Economic impact of materials modelling
Here is the executive summary of a new report on the economic impact of materials modelling, co-authored with Christa Court from MRIGlobal in the framework of the European Materials Modelling Council (EMMC) and the International Materials Modelling Board (IM2B). The full text as well as survey form is available here.
At the core of the report is an industry survey conducted during 2015 that provides corroboration for the indicators of research and development (R&D) process improvements found in earlier studies and new data relevant for quantitative economic analyses.
The survey is set in the context of an outline of metrics and methodologies that can be used to quantify the economic impacts of materials modelling from a variety of perspectives including R&D and industry stakeholders and society at large. At the micro-economic level, performance indicators include financial metrics such as net present value, return on investment (ROI), and internal rate of return. Where sufficient data are available, micro-economic analyses could be extended to a more in depth cost benefit analysis. Finally, macro-economic modelling methodologies can be used to model the wider impacts of the integration of materials modelling into the production function of various industries. Since materials modelling is a potentially disruptive technology, macro-economic impact assessment will likely require dynamic simulation models, which are scenario specific and necessitate someone with a high level of both problem domain knowledge and modelling domain knowledge.
Research impact is reviewed briefly based on bibliometrics, case studies, peer review, and economic analysis  using evidence gathered for a previous report  as well as the recent UK Research Excellence Framework , which includes 15 cases involving materials modelling.
The study also investigates how materials modelling impacts the industrial R&D process and outlines the value and potential of materials modelling for industrial research and innovation, competitiveness, and profitability using examples from materials industries based on recent Integrated Computational Materials Engineering studies and a Computer-Aided Drug Design study, which demonstrated the usefulness of defining a performance metrics for a modelling function in an industrial R&D organisation.
The survey analysis was based on information provided by 29 companies covering a wide range of sizes and industry sectors and an even distribution in terms of types and scales of modelling. The qualitative benefits identified in the responses were categorised into the following Key Performance Indicators: More efficient and targeted exploration; Deeper understanding; Broader exploration; R&D strategy development; Source of property data; Trouble shooting; Performance optimisation; Intellectual property protection; Value chain benefits; Improved communication and collaboration between R&D and production; Upscaling and market introduction as well as marketing benefits.
On a quantitative level about 80% of companies reported innovation accomplishment, 60% cost savings, 35% job creation, and 30% revenue increase due to materials modelling. A wide variety of project sizes are represented, with total materials modelling investment (covering staff, software and hardware) ranging from €45K to €4M (average €1M, median €½M). Staff was the largest cost factor: the ratio of staff costs to the median cost of software and hardware, respectively, is 100/20/6. Cost savings due to the materials modelling project ranged from €100K to €50M (average €12M, median €5M). The ROI, determined by the ratio of revenue generated and investment in modelling, ranged from 2 to 1000. Removing the largest and the smallest values yields an average ROI of 8. A trend for ROI to grow more than linearly with investment in modelling was found.
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