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Pronóstico de ventas a través de una aplicación web aplicando machine learning y variables exógenas

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Ferro Rugeles, Rubén Darío
Cossio Escobar, Gonzalo
Fernandéz Moncada, Nicolas
Abstract (Spanish)
Actualmente, el entorno empresarial y organizacional, se enfrenta a una problemática generalizada que trasciende sectores: la gestión ineficiente de los pronósticos de ventas y su falta de implementación. La realización de las predicciones de ventas, dentro de las organizaciones, se realiza de manera manual y utilizando métodos obsoletos, lo cual consume recursos valiosos, tanto en tiempo como en dinero, con consecuencias económicas bastante graves. Se habla de cifras impactantes, cuyos valores oscilan entre 568 millones de pesos a nivel de una sola organización. Estas pérdidas, representan desafíos reales para las empresas, ya que estos recursos podrían destinarse a la expansión, innovación e inversión en otros aspectos cruciales del negocio aumentando su crecimiento. En este contexto, la precisión en las proyecciones de ventas es vital, ya que cualquier margen de error puede tener un impacto significativo en los resultados financieros. En un mundo empresarial altamente competitivo, la capacidad de identificar patrones y relaciones entre indicadores y ventas es un activo invaluable. La implementación de soluciones de pronóstico de ventas precisas y eficientes no solo permite recuperar pérdidas, sino que también impulsa el crecimiento sostenible y la toma de decisiones informadas, lo que convierte esta área en una prioridad en cualquier organización que busque el éxito en el mercado global. Con base en las razones y el contexto expuesto anteriormente, este proyecto de grado tiene como objetivo desarrollar una solución, que incluya un modelo de predicción de ventas, el cual emplee técnicas de machine learning previamente seleccionados e investigadas, para la predicción de ventas, incorporando diferentes variables exógenas que impactan significativamente en el mercado. Para los modelos seleccionados, se incluirán variables relevantes tanto del entorno empresarial como del contexto externo. La solución se visualizará a través de una aplicación web, desarrollada para el trabajo de grado. El ambiente de desarrollo será Google Colab y se utilizará una aplicación web desarrollada en Angular como plataforma de visualización de la data. Inicialmente, se extraerán indicadores macroeconómicos del mercado mundial de la página del Banco Mundial, posteriormente se realizara un análisis exploratorio de los datos y se decidirá que variables tanto internas como externas serán de importancia para la realización del modelo. A continuación, se realizará la evaluación de diferentes modelos de machine learning aplicados a Series Temporales. En este contexto, se evaluará el modelo con los datos de las ventas de una compañía del sector de tecnología. El modelo deberá aprender de las múltiples variables y pronosticar las ventas. Por último, se realizará la visualización de los datos a través de una aplicación web, donde se mostrará la precisión de predicción del modelo, la data histórica, data pronosticada y las variables de alto impacto en el modelo.
Abstract (English)
Currently, the business and organizational environment is facing a widespread problem that transcends sectors: the inefficient management of sales forecasts and their lack of implementation. Sales forecasting within organizations is done manually and using obsolete methods, which consumes valuable resources, both in time and money, with quite serious economic consequences. We are talking about shocking figures, with values ranging from 568 million pesos at the level of a single organization. These losses represent real challenges for companies, since these resources could be used for expansion, innovation and investment in other crucial aspects of the business, increasing its growth. In this context, accuracy in sales projections is vital, as any margin of error can have a significant impact on financial results. In a highly competitive business world, the ability to identify patterns and relationships between indicators and sales is an invaluable asset. Implementing accurate and efficient sales forecasting solutions not only enables loss recovery, but also drives sustainable growth and informed decision making, making this area a priority in any organization seeking success in the global marketplace. Based on the above reasons and context, this degree project aims to develop a solution, including a sales prediction model, which employs previously selected and researched machine learning techniques for sales prediction, incorporating different exogenous variables that significantly impact the market. For the selected models, relevant variables from both the business environment and the external context will be included. The solution will be visualized through a web application, developed for the degree work. The development environment will be Google Colab and a web application developed in Angular will be used as the data visualization platform. Initially, macroeconomic indicators of the world market will be extracted from the World Bank website, then an exploratory analysis of the data will be performed and it will be decided which internal and external variables will be of importance for the realization of the model. Then, the evaluation of different machine learning models applied to Time Series will be carried out. In this context, the model will be evaluated with the sales data of a company in the technology sector. The model should learn from multiple variables and forecast sales. Finally, the visualization of the data will be done through a web application, where the prediction accuracy of the model, the historical data, the predicted data and the variables of high impact on the model will be shown.
Director

Advisors/Directors

Jiménez Gordillo, José Fernando
Extent
93 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/2815
Collections

Collections

GE - Trabajos de Grado Maestría en Ciencia de Datos
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