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To represent the relationship between products, parts, machines, resources, operators, and skills, standardized production and product\u2010relevant data are transformed into a set of bi\u2010 and multipartite networks. This representation is beneficial in production flow analysis (PFA) that is used to identify improvement opportunities by grouping similar groups of products, components, and machines. It is demonstrated that the goal\u2010oriented mapping and modularity\u2010based clustering of multilayer networks can serve as a readily applicable and interpretable decision support tool for PFA, and the analysis of the degrees and correlations of a node can identify critically important skills and resources. The applicability of the proposed methodology is demonstrated by a well\u2010documented benchmark problem of a wire\u2010harness production process. 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