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CT will carry out research and development on a predictive algorithm capable of optimizing the various factors that affect the wellbeing and productivity of employees using data provided by an IoT system.
This comprehensive proposal includes various objectives such as employee wellbeing, energy savings, tracking of people, equipment and/or tools, presence monitoring, and industrial safety.
CT, a leader in providing innovative technological engineering solutions for the entire product life cycle, has launched a new R&D project to develop a predictive algorithm to optimize the various factors that have an impact on the wellbeing and productivity of employees using data collected by an IoT system, client and user preferences, as well as other external data. An IoT solution will be implemented to collect data in a real setting that is relevant to the objective of the project, creating an adaptive algorithm that learns from historical data and is also capable of making predictions to optimize the impact of measures taken or proposed. Other factors that can be optimized include the temperature in an office or the loss of tools in a manufacturing company, starting with the sensorization of tools and employees, to keep track of work tools. Both the solution as well as the demo will be implemented incrementally using the SCRUM process framework.
Currently there is no solution on the market that takes an integrated approach to objectives such as employee wellbeing, energy savings, tracking of people, equipment and/or tools, presence monitoring, and industrial safety. Having such a solution would not only have an impact on the results of private companies and public institutions, but also it could have a positive impact on public health and emergencies in which this type of system would allow people in dangerous situations to be located.
Upon completion, this project will introduce novel software to the market that not only collects and processes data from an environment, but also learns from how they evolve over time and is capable of relating different parameters to make predictions that improve the comprehensive response of the system.
The project is being co-financed by the Spanish Ministry of Energy, Tourism and Digital Agenda within the National Plan for Scientific and Technological Research and Innovation 2017-2020, file number TSI-100110-2019-5.
