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Abstract In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and is processed according to the order requirements. The timely completion of orders depends on the individual station-based operations concluding within their scheduled cycle times. If an error occurs in one station, it can have a knock-on effect, resulting in delays on the downstream stations. To the best of our knowledge, there exist no methods for automatically distinguishing between source and knock-on errors in this setting, as well as establishing a causal relation between them. Utilizing real-time information about conditions collected by a production data acquisition system, we propose a novel vehicle manufacturing analysis system, which uses deep learning to establish a link between source and knock-on errors. We benchmark three sequence-to-sequence models, and introduce a novel composite time-weighted action metric for evaluating models in this context. We evaluate our framework on a real-world car production dataset recorded by Volkswagen Commercial Vehicles. Surprisingly we find that 71.68% of sequences contain either a source or knock-on error. With respect to seq2seq model training, we find that the Transformer demonstrates a better performance compared to LSTM and GRU in this domain, in particular when the prediction range with respect to the durations of future actions is increased.

A global and local discussion on Public Health relevance is taking place, including the future role and organization of its services. Noteworthy becomes the role played by Public Health Specialists. This work presents the results of a workshop, carried out following the Guilbert methodology, whose aim was to define Public Health Doctors functions and their related activities. The programme involved 30 professionals from Triveneto area (North Eastern Italy), working in Prevention Departments at National Health Service and Universities. The key-functions identified were: 1) Health status assessment and identification of community risk factors, 2) Health Promotion, 3) Prevention, 4) Protection, 5) Planning, 6) Communication, 7) Professional Training, 8) Alliances and resources for complex Public Health programs, 9) Crisis management in Public Health, 10) Research. For each function activities were identified, meaning concerning areas and contents that must be warranted by professionals. This experience allowed to share existing attitudes and experiences present in Triveneto area, and it can stand as a feasible instrument for different settings. Nevertheless, it appears mandatory explaining at each level in the society role and functions of Prevention Departments.