MART PM - Sustainable Manufacturing by Automated Real-Time Performance Management
|Coordinator||Lunds universitet - Institutionen för maskinteknologi - Lunds universitet|
|Funding from Vinnova||SEK 4 871 166|
|Project duration||April 2018 - May 2021|
|Venture||The strategic innovation programme for Production2030|
|Call||Produktion2030, Call for Cost-Effective Automation|
Purpose and goal
The purpose of the SMART PM project is to streamline production and make it more sustainable through digitalized decision-making tools for production management. The project has had several work packages that focus on different aspects of the overall problem: New optical technology for collecting assembly data has been tested. Machine learning has been used to analyze sensor data to predict tool life. A process for starting up digitization programs in industry has been proposed and tested in several factories.
Expected results and effects
The project as a whole has already had, or has the potential to have, the effects intended in the proposal: To make production management more efficient and sustainable through digitalisation. The most important result from the project is a start-up process for the digitalisation journey in industrial companies, which has already been applied with great success. Another success of the project has been the development of a method to determine what information is actually needed before deciding on the digital technology for data collection: the PULL workshop.
Planned approach and implementation
The project SMART PM has been carried out by a consortium consisting of academics and industry stakeholders. The academic parties were Lund University of Technology, Chalmers University of Technology and the Royal Institute of Technology. Participating companies were Volvo Cars, Scania CV, Volvo CE, Mastec, Emballator, Swepart and Alfalaval. The project was carried out through a number of case studies that included various research and data collection methods: From interviews to machine learning of sensor data.