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Delivery models for optimizing the order volume estimation in the e-commerce industry

Andrea Buttazzoni

Delivery models for optimizing the order volume estimation in the e-commerce industry.

Rel. Giulio Mangano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale, 2021

Abstract:

Our society is subject to constant changes manly due to globalization. Over the years, markets are becoming more volatile and erratic. Indeed, 21st-century businesses give a particular example of innovation and adaptation to new trends. This phenomenon can be perceived in one industry in particular, electronic commerce, also called e-commerce, which refers to the exchange of products and services through internet. In the last decades, e-commerce has been one of the fastest-growing industry. My studies are focused on understanding the strategies that e-commerce businesses have implemented to their logistic systems and will be presented the project I developed to optimize the routing systems used in this industry. To compete in the globalized e-commerce environment, companies have to consider many factors that can impact customers to define the best logistic system and, consequently, a successful business. The dissertation will discuss the main supply chain and routing models used in the e-commerce industry. Furthermore, it will present the shortcomings of the above-mentioned models and identify exemplary strategies implemented in this field to ensure companies’ business success. This thesis is organized into five chapters, which will be presented the structure of the e-commerce business, its logistic system and the results obtained from the deployment of my project. The first chapter gives an overview of the e-commerce business whereas the second and third chapter present the supply chain and routing models used in this industry. The fourth chapter presents the project that I have been working on, and the last chapter provides conclusions and benefits for the topics studied.

Relators: Giulio Mangano
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 60
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: Amazon UK Services ltd
URI: http://webthesis.biblio.polito.it/id/eprint/17783
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