Parts feeding is a complex logistic problem that is further complicated by the market demand for more product variety, which forces companies and manufacturers to adopt the mixed model approach in their assembly systems. Among the parts feeding policies for mixed-model assembly systems, there is the so-called "station-sequence" policy, where stationary kits are prepared using sequences of parts that follow the sequence of the production models. This policy can reduce stocks at the assembly stations but can also lead to potential production stops due to its low robustness. The aim of this paper is to study the station-sequence parts feeding policy, focusing on its dynamic time dependence and analyzing the effects of time and model mix perturbations on the performance of the assembly system. The study was conducted through a simulation model and a statistical analysis. The final discussion also provides a set of Industry 4.0 (I4.0) enabled solutions that are able to address the negative effect of variability on the performance of the system.

Modelling and Managing “Station-Sequence” Parts Feeding in the I4.0 Era: A Simulation Approach for In-Plant Logistics

Faccio M.;Granata I.;Maretto L.
2023

Abstract

Parts feeding is a complex logistic problem that is further complicated by the market demand for more product variety, which forces companies and manufacturers to adopt the mixed model approach in their assembly systems. Among the parts feeding policies for mixed-model assembly systems, there is the so-called "station-sequence" policy, where stationary kits are prepared using sequences of parts that follow the sequence of the production models. This policy can reduce stocks at the assembly stations but can also lead to potential production stops due to its low robustness. The aim of this paper is to study the station-sequence parts feeding policy, focusing on its dynamic time dependence and analyzing the effects of time and model mix perturbations on the performance of the assembly system. The study was conducted through a simulation model and a statistical analysis. The final discussion also provides a set of Industry 4.0 (I4.0) enabled solutions that are able to address the negative effect of variability on the performance of the system.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3471462
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