In the 21st century, the concept of sustainability has taken center stage in the operational strategies and core values of organizations worldwide. This paradigm shift, driven by growing awareness of the environmental impact of human activity and pressure from consumers, governments, and investors, is pushing companies to reconsider their operational practices under the lens of sustainability. Managing and reducing greenhouse gas emissions, in particular, have become key priorities. The Global Reporting Initiative (GRI) standards provide a methodological framework for the effective measurement, management and reporting of environmental performance; the GHG Protocol, then, provides direct guidance on the methodology for managing and calculating CO2 emissions. However, the complexity and volume of environmental data require advanced technological solutions that simplify the reporting process. In this context, business intelligence (BI) platforms emerge as key tools, offering advanced data management capabilities that can transform sustainability reporting. This study will focus on a twofold trajectory: on the one hand, the literature and state of the art regarding sustainability data collection and analysis will be analyzed, with a focus on the role of BI technologies. On the other, we will dive into the modeling of some specific classes of downstream and upstream logistics, making use of the GHG Protocol guidelines, in order to support the implementation of a general platform capable of managing and analyzing sustainability data and creating detailed and informative reports. This will not only illustrate the effectiveness of BI in overcoming sustainability reporting challenges, but also provide insights into how companies can use these technologies in practice to identify opportunities for strategic environmental improvement.
Nel XXI secolo, il concetto di sostenibilità ha assunto un ruolo centrale nelle strategie operative e nei valori fondamentali delle organizzazioni in tutto il mondo. Questo cambiamento paradigmatico, guidato dalla crescente consapevolezza dell'impatto ambientale dell'attività umana e dalla pressione di consumatori, governi e investitori, spinge le aziende a riconsiderare le loro pratiche operative sotto la lente della sostenibilità. La gestione e la riduzione delle emissioni di gas serra, in particolare, sono diventate priorità chiave. Gli standard Global Reporting Initiative (GRI) forniscono un quadro metodologico per la misurazione, gestione e comunicazione efficace delle performance ambientali; il GHG Protocol, poi, fornisce delle linee guida dirette sulla metodologia di gestione e calcolo delle emissioni di CO2. Tuttavia, la complessità e il volume dei dati ambientali richiedono soluzioni tecnologiche avanzate che semplifichino il processo di reporting. In questo contesto, le piattaforme di Business Intelligence (BI) emergono come strumenti fondamentali, offrendo capacità avanzate di gestione dei dati che possono trasformare il reporting di sostenibilità. Il presente studio si concentrerà su una duplice traiettoria: da un lato, verrà analizzata la letteratura e lo stato dell’arte riguardante la raccolta e l'analisi dei dati di sostenibilità, con particolare attenzione al ruolo delle tecnologie di BI. Dall'altro, ci si immergerà nella modellizzazione di alcune classi specifiche della logistica a valle e a monte, servendosi delle linee guida del GHG Protocol, al fine di supportare l’implementazione di una piattaforma generale atta a gestire e analizzare i dati di sostenibilità e creare report dettagliati e informativi. Questo non solo illustrerà l'efficacia della BI nel superare le sfide del reporting di sostenibilità, ma fornirà anche spunti su come le aziende possano utilizzare nella pratica queste tecnologie per identificare opportunità di miglioramento ambientale strategico.
MODELLIZZAZIONE DI UNA PIATTAFORMA DI BUSINESS INTELLIGENCE PER LA GESTIONE DELLE ATTIVITÀ DI REPORTING
MARGHERITI, VINCENZO GIUSEPPE
2023/2024
Abstract
In the 21st century, the concept of sustainability has taken center stage in the operational strategies and core values of organizations worldwide. This paradigm shift, driven by growing awareness of the environmental impact of human activity and pressure from consumers, governments, and investors, is pushing companies to reconsider their operational practices under the lens of sustainability. Managing and reducing greenhouse gas emissions, in particular, have become key priorities. The Global Reporting Initiative (GRI) standards provide a methodological framework for the effective measurement, management and reporting of environmental performance; the GHG Protocol, then, provides direct guidance on the methodology for managing and calculating CO2 emissions. However, the complexity and volume of environmental data require advanced technological solutions that simplify the reporting process. In this context, business intelligence (BI) platforms emerge as key tools, offering advanced data management capabilities that can transform sustainability reporting. This study will focus on a twofold trajectory: on the one hand, the literature and state of the art regarding sustainability data collection and analysis will be analyzed, with a focus on the role of BI technologies. On the other, we will dive into the modeling of some specific classes of downstream and upstream logistics, making use of the GHG Protocol guidelines, in order to support the implementation of a general platform capable of managing and analyzing sustainability data and creating detailed and informative reports. This will not only illustrate the effectiveness of BI in overcoming sustainability reporting challenges, but also provide insights into how companies can use these technologies in practice to identify opportunities for strategic environmental improvement.| File | Dimensione | Formato | |
|---|---|---|---|
|
MARGHERITI_tesi.pdf
File consultabile da postazioni biblioteca
Dimensione
5.52 MB
Formato
Adobe PDF
|
5.52 MB | Adobe PDF |
I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/20.500.14253/68801