A constantly increasing number of product variants, shorter product life cycles and ever faster changing factories are increasing the demands on intralogistics. This is accompanied by the need for ever more efficient transport logistics with high flexibility and scalability of the transport systems used. In practice, communication problems can already occur today, which have a negative impact on the expected logistics performance. If the number of participating AGVs is increased in the future, there will also be major scalability problems. New concepts rely on swarms of automated guided vehicles (AGVs) to improve the communication of AGVs on the one hand and to increase logistics performance on the other.
Radio communication, decentralized control, automated guided vehicles, network coding
The relevance and added value of artificial intelligence (AI) and machine learning (ML) have increased significantly in recent years. Extensive potential has emerged, particularly in the area of production. However, the high complexity of ML and the lack of evidence of its added value often mean that particularly small and medium-sized enterprises (SMEs) do not engage further with its introduction and use. For this reason, a holistic guide has been developed that accompanies manufacturing SMEs from the identification of suitable use cases and maturity level analysis through to the implementation of measures and continuous improvement processes, providing the required concepts.
Machine learning, implementation strategy, guide, production, maturity level
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
