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Big Data Analytics applied to Logistics and Supply Chain Management: An Amazon Case Study

Marco Sperindeo

Big Data Analytics applied to Logistics and Supply Chain Management: An Amazon Case Study.

Rel. Guido Perboli, Stefano Musso. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2021


The objective of this dissertation is to present the key benefits for companies deriving from the implementation of Big Data analytics as well as a data-driven approach to business. The thesis will be based both on theoretical knowledge – achieved through literature review – and practical experience, achieved during an internship in the Business Intelligence department of Amazon where Big Data analytics were deployed for the management of Supply Chain and Logistics. The work will be structured in three sections as follows: 1-?? An Overview of Big Data Analytics This section of the thesis aims at providing an overview of the definition and history of big data analytics. Furthermore, it explores the technology and business drivers for implementing Big Data analytics within a company. 2-??Big Data Techniques and Technologies This section reviews the different technologies and techniques most widely utilized to manage and process Big Data. 3-??The Amazon Case Study This section is related to the project work involving the deployment of Big Data analytics conducted during my internship experience in Amazon Logistics and will be structured as follows: -??Introduction: How the SC management & Logistics business case and the role I covered link to big data analytics. Which elements allows to classify the datasets analyzed as big data? What were the big data tools/technologies involved and techniques applied? -??General context: Introduction to Amazon Fulfillment Network and the Inbound Cross-dock. -??Problem Statement: Definition and quantification of the optimization opportunity for what it concerns the container-breaking and sortation processes performed in IXD building: what is sub-optimal breaking? -??Solution Statement: Description of the possible solution to implement in order to eliminate the inefficiencies in the container-breaking process. -??Evaluation of the solution effectiveness using descriptive and statistical data analytics: -??Takeaways: Through the analysis of big data, it has been possible to not only define and quantify the optimization opportunity, but also to evaluate the effectiveness of the possible solution identified by conducting statistical analyses on the data to consume in input. The results of the case will pave the way for future improvements of the business process in scope. (N.B.:: the business case will not contain real figures nor real names as per the Confidentiality Agreement that was signed with Amazon) The dissertation will be concluded by observations about the future role of Big Data Analytics in large-scale businesses.

Relators: Guido Perboli, Stefano Musso
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 69
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/21571
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