Neacşu (Dobrişan), G.C.; Niţu, E.L.; Gavriluţă, A.C.; Vlad, G.G.; Dobre, E.M.; Gheorghe, M.; Stan, M.M. Process Analysis and Modelling of Operator Performance in Classical and Digitalized Assembly Workstations. Processes2024, 12, 533.
Neacşu (Dobrişan), G.C.; Niţu, E.L.; Gavriluţă, A.C.; Vlad, G.G.; Dobre, E.M.; Gheorghe, M.; Stan, M.M. Process Analysis and Modelling of Operator Performance in Classical and Digitalized Assembly Workstations. Processes 2024, 12, 533.
Neacşu (Dobrişan), G.C.; Niţu, E.L.; Gavriluţă, A.C.; Vlad, G.G.; Dobre, E.M.; Gheorghe, M.; Stan, M.M. Process Analysis and Modelling of Operator Performance in Classical and Digitalized Assembly Workstations. Processes2024, 12, 533.
Neacşu (Dobrişan), G.C.; Niţu, E.L.; Gavriluţă, A.C.; Vlad, G.G.; Dobre, E.M.; Gheorghe, M.; Stan, M.M. Process Analysis and Modelling of Operator Performance in Classical and Digitalized Assembly Workstations. Processes 2024, 12, 533.
Abstract
The strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a Lean Learning Factory was developed in the Pitesti University Center, which allows students to learn, experiment, and research, in an environment similar to that of enterprises, the new Lean Manufacturing methods and techniques, and Industry 4.0. The research presented in this study had as its objective identifying the minimum number of repetitions necessary to train operators who serve two differently organized workstations, one classic, the other including digital techniques, but on which the same assembly operation is performed. Several indicators were considered in the analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percentage ratio between the standard duration of cyclical activities and the effective duration of the work cycle. The evolution of these indicators was mathematically modelled by regression functions, using the least squares method. The obtained results also highlight the usefulness of applying the DOJO method as a Lean manufacturing-specific learning technique and the efficiency of implementing digital techniques in work organization.
Keywords
lean learning factory; assembly workstations; DOJO; industry 4.0; regression analysis
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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