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Decision-making predictive model development for the organization active positioning faced with the appearance of new technologies

We are now living in a world where the globalisation phenomenon has changed the world economic order. We are also facing other important challenges such as climate change, the shift paradigm in the energy model, social inclusion among others.

 

In this context, innovation emerges as a key figure. It is a dynamic and multifaceted process that drives progress, competitiveness and growth. It is a fundamental element to facing the challenges of our constantly evolving society, promoting change and, in short enhancing the quality of life of society. Innovation is a complex process that involves the creation, development and implementation of ideas, methods, new or significantly improved products or services. Its purpose is based on generating value, solving problems, satisfying needs or taking advantage of the different opportunities that arise in various fields.

 

We can also see that the scope of innovation is not only limited to the development of products or services, but also involves processes, business models, organisational strategies and changes in mindset or culture to foster creativity and adaptability. The expression of this process can be incremental, driving gradual improvements, or disruptive, generating radical changes capable of completely transforming industries.

 

In view of this scenario, it is necessary to introduce the concept of forecasting as the possibility of making statements about something specific sufficiently in advance. In this case, forecasting innovation allows us to know the trends that will emerge in the future, their impact on society or on our organisation, to analyse whether our R&D system is well oriented, to redirect research if it is necessary and to facilitate decision-making in the senior management of companies or institutions.

 

This research work proposes a new approach applied to the generation of predictive models of innovation, specifically the creation of new patents. It is based on a combination of both scientific articles and patents, which can be analysed and associated with a given technology. The proposed solution has been developed for hot stamping technology and subsequently validated with cast iron technology. This procedure combines statistical methods with machine learning techniques, resulting in both a Bayesian network and a decision tree model that help us to identify patterns of behaviour in the production of protected intellectual property. In summary, the analysis performed is able to detect, with a high degree of probability, a researcher’s propensity to generate new patents.

 

The proposed solution, developed for hot stamping technology and, subsequently, validated for cast iron technology, combines statistical methods with machine learning techniques, resulting in both a Bayesian network and a decision tree model, which help us to identify patterns of behaviour in the production of protected intellectual property. In summary, the analysis performed is able to detect, with a high degree of probability, a researcher’s propensity to generate new patents.

Authors:

Luis Miguel Arias

Thesis directors:

Javier Nieves (AZTERLAN), Garikoitz Artola (AZTERLAN), Igone Porto (UPV/EHU)

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