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Operational Planning Tool Interfacing Manufacturing Integrated Simulations with Empirical Data

Periodic Reporting for period 2 - OPTIMISED (Operational Planning Tool Interfacing Manufacturing Integrated Simulations with Empirical Data)

Reporting period: 2017-05-01 to 2018-10-31

OPTIMISED is developing and demonstrating a manufacturing scheduling optimisation system, applied to three industrial businesses in manufacturing and maintenance repair and overhaul (MRO) which uses sensor technology, simulation in the form of a digital twin, data analytics and artificial intelligence techniques to monitor, react to and improve manufacturing performance.

Productivity and sustainability is important for European society to ensure that manufacturing remains a cost-effective and competitive industry and employment sector.

OPTIMISED Vision
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.

The OPTIMISED vision has been achieved by developing systems which are able to Monitor system performance through an integrated sensor network, automatically detecting bottlenecks, faults and performance drop-off. Continuously evolve (through optimisation) to respond to disruptive events, supply chain disruptions and non-quality issues through factory simulation modelling. Improve understanding and monitoring of energy demand curve and energy usage per industrial process and globally improve efficiency of production line through reduced energy waste. Understand potential benefits, added value and impacts of participating in Demand Side Response (DSR) processes and becoming an active player in the changing energy industry, instead of remaining a conventional passive element that simply acquires a service from energy providers.

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 has been guided by the following specific scientific and technological objectives:
• Developing methods for real-time system awareness using integrated sensor networks
• 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.
• 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
• Designing methodologies for assembly requirements and capabilities modelling and system and station behaviour simulation
• Designing smart human-machine interfaces that pro-actively support the user throughout factory operations
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.

Work package 2 formalises the OPTIMISED architecture of the information back-bone and its integration with the semantic simulation models and the optimised planning and scheduling system. A reference architecture has been created.

WP3 uses the architecture and data mapping defined in WP2 to develop the data analytics methods and the information back-bone infrastructure.

WP4 uses 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.

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. All 3 demonstrators have been successfully completed.

WP6 focuses on the dissemination and commercial exploitation OPTIMISED project. A project website has been generated. A good number of presentations at industrial and academic conferences and workshops has occurred in the period which have been well received and have led to exploitation potential for the partners. An exploitation plan has been agreed and published. A project flyer for the project has been published and is used by the consortium members at dissemination events. 3 videos of the demonstrators can be found on the project website.

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.

Alstom
The results for single day planning against a baseline dataset show a 60% reduction in over-maintenance. A significant reduction in daily planning from 3hrs to 30 minutes (29.5 minutes required for human validation, 19seconds required for optimized plan calculation).

GOIMEK
The results for the manufacturing demonstrator at GOIMEK show a >10% improvement in planning speed and accuracy and further show the opportunity to reduce and constrain energy consumption which could lead to significant cost reductions in energy utilization as well as improving the environmental sustainability of the products.

Laing O'Rourke
The increase in service level achieved is 2% with an associated reduction in working hours of 10%.

3 Key Exploitable Requirements of technologies for future exploitation have been analysed and business cases prepared.

2 research articles published, 9 in preparation or accepted
The 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
• manufacturing-specific decision-making tools based on data analytics and big data
• 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
• SAFETY
OPTIMISED uses the principles of Measure, Simulate, Optimise
Project Logo
3 Demonstrators