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Title: | Analytical methods for logistic tenders |
Author: | Álvarez Benítez, Cristina |
Director: | Juan, Angel A. |
Tutor: | García-Villoria, Alberto |
Abstract: | The concept of reverse auction or tender is gaining importance in the logistics and transportation industry. The number of services contracted through this process is increasing, and many provider companies need to provide competitive prices to gain them. A first part of the approach details different projects to improve organizationally the tender process. A second part of the approach presents the complete structure of the Smart Tenders Management with Artificial Intelligence project, which is divided in four stages; (1) Automation of Transport Cost Analysis, (2) Predicting Cost Evolution and Establishing Long-term Tariffs, (3) Modelling Success Probabilities in Tenders and (4) Optimization of the Tenders Portfolio. The main goal of this project focuses on the second stage of the Smart Tenders Management with Artificial Intelligence project. That is, the development of a methodology with few parameters (alpha, beta and gamma), which are automatically adjusted, based on time series and more concretely Winters¿ Method. It allows to calculate low-risk forecasted intervals, giving the decision-maker information regarding which is the most adequate long-term tariff to offer in the tender. For the experimentation with RStudio, ocean freight rates from Rotterdam to Shanghai during 7 years are used, from 2012 to 2018. Finally, after reading the data, fitting and testing the model, the calculation for the low-risk interval for the data used is obtained. |
Keywords: | logistic service provider tender management forecast time series analysis |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 15-Sep-2019 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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calvarezbenTFM0919memory.pdf | Memory of TFM | 2,57 MB | Adobe PDF | View/Open |
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