Analysis, planning, optimization, and automation of manual processes within Amazon
This thesis addresses the challenge of rising operational workload in large enterprises and the difficulty of scaling traditional automation beyond pilots. From the theoretical background, it highlights the evolution toward reasoning-capable or agentic AI, which combines autonomy, adaptability, and...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/444802 |
| Acceso en línea: | https://hdl.handle.net/2117/444802 |
| Access Level: | acceso embargado |
| Palabra clave: | Artificial intelligence--Financial applications Production control -- Data processing Product management -- Automation Quality of products Intel·ligència artificial--Aplicacions a les finances Producció -- Control -- Automatització Gestió de productes -- Automatització Qualitat dels productes Àrees temàtiques de la UPC::Economia i organització d'empreses |
| Sumario: | This thesis addresses the challenge of rising operational workload in large enterprises and the difficulty of scaling traditional automation beyond pilots. From the theoretical background, it highlights the evolution toward reasoning-capable or agentic AI, which combines autonomy, adaptability, and governance to move from isolated task automation to end-to-end process integration. The project, conducted during a six-month internship at Amazon, applied these principles in practice: more than one hundred manual processes were mapped, scored by impact, priority, and complexity, and distilled into a portfolio of sixty opportunities. Pilot automations validated that agentic AI could reduce manual effort while maintaining reliability and gaining user acceptance. Building on these pilots, a three-year roadmap was developed projecting up to 80% workload automation and significant operating expenditure savings. Results align with industry benchmarks, confirming that leadership sponsorship, value frameworks, and workforce engagement are essential for success. For confidentiality reasons, process details are anonymised and all outcomes are expressed in percentages. The thesis demonstrates both the potential and the dependencies of scaling reasoning-capable AI in enterprise contexts. |
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