Purpose, originality and value: the main aim of this dissertation is to shed the light on the relationship between Supply Chain Finance (SCF) and Artificial Intelligence (AI), by addressing how AI can support the activities contained in the SCF process and what are the consequent benefits sought and challenges faced for buyers, suppliers and technology providers. Design, methodology and approach: the Preliminary Research Framework was created after a detailed literature review, that allowed to increase knowledge about the two topics and identify the existing gaps. Thanks to the case studies approach and to data triangulation of both primary and secondary data, it has been developed a Conceptual Framework answering to the questions posed by the authors. Findings: the dissertation provides numerous results. First, (1) it recognizes the activities that can be supported by AI along the whole SCF process and the (2) consequent benefits sought and challenges faced by buyers, suppliers, technology providers. In addition, it has been identified which AI solutions classes have an impact on (3) the SCF process, (4) benefits and (5) challenges, which SCF solutions can be supported by AI (6), and finally the benefits associated to the activities supported by AI in the SCF process (7). Practical and managerial implications: the research allowed to obtain significant theoretical contributions about an almost unexplored topic but is also a valuable tool for practitioners and real-world companies. Technology providers can assess which of their services can be enhanced, while buyers and suppliers find out their existing areas of improvement. Moreover, all the actors can rely on a guideline about potential benefits generated and challenges that might encounter. Limits and future research: a first limitation concerns the fact that mainly technology providers have been interviewed, being the actual enabler of AI in SCF domain. Moreover, the financial institutions’ point of view has not been taken in consideration. To conclude, this research has to be taken as a starting point for further studies on this topic, due to the innovative nature of the subject.
Scopo originalità e valore: l’obiettivo principale di questa tesi è far luce sulla relazione tra Supply Chain Finance (SCF) e Intelligenza Artificiale (IA), affrontando come l'IA può supportare le attività contenute nel processo di SCF e quali sono i conseguenti benefici generati e le sfide affrontate per i buyers, suppliers e technology providers. Design, metodologia e approccio: il Framework di Ricerca Preliminare è stato creato dopo una dettagliata revisione della letteratura, che ha permesso di aumentare la conoscenza dei due argomenti e identificare le lacune esistenti. Grazie all'approccio di casi di studio e alla triangolazione dei dati primari e secondari, è stato sviluppato un Framework Concettuale che risponde alle domande poste dagli autori. Risultati: la tesi fornisce numerosi risultati. Innanzitutto, (1) riconosce le attività che possono essere supportate dall'IA lungo il processo di SCF e i (2) conseguenti benefici ricercati e le sfide affrontate da buyers, suppliers e technology providers. Inoltre, sono emerse quali classi di soluzioni di IA hanno un impatto su (3) il processo di SCF, (4) benefici e (5) sfide, quali soluzioni SCF possono essere supportate dall’IA (6) e infine i vantaggi associati a le attività supportate dall’ IA nel processo SCF (7). Implicazioni pratiche e gestionali: la ricerca ha permesso di ottenere significativi contributi teorici su un argomento inesplorato, ma è anche uno strumento prezioso per professionisti e aziende. I technology providers possono valutare quali servizi possono essere migliorati, mentre buyers e suppliers possono scoprire possibili aree di miglioramento. Inoltre, tutti gli attori possono fare affidamento su delle linee guida riguardanti i potenziali benefici generati e sulle sfide da affrontare. Limiti e ricerche future: una limitazione riguarda il fatto che sono stati intervistati principalmente technology providers, essendo gli abilitatori dell'IA nel SCF. Inoltre, il punto di vista delle financial institutions non è stato preso in considerazione. Per concludere, questa ricerca deve essere presa come punto di partenza per ulteriori studi su questo argomento, a causa della sua natura innovativa.
Artificial intelligence support for supply chain finance programs
ZAMBUTO, MICHELE VALENTINO;PICOZZI, FRANCESCA
2018/2019
Abstract
Purpose, originality and value: the main aim of this dissertation is to shed the light on the relationship between Supply Chain Finance (SCF) and Artificial Intelligence (AI), by addressing how AI can support the activities contained in the SCF process and what are the consequent benefits sought and challenges faced for buyers, suppliers and technology providers. Design, methodology and approach: the Preliminary Research Framework was created after a detailed literature review, that allowed to increase knowledge about the two topics and identify the existing gaps. Thanks to the case studies approach and to data triangulation of both primary and secondary data, it has been developed a Conceptual Framework answering to the questions posed by the authors. Findings: the dissertation provides numerous results. First, (1) it recognizes the activities that can be supported by AI along the whole SCF process and the (2) consequent benefits sought and challenges faced by buyers, suppliers, technology providers. In addition, it has been identified which AI solutions classes have an impact on (3) the SCF process, (4) benefits and (5) challenges, which SCF solutions can be supported by AI (6), and finally the benefits associated to the activities supported by AI in the SCF process (7). Practical and managerial implications: the research allowed to obtain significant theoretical contributions about an almost unexplored topic but is also a valuable tool for practitioners and real-world companies. Technology providers can assess which of their services can be enhanced, while buyers and suppliers find out their existing areas of improvement. Moreover, all the actors can rely on a guideline about potential benefits generated and challenges that might encounter. Limits and future research: a first limitation concerns the fact that mainly technology providers have been interviewed, being the actual enabler of AI in SCF domain. Moreover, the financial institutions’ point of view has not been taken in consideration. To conclude, this research has to be taken as a starting point for further studies on this topic, due to the innovative nature of the subject.| File | Dimensione | Formato | |
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2019_12_Picozzi_Zambuto.pdf
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https://hdl.handle.net/10589/151768