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Genre/Form: | Thèses et écrits académiques |
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Material Type: | Document, Thesis/dissertation, Internet resource |
Document Type: | Internet Resource, Computer File |
All Authors / Contributors: |
David Arturo Pardo Melo; Yannick Frein; Bernard Penz; Khaled Hadj Hamou; Sylvie Norre; Mustapha Sali; Van-Dat Cung; Université Grenoble Alpes (2020-....).; École doctorale Ingénierie - matériaux mécanique énergétique environnement procédés production (Grenoble).; Sciences pour la conception, l'optimisation et la production (Grenoble). |
OCLC Number: | 1264288064 |
Notes: | Titre provenant de l'écran-titre. |
Description: | 1 online resource |
Responsibility: | David Arturo Pardo Melo ; sous la direction de Yannick Frein et de Bernard Penz. |
Abstract:
The aeronautic industry is characterized by small production rates compared to others industries as the automotive industry for example. Due to small production volumes, manufacturers are often in a weak position in relation to their suppliers (they represent an insignificant part of the turnover of their suppliers) in this industry. This results in a constraining supply chain network configuration. Furthermore, in several cases in the aeronautic industry, suppliers manage inbound transportation, and there's no visibility over transportation operations. Taking into account these characteristics, the objective of this Ph.D. was to develop optimization and modelling tools that support inbound Supply Chain Network Design (SCND) by minimizing total cost and reducing CO2 emissions in the aeronautic industry and industries with similar characteristics. Airbus Helicopters (AH) is a division of Airbus, a leader in the aeronautic industry. In the last years, the launching of a new range of helicopters at AH has been the occasion to innovate inbound supply chain design process. Taking into account the Airbus transformation context and the objective of this study, we conducted a case study at AH. First of all, we analysed the current AH supply chain. This analysis allowed us to identify 4 optimization axes for our case: transportation lot sizing, transportation mode selection, cross-dock location and milk run concept. In the first part of the Ph.D. we developed an optimization model per optimization axis, with the aim of minimizing total costs, without including explicitly CO2 emissions. Using these models we built an optimized inbound supply chain solution for AH. As a result, by optimizing transportation lot sizes, extending the possibility of using sea freight for all the North America suppliers, implementing a cross-docking facility at Toulouse and implementing a cross-docking facility New York with multimodal road-sea transportation from there to Airbus, total cost and CO2 emissions could be reduced by 46% and 47% respectively. In the last part of this study, we included explicitly CO2 emissions in our cross-dock location model, we studied North American suppliers' relocation, electric transportation and drone's delivery. As a result, by implementing a cross-docking facility at New-York, and a cross-docking facility at Paris with multimodal rail-road transportation from there to Airbus, cost and CO2 emissions could be reduced by 41% and 56% respectively. By relocating North American suppliers in France, and implementing the cross-docking facility at Paris with multimodal rail-road transportations, cost and CO2 emissions could be reduced by 44% and 63% respectively. Finally based on literature review, we showed that the electrification of the road fleet of Airbus in the future would reduce significantly road CO2 emissions (at least 30% reduction in the case of hydrogen fuel cells) caused by fuel combustion and would eliminate them in the case of battery electric light duty vehicles. Regarding drones delivery, this solution could reduce CO2 emissions by 0,005% per year. However, this reduction is not significant compared to the implementation effort required.
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