Identification & Background:
Project title: iSafety: Intelligent system for occupational safety and well-being in the retail sector
Project code: NORTE-01-0247-FEDER-072598
Main objective: Strengthening research, technologicaldevelopmentandinnovation
Region of intervention: North of Portugal
Beneficiary entity:
MORE – Laboratório Colaborativo Montanhas de Investigação – Associação (Líder do projeto)
Instituto Politécnico de Bragança, IPB
Sonae MC – Serviços Partilhados SA
Approval date: 2021-07-29
Start date: 2021-04-01
Conclusion date: 2023-06-30
Total eligible investment: € 617.145,80
European Union financial support: FEDER – €413.504,
Objectives
The iSafety project comprises the following objectives:
Obtain a functional product for the retail sector, and in this project the target is the Direction of Health and Safety at Work of SONAE MC,to support the management and prevention of accidents and consequent risk reduction (able to identify the probability of future accidents, from historical data).
Develop a local extension of the predictive model to be implemented in 9 Units of SONAE MC. The goal is to go beyond reading historical data and create the interaction and communication between the predictive model and the local unit, in particular to provide a pilot product that can be fed through local (e.g. machine failure) or external (weather) insights.
With the implementation of the solution developed it is intended to:
• Reduce by 20% the number of work accidents in the 9 units under study.
• Reduce by 10% the frequency rate of work accidents in the 9 units.
• Reduce by 10 % the severity rate of accidents at work in the 9 units
Activities
The project includes the execution of the following activities:
A1-Preliminary studies and technical specifications
A2-Design of the forecasting model
A3-Prototype development in a controlled environment
A4-Testing and validation in a relevant environment
A5-Dissemination, communication and exploitation of results
A5-Project management
Expected results
An innovative tool supported by ICT tools combined with statistical strategies, data analysis and machine learning algorithms for early identification of situations with potential risk of accident or about disease development related to your work.
Cofinanciado por:
