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Enrico La Longa
A Comprehensive Analysis of Lanes Performance Parameters for Enhanced Transportation Planning: Lane Wise Mapping.
Rel. Elisabetta Raguseo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Della Produzione Industriale E Dell'Innovazione Tecnologica, 2023
Abstract: |
The central focus of my thesis centers on an in-depth analysis of Amazon as a company. In the first chapter, the company is analyzed both historically and socially, meaning values and mission and history, in order to better understand the type of company Amazon is. Additionally, the economic side of Amazon is analyzed as well, through a wide range of different tests: Pestel, SWOT and Porter's five forces are performed. This helps to dive deep in all aspect of Amazon business, giving an holistic vew of the company. However, the main focus of the thesis is a specific project that I had the privilege of participating in during my internship at the company. This project had a clear and impactful objective: to develop a tool using a visualization software, QuickSight, that would streamline the way stakeholders access and utilize critical parameters related to lane performance. The motivation behind this project was rooted in the need to optimize the efficiency of decision-making processes within Amazon. In the complex and fast-paced logistics and e-commerce industry, timely access to crucial data is fundamental. The overarching goal was to create a unified platform where all essential metrics and performance indicators for various shipping lanes were readily available and continually updated. By achieving this objective, our team aimed to significantly reduce the time and effort required to conduct performance analyses, thereby enhancing the overall utilization of company resources. This was particularly essential for Amazon, a company known for its dedication to operational excellence and customer satisfaction. Furthermore, the tool was designed to provide stakeholders with a comprehensive and holistic view of lane performance. This complete perspective empowered decision-makers to make more informed and effective choices regarding logistics, resource allocation, and process improvements. Through the creation of this powerful visualization tool, my internship experience at Amazon offered a firsthand glimpse into how a global leader in e-commerce continually strives to improve its operations and deliver excellence in the highly competitive marketplace. |
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Relatori: | Elisabetta Raguseo |
Anno accademico: | 2023/24 |
Tipo di pubblicazione: | Elettronica |
Numero di pagine: | 94 |
Informazioni aggiuntive: | Tesi secretata. Fulltext non presente |
Soggetti: | |
Corso di laurea: | Corso di laurea magistrale in Ingegneria Della Produzione Industriale E Dell'Innovazione Tecnologica |
Classe di laurea: | Nuovo ordinamento > Laurea magistrale > LM-33 - INGEGNERIA MECCANICA |
Aziende collaboratrici: | Amazon Luxemburg |
URI: | http://webthesis.biblio.polito.it/id/eprint/28188 |
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