Advanced Manufacturing Activities of Top R&D investors: Geographical and Technological Patterns
Bibliographic record
Abstract
Advanced manufacturing technologies (AMTs) and other key enabling technologies (KETs) are expected to have a major impact on productivity, efficiency, profitability and employment in major industrial sectors worldwide. Thus, development of AMTs and KETs\nis considered essential if the European Union is to achieve the strategic goals set out in the European Commission’s Employment, Growth and Investment priorities. Indeed, AMTs and KETs are among the top priorities identified as necessary to support the competitiveness of European industries in the context of the European flagship on industrial modernisation.\n\nThis study builds upon and extends results that were obtained in the context of the Advanced Manufacturing Technologies for Competitiveness AMTEC project, in which the technological profiles of the patent portfolios of the EU Industrial R&D Investment\nScoreboard companies were constructed using patent-based analysis. In particular, their technological competences were investigated and it was found that European companies invest in KETs, and in particular in AMTs, as these technologies are considered to be vital\nfor maintaining current competitiveness. However, other countries also invest heavily in AMTs and KETs.\n\nIt is therefore very important for the EU to define a strategy that aims to find a suitable position in the global value and innovation chains and that selectively augments existing capabilities. To this end, a methodology based on patent analysis was applied to assess the capacity of the world’s top R&D investors in developing AMTs. Particular emphasis was placed on complex AMT patents that also pertain to at least one of the five KETs. These patents are considered important because they represent AMT applications used for the development of KETs in general or, conversely, they represent other KET applications that can be incorporated into AMT systems. \n\nThe main questions addressed by this study were (1) In which countries are the most important inventors of AMTs and applicants for AMT-related patents located? (2) Is it possible to analyse internationalisation patterns and knowledge flows between world regions and countries? and (3) Are there any special patterns and clusters between AMT related technological fields and the five core KETs and, if so, which companies are responsible for the development of these technological applications?\n\nDeveloping and patenting AMT-related technologies is particularly important for firms in the Aerospace & defence, Industrials, Automobiles & parts and Electronics & electrical equipment sectors. Moreover, the more specialised a sector is in developing AMT-related technologies, the less internationalised the AMT-related activities of the firms in the sector appear to be.\n\nIn general AMT-related R&D activities of European- and US-based firms are more internationalised than the activities of Japanese- and Asian-based companies. It was found that many Scoreboard firms based in the USA, Japan, Germany, France and the UK own and develop a large number of AMT-related patents. However, there are also many inventors of AMT-related technologies based in other countries, such as China, India, Canada, Italy, Belgium and Spain.\n\nFinally, the ratio of complex AMT patents to the total number of AMT-related patents is close to 8%, the vast majority being patents that relate to micro- and nano-electronics, advanced materials or photonics. Companies that own these complex patents are often relatively small firms that are highly specialised in the development of AMT-related applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".