| INSTRUMENTS |
CONTRIBUTION TO THE CIRCULAR ECONOMY |
| Big data |
Provides meaningful information to the user (Orozova & Georgieva, 2014);A foundation for informed decisions in CE-enabled supply chain networks (Gupta et al., 2019) |
| IoT |
Communication between devices on the Internet, in order to perform business processes and activities, such as automated manufacturing, home automation and smart waste management (Liu et al., 2022);Possibilities to improve control in CE;Supports the fulfilment of the complex requirements of circular supply chains;Extending the use of the product, improving its maintenance, by detecting errors and improving technical support;Reduces the need to keep large stocks of spare parts in physical warehouses. |
| Block-chain |
Guarantees high levels of transparency in the supply chain;Ensures traceability, ethical sourcing, more efficient material flows. |
| Machin learning |
Preparation of forecasts with a high degree of accuracy;Use of massive amounts of data analysis in the design of circular products;The reduction of human bias in testing and prototyping (Ghoreishi & Happonen, 2020);Offers systematic solutions for industrial applications (Lee et al., 2018) |
| Computer Vision |
Automatic extraction, analysis and understanding of useful information from an image or sequence of images.Autonomous visual comprehension. |
