Resumen:
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[EN] The planning of an Aftermarket Supply Chain is a very complex task. This is due to an unpredictable
demand which is driven by the need for maintenance and repair. This drive translates to a high
variety of lead ...[+]
[EN] The planning of an Aftermarket Supply Chain is a very complex task. This is due to an unpredictable
demand which is driven by the need for maintenance and repair. This drive translates to a high
variety of lead times, a large number of stock-keeping units (SKUs) and the capacity to deliver spare
parts during its full lifecycle. With all these complexities in place, optimizing and parametrizing the
planning process is a difficult and time-consuming task. Moreover, the current optimization tool
focuses only on one node (each warehouse individually) of the whole Supply Chain, without
considering the information such as inventory levels of the other nodes. Hence, the Supply Chain is
not completely connected, making it difficult to get a better understanding of the system performance
to identify cost draining areas. This leads to capital being tied up in the upper stream of the Supply
Chain and later adding unnecessary costs like high inventory costs, rush freight costs, return or
scrapping cost.
In this study, Discrete Event Simulation (DES) is explored as an additional optimization tool that
could analyse and improve the performance of the whole Supply Chain. To do that, the functioning of
a node is modelled by replicating the logics behind the flow of material, which includes analysing
some manual workflows which are currently present. In Addition, all the information needed from
the orders, order lines and parts are mapped. The later part of the study aims to connect all the nodes
to form a whole overview of the Supply Chain and further perform optimizations globally.
As an outcome, Multi-Echelon Inventory Optimization has been performed on the whole Supply
Chain after connecting all the nodes and thus getting an overview. Furthermore, the impact of
different parameters has been studied on the whole model to understand the sensitivity of
parameters such as variations in lead time and demand. Finally, different what-if scenarios such as
COVID and problems with delay in suppliers were studied, which could help understand the impact
of unforeseen situations.
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[ES] Investigar las oportunidades y los desafíos potenciales del uso de la simulación de eventos discretos para mejorar una red global de suministro del mercado de accesorios, así como construir y probar un modelo de ...[+]
[ES] Investigar las oportunidades y los desafíos potenciales del uso de la simulación de eventos discretos para mejorar una red global de suministro del mercado de accesorios, así como construir y probar un modelo de simulación en un subconjunto de la red para una serie de escenarios diferentes, incluida una evaluación de beneficios.
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