As part of its digitalization offensive, SMS group (www.sms-group.com) has kicked off a research and development project on intelligent production planning in coopeation with Jacobs University of Bremen, Germany. The project will cover aspects such as dynamic reacting to specific production situations, use of artificial intelligence and autonomous learning of automation systems.
The dynamic planning and optimization processes to be developed during the project will be integrated into the automation environment in place at the SMS group customer Big River Steel in the U.S.A, which includes, for example, the X-Pact® MES 4.0 production planning system. Specifically, the project is to provide solutions and optimization models for “production planning with real product cycle times”, “yield optimization through smart campaign planning” and “re-scheduling of sequences in CSP® plants (Compact Strip Production)” after interruption of casting”.
Improved adherence to production schedules and increased yield by reducing downgrading and scrap will have positive effects on the economic efficiency of the customer’s production facilities. For example, it is planned to introduce machine learning and pattern recognition techniques to predict the timeliness of orders. A further objective of the project is the development of a planning module based on artificial intelligence - X-Pact® MES 4.0 Performance Enrichment Analysis. This module is to detect relationships between production parameters and performance indicators on the basis of historic production data. These capabilities are intended to be used, for example, to perform scalable, self-learning order analyses and generate plans that take into account order schedules.
The joint project was officially kicked-off at the end of 2017. During a presentation of the project at Big River Steel in the U.S.A., representatives of the Jacobs University explained their views of what steps should be taken to implement a “Learning Factory”. They presented previous reference projects to illustrate the University’s capabilities and suitability as a partner for this project. The responsible research group is internationally renowned for the high quality of its network-based analyses of complex systems, its strong international orientation, the highly professional project management and the excellent know-how in modern data analysis methods.
The dynamic planning and optimization processes to be developed during the project will be integrated into the automation environment in place at the SMS group customer Big River Steel in the U.S.A, which includes, for example, the X-Pact® MES 4.0 production planning system. Specifically, the project is to provide solutions and optimization models for “production planning with real product cycle times”, “yield optimization through smart campaign planning” and “re-scheduling of sequences in CSP® plants (Compact Strip Production)” after interruption of casting”.
Improved adherence to production schedules and increased yield by reducing downgrading and scrap will have positive effects on the economic efficiency of the customer’s production facilities. For example, it is planned to introduce machine learning and pattern recognition techniques to predict the timeliness of orders. A further objective of the project is the development of a planning module based on artificial intelligence - X-Pact® MES 4.0 Performance Enrichment Analysis. This module is to detect relationships between production parameters and performance indicators on the basis of historic production data. These capabilities are intended to be used, for example, to perform scalable, self-learning order analyses and generate plans that take into account order schedules.
The joint project was officially kicked-off at the end of 2017. During a presentation of the project at Big River Steel in the U.S.A., representatives of the Jacobs University explained their views of what steps should be taken to implement a “Learning Factory”. They presented previous reference projects to illustrate the University’s capabilities and suitability as a partner for this project. The responsible research group is internationally renowned for the high quality of its network-based analyses of complex systems, its strong international orientation, the highly professional project management and the excellent know-how in modern data analysis methods.
