Sustainable and Flexible Automation of Seasonal Production through Dynamic Resource Management
Reference number | |
Coordinator | Linköpings universitet - Linköpings tekniska högskola Inst f ekon & industruell utv IEI |
Funding from Vinnova | SEK 4 981 953 |
Project duration | April 2021 - May 2024 |
Status | Completed |
Venture | The strategic innovation programme for Production2030 |
Call | SIP Produktion2030, call 13 |
Important results from the project
The project has been successful in creating new synergies in the food and beverages sector with the purpose of developing a new solution for the growing challenge of handling custom orders in systems designed for high volume production. The final result was a movable robotized solution for the near optimal palletizing of mixed products. There are two key innovations: the palletization algorithm and the ultra-reconfigurable robotic cell which, in conjunction, allow the robot to operate autonomously where needed and when needed, increasing flexibility and reducing costs.
Expected long term effects
The dynamic and movable palletizing robotic cells and some of their support technologies, both developed in the project, are alreay in operation at ORKLA. The higher flexibility enabled by the cells allows operators to focus on more added value activities other then the manual construction of mixed pallets. Additional discussions are in place for the exploitation of the advanced palletization algorithm, both for the initial use case but also for a collateral use which is the optimization of packaging sizes, with a immediate impact in operations from a sustainability perspective.
Approach and implementation
The project was executed according to the work plan and in an agile way. In projects of high industrial involvement requirements and solutions need to be constantly monitored and revised and it is important to create a forum for constant discussion and refinement of R&D directions. This was the direction taken in the project which finally culminated in a set of results with very high scientific and industrial impact. This approach also resulted in important side effects such as the production of a public and realistic dataset in a domain where such date is rare.