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Élément Dublin Core | Valeur | Langue |
---|---|---|
dc.contributor.author | Nacerdine, Dounia Amira | - |
dc.contributor.other | Bouchafaa, Bahia, Directeur de thèse | - |
dc.date.accessioned | 2025-02-03T14:55:20Z | - |
dc.date.available | 2025-02-03T14:55:20Z | - |
dc.date.issued | 2024 | - |
dc.identifier.other | EP00884 | - |
dc.identifier.uri | http://repository.enp.edu.dz/jspui/handle/123456789/11176 | - |
dc.description | Mémoire de Projet de Fin d’Etudes : Génie Industriel. Data Science-Intelligence Artificielle : Alger, Ecole Nationale Polytechnique : 2024 | fr_FR |
dc.description.abstract | The project focuses on optimizing cash flow management for a client company by leveraging advanced predictive analytics, including machine learning and time series forecasting models. The aim is to provide future insights into cash flow trends, particularly for accounts receivable and accounts payable. This allows for better strategic financial planning, improved liquidity management, and efficient resource allocation, ultimately enhancing the firm’s financial stability and operational efficiency. | fr_FR |
dc.language.iso | fr | fr_FR |
dc.subject | Cash Flow | fr_FR |
dc.subject | Machine Learning | fr_FR |
dc.subject | Time Series | fr_FR |
dc.subject | Accounts payable | fr_FR |
dc.subject | Accounts receivable | fr_FR |
dc.subject | PwC (PricewaterhouseCoopers) | fr_FR |
dc.title | Cash flow management optimisation using statistical and machine learning techniques : application : client company of PwC | fr_FR |
dc.type | Thesis | fr_FR |
Collection(s) : | Département Génie industriel : Data Science_Intelligence Artificielle |
Fichier(s) constituant ce document :
Fichier | Description | Taille | Format | |
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pfe.2024.DSIA.NACERDINE.Dounia_Amira.pdf | PI02724 | 4.56 MB | Adobe PDF | Voir/Ouvrir |
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