The purpose of this paper is to study an Industry 4.0 scenario of 'technical assistance' and use manufacturing execution systems (MES) to address the need for easy information extraction on the shop floor. We identify specific requirements for a user-friendly MES interface to develop (and test) an approach for technical assistance and introduce a chatbot with a prediction system as an interface layer for MES. The chatbot is aimed at production coordination by assisting the shop floor workforce and learn from their inputs, thus acting as an intelligent assistant. We programmed a prototype chatbot as a proof of concept, where the new interface layer provided live updates related to production in natural language and added predictive power to MES. The results indicate that the chatbot interface for MES is beneficial to the shop floor workforce and provides easy information extraction, compared to the traditional search techniques. The paper contributes to the manufacturing information systems field and demonstrates a human-AI collaboration system in a factory. In particular, this paper recommends the manner in which MES based technical assistance systems can be developed for the purpose of easy information retrieval.

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In recent years, the demand for digitalization, automation, and smart systems in the airline industry has accelerated. Furthermore, due to the ongoing global pandemic as of 2022, airlines are faced with the challenge of offering flexibility in both cargo and passenger capacity. Studies show that the use of smart products and autonomous agents are expected to play a key part in the digital transformation of the logistics industry. This paper aims to examine the current state-of-the-art in multi-agent systems and reinforcement learning with special interest in intelligent baggage handling systems. How to simplify, implement and simulate a system of autonomous baggage carts as a software model in order to examine congestion situations will be the main topics of this paper. Furthermore, how the findings from the software model may be applied to real-world scenarios related to Industry 4.0 and baggage handling will also be discussed.