Resumo:
The appliance of information systems in managing the productivity of field teams
is a natural way to solve the various challenges for the evolution of management
practices in service companies particularly in sectors that use field teams such as
telecommunications, energy, logistics, sanitation, or maintenance. Field teams are
responsible for much of the operational work and have a significant impact on customer
satisfaction, directly affecting the company's financial performance. A system to
replace the coercive control of dispatch supervision teams needs to have the
intelligence to cross data from different monitoring mechanisms, learning from the
behavior of the teams and encouraging people to collaborate naturally with information.
The question to be answered in the research is how to monitor activities productivity
of people in autonomous and remote work. The main objective of this work is to
develop a model, using machine learning in image recognition, to the implementation
of an information system for monitoring the service provision process of
telecommunications companies. To this end, it presents a most important processes
of telecommunications field service mapping and a detailed description of collecting
useful data opportunities. The research methodology adopted was CRISP-DM in its
six phases that include: understanding the problem, understanding the data, data
preparation, modeling, assessment, and application. As a result, the research
contributes to an automatic productivity monitoring approach, with minimal operator
requirements and team adherence in an organic and systemic way. In addition, the
research presents the implementation models for the proposed system.
