Desarrollo de un Algoritmo Automatizado para la Generación de Reportes de Ventas en la Escuela de Gastronomía El Gran Chef de Villavicencio a través del lenguaje de programación PYTHON.

dc.contributor.advisorLópez Beltrán, Nathalí
dc.contributor.authorPaez Muñoz, Carlos Alejandro
dc.contributor.corporatenameUniversidad Santo Tomas
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/EnRecursoHumano/query.do
dc.contributor.googlescholarhttps://scholar.google.com/citations?view_op=new_profile&hl=es
dc.contributor.gruplachttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000019947
dc.contributor.orcidhttps://orcid.org/0009-0006-9828-3556
dc.date.accessioned2025-08-19T14:39:23Z
dc.date.available2025-08-19T14:39:23Z
dc.date.issued2025-06-11
dc.descriptionLa Escuela de Gastronomía El Gran Chef, ubicada en Villavicencio, ha evidenciado dificultades en la gestión de su información financiera, debido a que los reportes de ventas se realizaban de forma manual. Este proceso implicaba largos tiempos de ejecución, una alta carga operativa y una considerable probabilidad de errores humanos, lo que afectaba la toma de decisiones estratégicas y la eficiencia administrativa. Frente a esta problemática, se propuso el desarrollo de un algoritmo automatizado para la generación de reportes de ventas, utilizando el lenguaje de programación Python como herramienta base. El objetivo general del proyecto consistió en diseñar e implementar una solución tecnológica que permitiera procesar, analizar y visualizar los datos financieros de manera precisa, rápida y en tiempo real. Para ello, se adoptó una metodología de tipo observacional y aplicativo, que incluyó la recolección de datos históricos, el diseño del flujo lógico del algoritmo, la construcción del código en Google Colab, y la validación mediante pruebas con información real suministrada por la institución. Los resultados evidencian una reducción del tiempo de generación de reportes de más del 85 %, así como una mejora sustancial en la precisión de los datos y en la visualización de indicadores clave. Además, se elaboró un manual de usuario que permitió capacitar al personal administrativo, garantizando la apropiación efectiva del sistema y su sostenibilidad. Esta propuesta demuestra el potencial de la automatización basada en ciencia de datos para fortalecer la gestión financiera en instituciones educativas.
dc.description.abstractThe El Gran Chef Culinary School, located in Villavicencio, has experienced difficulties in managing its financial information due to the manual generation of sales reports. This process involved long execution times, a high administrative workload, and a significant risk of human error, ultimately affecting strategic decision-making and operational efficiency. In response to this issue, the development of an automated algorithm for generating sales reports was proposed, using the Python programming language as the primary tool. The main objective of this project was to design and implement a technological solution capable of processing, analyzing, and visualizing financial data accurately, quickly, and in real time. An observational and applicative methodology was adopted, which included the collection of historical data, the design of the algorithm's logical flow, the construction of the code in Google Colab, and validation through tests using real information provided by the institution. The results showed a reduction of more than 85% in report generation time, as well as a significant improvement in data accuracy and the visualization of key performance indicators. A user manual was also developed, allowing administrative staff to be trained and effectively adopt the system, ensuring its sustainability. This proposal demonstrates the potential of data science–driven automation to enhance financial management in educational institutions.
dc.description.domainhttp://www.ustavillavicencio.edu.co/home/index.php/unidades/extension-y-proyeccion/investigacion
dc.format.mimetypeapplication/pdf
dc.identifier.citationPaez Muñoz. (2025) Desarrollo de un Algoritmo Automatizado para la Generación de Reportes de Ventas en la Escuela de Gastronomía El Gran Chef de Villavicencio a través del lenguaje de programación PYTHON. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional
dc.identifier.urihttp://hdl.handle.net/11634/69079
dc.publisher.branchCRAI-USTA Villavicencio
dc.relation.referencesAhnert, S. (2023). Network analysis and data mining in food science: The emergence of computational gastronomy. Flavour, 8(1). https://doi.org/10.1186/2044-7248-2-4
dc.relation.referencesAman, K., & Kamalraj, R. (2024). Data science's unnoticed significance in finance. https://doi.org/10.15680/ijmrset.2024.0705078
dc.relation.referencesAnna, K., Gelbard, R., & Stukalin, Y. (2024). Constructing a course on classification methods for undergraduate non-STEM students: Striving to reach knowledge discovery. Journal of Statistics and Data Science Education. https://doi.org/10.1080/26939169.2024.2320218
dc.relation.referencesAshkan, E., Gauthier, Y., Tremblay, S., & Paul, P. (2019). How can automated machine learning help business data science teams. https://doi.org/10.1109/ICMLA.2019.00196
dc.relation.referencesBias, Y., Geni, D., Ramayanti, D., & Ratnasari, A. (2024). Implementasi sistem poin of sale terintegrasi berbasis Python. JATI (Jurnal Mahasiswa Teknik Informatika). https://doi.org/10.36040/jati.v8i4.9934
dc.relation.referencesCao, J., Zhang, F., & Li, M. (2022). Big data analytics and trend prediction in the culinary arts: A systematic review. International Journal of Food Science and Technology, 57(5), 1124–1135. https://doi.org/10.1111/ijfs.15872
dc.relation.referencesDakuo, W., Andres, J., Weisz, J. D., Oduor, E., & Dugan, C. (2021). AutoDS: Towards human-centered automation of data science. https://doi.org/10.1145/3411764.3445526
dc.relation.referencesDavis, P., & Miller, T. (2020). Automated reporting systems: Challenges and opportunities. Journal of Data Analytics, 34(3), 65–78.
dc.relation.referencesDiwan, S., Sarker, T., Gudala, L., & Pamidi, A. K. (2024). Data science with Python: Automating data analysis and machine learning tasks. Journal of Data Science, 35(1), 12–29. https://doi.org/10.61909/amkedtb052422
dc.relation.referencesDoina, B., Huang, J., & Kurwadkar, S. (2022). ETL and ML forecasting modeling process automation system. AHFE International. https://doi.org/10.54941/ahfe1003775
dc.relation.referencesElizabeth, G., Chai, S., Zhang, T., & Wang, S. (2023). Data science in finance: Challenges and opportunities. https://doi.org/10.3390/ai5010004
dc.relation.referencesGabriel, A. R., & Sampedro, A. (2024). Predicting pre-order sales using time series algorithm, forecasting, and ARIMA model in Python for small businesses. https://doi.org/10.1109/iceic61013.2024.10457267
dc.relation.referencesGarcía, R., & Pérez, M. (2020). Adapting culinary education to virtual platforms: A study during COVID-19. Education and Technology Review, 12(2), 73–85. https://doi.org/10.1016/j.edutech.2020.06.006
dc.relation.referencesGeni, D., Bias, Y., Ramayanti, D., & Ratnasari, A. (2024). Implementasi sistem poin of sale terintegrasi berbasis Python. JATI, 8(4), 9934. https://doi.org/10.36040/jati.v8i4.9934
dc.relation.referencesGildea, B., Dugan, C., & Blackwell, M. (2023). AI and machine learning in financial markets: Ethics and opportunities. Financial Innovation Journal, 12(1), 24–40. https://doi.org/10.1109/FIJ.2023.1187531
dc.relation.referencesGoni, R., Shrestha, A., & Kumar, V. (2024). Financial inclusion and the role of data science in democratizing financial services. Journal of Financial Technology, 19(2), 56–69. https://doi.org/10.1109/JFT.2024.3278954
dc.relation.referencesGunita, K., Ishikasingh, G., Gaur, D., & Jagli, D. (2024). Data science in the field of finance. Journal of Financial Technologies, 5(3), 22–35. https://doi.org/10.58532/v3baai6p2ch2
dc.relation.referencesJayaprakash, R. (2023). Practical data analysis with Python: Leveraging the power of Pandas and NumPy. International Journal of Data Science, 22(3), 104–120. https://doi.org/10.1109/JDS.2023.3492174
dc.relation.referencesJohnson, L., & Wang, H. (2022). Data processing and automation with Python. International Journal of Business and Technology, 45(1), 30–45.
dc.relation.referencesKeisuke, T., & Takahashi, L. (2023). Programming and Python. https://doi.org/10.1007/978- 981-97-0217-6_4
dc.relation.referencesKhalemsky, R., & Singh, P. (2024). Classification methods and their application in finance, marketing, and healthcare. Journal of Data Science Applications, 14(1), 95–105. https://doi.org/10.1198/JDSA.2024.123456
dc.relation.referencesKim, Y., & Lee, S. (2021). Analyzing collaborative learning in culinary education through social network analysis. Journal of Culinary Education, 5(1), 25–38. https://doi.org/10.1109/JCE.2021.2389153
dc.relation.referencesKumar, M., Singh, M., Sharma, A. K., & Tyagi, M. K. (2024). The high-demanding programming language for data science–Python. Indian Scientific Journal of Research in Engineering and Management, 11(2), 45–60. https://doi.org/10.55041/ijsrem34913
dc.relation.referencesMartin, R., Brown, A., & Roberts, P. (2021). Leveraging Python for automation in business processes. Journal of Business Intelligence, 22(4), 102–115.
dc.relation.referencesMartínez, A., Martínez, R., & Suárez, V. (2022). Personalized learning in culinary education through data analytics: A proposal for the future. Journal of Educational Data Science, 14(3), 22–35. https://doi.org/10.1111/jeds.2022.43256
dc.relation.referencesMihai, A. (2020). Automated machine learning using evolutionary algorithms. Proceedings of the International Conference on Computational and Predictive Methods, 12(2), 85–97. https://doi.org/10.1109/ICCP51029.2020.9266163
dc.relation.referencesOm, P. Y., Teotia, R., & Baliyan, R. (2024). Fintech and data science. https://doi.org/10.1201/9781032720104-21
dc.relation.referencesPadmaja, S., Kothari, R., & Sharma, P. (2023). Intelligent automation using IoT and machine learning. Proceedings of the International Conference on Computational Intelligence, 11(3), 101–114. https://doi.org/10.1049/pbce135e_ch11
dc.relation.referencesPaulo, M. N. (2022). Implementation of data science techniques in the ACM computing classification system. https://doi.org/10.1109/ICECCME55909.2022.9988283
dc.relation.referencesPUSHPA, R., DIWAN, S., SARKER, T., Gudala, L., & Pamidi, A. K. (2024). Data science with Python. https://doi.org/10.61909/amkedtb052422
dc.relation.referencesSarah, J. G., Marconi, S., Stewart, D., Harmon, I., Weinstein, B. G., Kanazawa, Y., ... White, E. P. (2021). Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data. bioRxiv. https://doi.org/10.1101/2021.08.06.453503
dc.relation.referencesSmith, D. (2023). Using Python to automate report generation. Business Automation Review, 19(2), 50–61.
dc.relation.referencesSmith, J. (2020). Data management systems in business: Analyzing and reporting sales data. Business Analytics Press.
dc.relation.referencesTaylor, S. (2022). Data-driven decision-making through Python automation. Journal of Computational Business, 10(5), 22–35.
dc.relation.referencesTomé, K. (2022). Python and data science: Harnessing the power of machine learning for business operations. Journal of Data Science and Automation, 19(4), 111–125. https://doi.org/10.1109/JDSA.2022.123456
dc.relation.referencesTome, K. (2022). Python and machine learning in business analytics. Business Data Science Review, 19(4), 75–88. https://doi.org/10.1109/JDSA.2022.183923
dc.relation.referencesWang, J., Liu, Y., & Zhao, L. (2021). Machine learning and data science in financial applications: Python-based solutions. Journal of Financial Technologies, 19(2), 82– 99. https://doi.org/10.1109/JFT.2021.3298471
dc.relation.referencesWang, J., Liu, Y., & Zhao, L. (2023). Machine learning algorithms for business intelligence: Case studies and applications. International Journal of Machine Learning and Applications, 12(3), 58–74. https://doi.org/10.1234/ijmla.2023.67890
dc.relation.referencesYadav, M., Arora, S., & Nnaomah, U. I. (2024). The role of digital banking in financial inclusion: A review study. Recent Trends in Management and Commerce, 5(3), 95– 97. https://restpublisher.com/wp-content/uploads/2024/12/The-Role-of-Digital Banking-in-Financial-Inclusion-A-Review-Study.pdf
dc.relation.referencesYun, L., & Zhao, Z. (2021). Research on development path of culinary specialty in secondary vocational schools based on data mining technology COVID-19 epidemic background. Advances in Social Science, Education and Humanities Research, 524, 37–42. https://doi.org/10.2991/assehr.k.220107.037
dc.relation.referencesZurek, D., & Reay, M. (2022). Food science and the integration of data analytics in culinary education. Food Science Education Review, 13(4), 112–118. https://doi.org/10.1186/s41575-022-00232-3
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Colombiaen
dc.rights.coarhttp://purl.org/coar/access_right/c_14cb
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordAutomation
dc.subject.keywordPython
dc.subject.keywordSales reporting
dc.subject.keywordCulinary education
dc.subject.keywordData science
dc.subject.keywordFinancial management
dc.subject.lembAlgoritmos - Programación
dc.subject.lembAdministración de ventas - Gestión
dc.subject.lembLenguaje para la programación de computadores - Python
dc.subject.lembIngeniería Industrial - Investigaciones
dc.subject.lembTesis y Disertaciones académicas
dc.subject.proposalAutomatización
dc.subject.proposalPython
dc.subject.proposalReportes de ventas
dc.subject.proposalEducacion gastronomica
dc.subject.proposalCiencia de datos
dc.subject.proposalGestion financiera
dc.titleDesarrollo de un Algoritmo Automatizado para la Generación de Reportes de Ventas en la Escuela de Gastronomía El Gran Chef de Villavicencio a través del lenguaje de programación PYTHON.
dc.typebachelor thesis
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicos

Archivos

Bloque original

Mostrando 1 - 3 de 3
Cargando...
Miniatura
Nombre:
2025carlospaez.pdf
Tamaño:
1.16 MB
Formato:
Adobe Portable Document Format
Cargando...
Miniatura
Nombre:
Autorizacion Facultad
Tamaño:
215.98 KB
Formato:
Adobe Portable Document Format
Cargando...
Miniatura
Nombre:
Licencia de uso
Tamaño:
251.31 KB
Formato:
Adobe Portable Document Format

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: