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The acquisition of COSMOlogic by Dassault Systèmes adds to the continuing integration of specialised providers of chemistry and materials modelling technologies into larger corporations. Other examples include the acquisition of QuantumWise by Synopsys and e-Xstream by MSC software. As the announcement states, COSMOlogic is about “Accurate Predictive Thermodynamics Modeling“. Why is this interesting for industry?
The design and optimisation of chemicals, materials and processes relies on reliable and robust property data for increasingly complex systems. Predictive modelling creates value basically in two ways: support innovation by means of insights and deeper understanding and predict properties of chemicals and materials that are otherwise hard or costly to get. COSMOlogic offers in particular the latter: reliable, robust data that can be used to design and optimise systems such as chemical processes, as for example demonstrated in a case study on Identification of Solvents for Extractive Distillation.
In fact, similar arguments could be made for the other acquisition success stories. E-Xstream provides properties of composites at the detailed material level that are required to design and optimise manufacturing and products. QuantumWise enables not only insights but also advanced electronic material data required to in next generation TCAD. It will be interesting to see how integration story continues.
What is materials modelling good for?
This webinar examines the impact materials modelling makes, both on a macro-economic and organisational level. In particular, the wide range of impact types and mechanisms will be discussed, based on evidence from surveys and interviews with users. It will be argued that a much wider potential remit for modelling should be considered than is commonly done.
In the light of these impact mechanisms, ways of measuring and increasing impact are discussed. Setting and assessing impact levels is shown to be important, and in this context a maturity model will be introduced. Higher levels of maturity are associated with integration and optimisation and set the scene for modelling as a key factor impacting on digitalisation.
Industry Case Studies: combining discrete and continuum modelling to address industrial R&D challenges
Materials modelling is used today by a range of industries to improve efficiency and achieve breakthroughs in the development of new and improved materials and processes. A set of four case studies has been developed by the European Materials Modelling Council which demonstrate how industrial R&D problems have been addressed by the integration of different types of materials models and what technical and technological benefits and business impacts were achieved as a result.
The case studies cover a diverse set of applications and industries, including chemical processing (Covestro), discovery of new functional materials (IMRA Europe), additive manufacturing of engine parts (MTU Aero Engines) and magnetic hard drive materials (Seagate):
- Identification of Solvents for Extractive Distillation
- Discovery of new thermoelectric materials
- Simulation of additive manufacturing of metallic components
- Integrated Recording Model for Heat Assisted Magnetic Recording (HAMR)
The case studies were compiled with the support of the EC Industrial Technologies Programme.
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.
In the context of my work with the European Materials Modelling Council, I recently launched a survey to gather success stories and information about the economic impact of materials modelling. The survey aims to build on my previous report which was mainly based on a literature review.The survey is inspired by studies that have been done by IDC in the area of High Performance Computing, in particular their Innovation and ROI Awards. The results of their study show that even without absolutely complete information, a clear picture of the impact emerges as more and more cases are gathered.
Already ten organisations have taken part, and I am looking forward to the opportunity to discuss the initial findings at an international cooperation workshop on multiscale materials modelling organised by the European Commission in September.