NMBP-23-2016 - Advancing the integration of Materials Modelling in Business Processes to enhance effective industrial decision making and increase competitiveness
The proposals should develop an integrated Business Decision Support System (BDSS) that can support decisions on new materials and new processes by calculating through hypothetical scenarios.
The BDSS should enable the integration of materials modelling and business tools and databases into a single work-flow, allowing for flexibility of application to different industrial sectors.
Proposals should create a framework that allows the flexible integration of existing or future discrete and continuum materials models with structured and unstructured data from multiple data bases containing materials data, commercial data and information on market trends, pricing, customer needs and demands.
The BDSS should enable a multi-criteria optimisation over all stages of product development by allowing the end-user to mirror the operational framework of their company. The structure of the BDSS should allow back-engineering from the end-goal. BDSS should be designed such as to optimise the integration of humans in new more complex industrial environments. The tool should be available to and usable by decision makers in manufacturing companies in the form of a platform which can be confidentially applied by a company.The tool should be validated against measurements, existing data and real financial arguments. Validation of the developed systems on public case studies and training of translators on the system is required.
Development of innovative methodologies should be included addressing innovative ways to connect existing and future models and how to use them in varying contexts (adaptive systems and networks). If appropriate, model development in terms of accuracy, robustness, uncertainty qualification and speed to allow a large design space to be explored may be included in order to enable exchange of modules and to prove flexibility of the framework. The consortium is expected to provide expertise on multiple discrete and continuum materials models[[http://ec.europa.eu/research/industrial_technologies/modelling-materials_en.html]], business decision support systems, data search technology (incl. optimalisation, genetic algorithms, symbolic regression, machine learning and cognitive learning).
Activities are expected to target Technology Readiness Level 5.
Funded proposals will be invited to participate in a cluster, to agree on standards to achieve seamless integration of their frameworks and of the modules to be linked into the framework. Projects are expected to contribute actively to on-going activities e.g. in the EMMC (European Materials Modelling Council), and EU funded clusters.
The Commission considers that proposals requesting a contribution from the EU between EUR 3 and 4 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.
Sustaining and growing businesses requires continuous product innovation. Making meaningful business strategy decisions is an ever more challenging task in a global context. The combination of materials and business modelling to explore what technical solutions are economically viable is not yet exploited to the extend it could. The sheer volume of data and information combined with its dynamic nature demands an ever better understanding of possible options. There is a need for a Business Decision Support System that supports the selection of the optimal material and process taking into account the implementation costs but also the associated risks, uncertainties and costs related to the modelling and simulation; a priority, especially for SMEs.
- Reduction of company costs and increased performance and commercial impact based on effective materials models driven business decisions;
- Guidance to companies in developing their strategies with an effective, user friendly materials models driven business decision system;
- Increased industrial use of existing materials knowledge and effective materials models;
- Improved trust of industrial decision makers in materials modelling and their commercial advantage;
- Essential company savings in time and money, especially via the elimination of the need for (some) plant trials.