leveling or for resource and capacity planning
- The application of machine learning for process optimization is expected to increase the adaptability of processes to changing conditions, which can stabilize product quality. For example, fault diagnostics can be performed or condition monitoring can be applied.
- In quality management, machine learning-based models can be used to monitor or predict product quality based on process data. In this way, measures such as random sample checks can be reduced.
However, when it comes to implementation, companies encounter hurdles such as lack of know-how, lack of proof of added value or lack of technical infrastructure. In the ML-Ready project, we want to support SMEs in particular in overcoming these hurdles in the future and using machine learning.
Publications about the project
Machine learning is already used in many areas of everyday life and offers far-reaching potential in production. At the same time, the efficient use of resources is becoming increasingly important due to the growing relevance of ESG. By implementing machine learning in production to increase resource efficiency, companies can become more effective and efficient while implementing ESG strategies. SMEs, in particular, face a major challenge when it comes to implementation. In addition to the high complexity of Machine Learning applications, there is often a lack of knowledge about suitable application possibilities as well as a lack of conviction about the benefits that can be derived from them. In the following article, applications of Machine Learning to increase resource efficiency along the internal supply chain as well as their potentials are discussed.
Machine Learning, production, resource efficiency
The use of machine learning has already become es-tablished and is applied in many areas of everyday life. Machine Learning is also becoming increasingly important in the field of production and logistics. However, the complex implementation poses major challenges, especially for small and medium-sized enterprises (SMEs). This leads to the fact that many SMEs refrain from using Machine Learning applications. For this reason, IPH – Institut für Integrierte Produktion and IPRI – International Performance Research Institute are working together on the research project „MLready“ to develop an implementation strategy that will enable SMEs to im-plement and use machine learning easily and efficiently.
machine learning, SMEs, production, ML implementation strategy