OPTIMISED is developing and demonstrating a manufacturing scheduling optimisation system, which uses smart sensors and data analytics to monitor, react to and improve manufacturing performance. OPTIMISED uses the principles of Measure, Simulate, Optimise?Measure: Machine and...\n\nOPTIMISED is developing and demonstrating a manufacturing scheduling optimisation system, which uses smart sensors and data analytics to monitor, react to and improve manufacturing performance.
OPTIMISED uses the principles of Measure, Simulate, Optimise
- Measure: Machine and Operator performance, energy demand side response, Internet of Things (IoT) sensors
- Simulate: Production simulation, factory planning & operations models
- Optimise: Use data analytics to re-plan production schedule, operator and maintenance tasks, based on measurements
Developing methods and tools for deployment of highly optimised and reactive planning systems is our vision. This can be done using factory modelling and simulation based on empirical data. The data is captured using smart sensors as well as pro-active human-machine interfaces.
Energy is the future
The impact of energy management on factory planning and optimisation is specifically assessed in the project. Reducing energy waste on one side, while understanding how energy is used in detail on the other side, allows future factories to reschedule production according to desired energy consumption. This is especially beneficial to energy providers, who seek to balance energy demand.
The research will be guided by the following specific scientific and technological objectives:
- Developing methods for real-time system awareness using integrated sensor networks, enhanced metrology, RFID product tracking technology and augmented reality devices
- Developing robust scheduling optimisation of the entire assembly line as well as of individual production units and operative resources
- Developing a multi-level scheduling simulation to address the multiple timescales that impact factory operations
- Developing systems with the ability to respond to changes in offsite power supply by reducing factory energy demands to agreed capped levels. Such systems will also allow the re-optimisation of the production process schedule to best achieve production demand given energy constraints
- Investigate and assess the impact on factory operations of energy demand management
- Designing advanced data gathering and distributed control infrastructures that integrate with an information management backbone enabling smart sensors and use of Internet of Things (IoT) devices and products
- Designing methodologies for assembly requirements and capabilities modelling and system and station behaviour simulation
- Developing mobile and collaborative tools for knowledge sharing and data analytics for resource performance optimisation
- Designing smart human-machine interfaces that pro-actively support the user throughout factory operations\n\nThe 7 Work packages within OPTIMISED have all been formally kicked-off.
Work package 1 (WP1) captures and defines the requirements that will lead to development and implementation of the OPTIMISED components within the industrial demonstrators. This work package began with the publication of a report on the state of the art. Industrial use cases have been defined for each demonstrator capturing their driving challenges. These Industrial use cases have been used to develop system requirements for each demonstrator, which has been done leading to the publication of D1.3, D1.4 and D1.5 system requirements documents. The validation test specification document has also been published.
Work package 2 will formalise the OPTIMISED architecture of the information back-bone and its integration with the semantic simulation models and the optimised planning and scheduling system. This work package began with the publication of the information model for the existing factories. The following documents have also been published during the period Data and Information Management plan, Data model design report, report on information architecture design, implementation strategy. These documents have allowed the development of a system architecture for OPTIMISED to allow the software development to begin. The implementation strategy specifically addresses how the software will be implemented.
WP3 will use the architecture and data mapping defined in WP2 to develop the data analytics methods and the information back-bone infrastructure. A report on data analytics implementation methods has been generated within this work package and the application of such methods to the demonstrators. A preliminary release of the dashboard software occurred within the period from this work package.
WP4 will use the architecture defined in WP2 to develop semantically driven simulation models for energy, process systems, the optimisation methods and optimised planning and scheduling applications and tools. The report on manual station simulation models has been published. The simulation models are progressing to plan for the 3 demonstrators.
WP5 focuses on the demonstration of the OPTIMISED components in the form of industrial demonstrators. These are based on the validation test cases defined in WP1 and also match the approach to develop technologies on system and station level that can smoothly be integrated. Significant progress on Demonstrator 1 has been made to date with Demonstrator 2 and 3 coming later in the programme.
WP6 focuses on the dissemination and commercial exploitation OPTIMISED project. A project website has been generated. An increasing number of presentations at industrial and academic conferences and workshops has occurred in the period with an associated twitter and LinkedIn account. A draft exploitation plan is in the advanced stages of development although is not due for project publication until month 36. A project flyer for the project has been published and is used by the consortium members at dissemination events.
WP7 is responsible for the management of the project. The project handbook was published internally to the project members early in the project and this is used to guide all project members. A project intranet using sharepoint was also created for the consortium members.\n\nThe current state-of-the-art leaves a number of notable knowledge gaps that will be targeted by the OPTIMISED project:
- pro-active Human-Machine Interfaces with sufficient support for the operator
- integrating local smart energy systems and tools with industrial enterprise systems
- integrating factory microgrids with the external grid supply
- manufacturing-specific decision-making tools based on data analytics and big data
- industrial implementation of advanced self-aware stations with local data analysis capabilities for use in energy consumption management and predictive maintenance
- computational frameworks for automatically learning how to optimise multi-level factory scheduling
OPTIMISED is working to address the following potential impacts in the areas of Economic Impact, Environmental Impact and Societal Impact:
- REDUCED RAMP-UP TIME AND REDUCED TIME-TO-MARKET
- REDUCED ENERGY COSTS AND INCREASED UPTAKE OF FEED-IN TARIFFS
- REALISATION OF LEAN
- REDUCTION IN ENERGY CONSUMPTION AND ENERGY WASTAGE, AND PROMOTION OF RENEWABLE ENERGY
- RAPID REACTION TO THE DEMAND SIDE RESPONSE REQUIREMENTS
- REDUCTION IN WASTE GENERATION AND REDUCTION IN MATERIAL CONSUMPTION
- AGING POPULATION
- FACTORIES AS GOOD NEIGHBOURS
- FLEXIBLE METHODS OF WORKING AND FACTORIES FOR HIGH LABOUR-COST AREAS
- JOBS / JOB RETENTION
- Not specified