Coping with Aging in Manual Assembly Systems: Age-differentiated Analyses and Mathematical Modeling for Predicting the Time Structure of Sensorimotor-skill Acquisition for Assembly in Series Production with Numerous Product Variants

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The research project focuses on coping with aging in manual series assembly with numerous product variants. The research objective is to produce an age-differentiated model and forecast of the time structure of learning processes for typical assembly tasks. The research objective consists of several sub-objectives. The first step is to develop a theory-based mathematical model of the time structure of learning processes that includes both current theories of learning and further conceptual aspects. This applies both to workers' early and central processes, as well as to the mathematical formulation of the learning progress over the course of time. By expanding existing knowledge during the research project, the model will be successively validated.To this end, several age-differentiated studies will be conducted. The impact of age on learning and learning time, as well as additional factors, will be analyzed in these studies. The focus of the studies will be on empirical research on factors that have not yet been analyzed or have been analyzed as part of other technical, operational or social parameters, such as different types of work plans and different job instruction methods. In addition, the influences of the length of breaks (after each task execution) and of chronotype on learning will be part of the analyses. These influences are probably conjunct in terms of human performance.The results and knowledge obtained will be consolidated in order to derive and develop a valid prediction method for learning times. The method will provide an exact and robust estimation of the learning time for typical manual tasks in industrial series production. This will be achieved with the help of a statistical approach which has already been used successfully in my preliminary studies. A mathematical description of the learning effect has first to be identified, the parameters of which are to be estimated as part of the prediction. Additional factors - compared to the preliminary studies - will be taken into account. In addition, more detailed and more advanced statistical approaches such as vector autoregressive and periodic vector autoregressive models, whose structure can be substantiated by theory and empirical data, will be used. The predictive validity of the model developed in the project will be assessed in a final laboratory study.