Generating Dashboards with Python on the RPA process for optimizing the assignment of gardening tasks

Hilario A.C.; Pedro Raúl A.D.L.C.; Herminio G.V.A.; Flores Salinas J.A. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/leird2024.1.1.709

Resumen

This article presents the results of a research focused on the development and implementation of dashboards for the visualization of Key Performance Indicators (KPIs) in the context of the automation of the assignment of gardening tasks through Robotic Process Automation (RPA). The research was carried out under a mixed methodological approach, using the quantitative method for the collection and analysis of operational data, as well as the qualitative method through interviews and observations to evaluate the perception of end users on the effectiveness of the solution. The research design was based on the Design Thinking methodology for the identification of needs and the development of iterative prototypes, complemented by the Lean Six Sigma methodology to optimize processes and reduce inefficiencies. During the development phase, critical KPIs such as response time, service level and percentage of tasks correctly assigned were selected. Based on these indicators, interactive dashboards were designed and built using advanced visualization tools, such as Tableau and Power BI, and integrated with an RPA system set up in a controlled environment. The results indicated significant improvements in three key areas: (1) a 40% increase in operational efficiency through the automation of repetitive tasks; (2) a 25% decrease in the number of errors in task assignment; and (3) the ability to monitor process performance in real time, facilitating informed decision-making. In addition, a high level of satisfaction was observed among users, who highlighted the clarity and usefulness of the dashboards to visualize the workflow and manage resources proactively. In the discussion, the positive impact of combining RPA with KPI visualization to optimize operational processes is highlighted, and areas for improvement are identified, such as the need to customize dashboards for different user profiles. Finally, recommendations are offered for large-scale implementation in other service areas, highlighting the importance of continuous adaptation and real-time impact assessment to ensure sustained performance. This study provides new insights into the strategic use of Robotic Process Automation and dashboards as complementary tools for operational process improvement and effective resource management in service-oriented industries.

Instituciones: Universidad Nacional de Ingeniería