New technological innovations in robotics, automation, and digitization enable agile manufacturing, smaller lot sizes, and quicker changes to the product and the production process. In such environments, learning is important, since workers must learn new tasks and adapt to frequent changes quickly. In an experimental study, we compare digital work instructions with traditional paper-based ones, the latter still being common in today's quality management systems. Our analysis is based on subjects working in the demonstration factory of a large German university in a realistic work environment; it, therefore, guarantees high internal and external validity. We show that digitally animated, interactive work instructions are an effective way to foster faster learning and enhanced performance when new manufacturing tasks are being carried out. Our results indicate that a combination of digital and traditional paper-based instructions does not yield any advantages over a sole reliance on digital learning.

In this paper, we report the adaptation of a named entity recognition (NER) system to the biomedical domain in order to participate in the "Shared Task Bio-Entity Recognition". The system is originally developed for German NER that shares characteristics with the biomedical task. To facilitate adaptability, the system is knowledge-poor and utilizes unlabeled data. Investigating the adaptability of the single components and the enhancements necessary, we get insights into the task of bioentity recognition.

Purpose With shortening product life cycles and an increasing number of product variants, manufacturing firms perform more production ramp-ups. In this context, learning is crucially important to quickly achieve high production process quality and stability. The paper aims to discuss this issue. Design/methodology/approach Through a laboratory experiment, this study analyzes spillover learning between consecutive ramp-ups and how this phenomenon is influenced by tacit knowledge transfer through observation and imitation. Findings The results prove the existence of spillover learning between consecutive ramp-ups. Moreover, they provide evidence how tacit knowledge transfer through observation and imitation enhances learning of new tasks in consecutive production ramp-ups. Research limitations/implications Future research could focus on the specific psychological processes driving tacit knowledge transfer and spillover learning, a topic which is only touched upon in this paper. Practical implications The findings show that manufacturing firms should not only aim at reaching a steep learning curve during a single production ramp-up, but should also take into account the effects of spillover learning with regard to future production ramp-ups. Furthermore, the paper provides novel insights concerning the allocation of workers to production tasks with regard to previous experience when introducing new personnel and during ramp-up phases. Originality/value Previous evidence on the existence and characteristics of spillover learning in production ramp-up situations is not conclusive. This paper provides new and unambiguous insights by considering different organizational settings.

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