Research | Funded Projects
Research Funded Projects
THE ASSESSMENT OF TECHNOLOGICAL COMPETENCIES IN THE SUPPLY CHAIN
The aim of this research project is to define a tool for assessing the technological competencies of supply chain partners, which should be integrated with other evaluation criteria for selecting suppliers.The technological content of a product and its components will be analysed through the study of patent data and the specialization of the different players on the technologies included in the product will be evaluated. In particular, content analysis of patents abstracts will be used.The methodology will be based on the following steps. First, given a specific product, its main components will be analysed and, for each component, its supplier will be found. Then, all the patents filed by such suppliers will be searched on PATSTAT and content analysis will be performed using T-LAB, in order to find the keywords that can be associated to the component. Therefore, for each component of the product we will have a list of keywords that are associated to the technologies included in the components.Occurrences of the specific keywords and their co-occurrences with the keywords related to the main product will be calculated. Some variables can be thus defined, in order to describe the technological competence of the supplier on the technologies included in the component:-development capability, defined as the number of occurrences of the specific keyword, which indicated how many times the specific technology is faced in the patents of the supplier;-commitment, defined as the number of occurrences on the number of patents of the supplier, defining which part of the innovation effort of the supplier is devoted to such technology;-technological specialization on the specific product, defined as the number of co-occurrences of the keyword with the keyword related to the main product, which indicates how much the specific technology was already used for the product under investigation.Furthermore, some quality indicators can be found for the keywords by defining their occurrences in high quality patents. Different indicators for patent quality can be used, e.g. its potential originality, defined with the lack of backward citations to prior art, or its technological acknowledgment, defined with the presence of external forward citations. Therefore, the number of occurrences of the keywords in those patents that are defined as quality ones, is a measure of the “quality” of the keyword itself.The same procedure can be made on the patents of the focal company, in order to detect both the keywords associated to the whole product and those associated to single components. The former will be used to define the technological competence of the company on the product and the latter to define its competence on some components, in order to compare it to the competence of the selected supplier, also to understand the motivation of the selection of the specific supplier.All the variables defined could be included in an assessment tool for evaluating the technological competences of the suppliers on the specific technologies, in order to help in the selection process.To start, the project will be based on the study of the product “smartphone” whose technological content is high even if the number of components is not very high, so that the analysis will be simpler. Yet, it could be extended to other kinds of product. The analysis can be performed both by analysing different subsequent models of smartphones for the same company, on order to evaluate the changes in the strategy of a specific company, and by comparing two or more models of concurrent smartphones for different companies, in order to compare the different strategies of competitors.
Department | Dipartimento di Ingegneria Industriale/DIIN | |
Principal Investigator | MICHELINO Francesca | |
Funding | University funds | |
Funders | Università degli Studi di SALERNO | |
Cost | 11.300,00 euro | |
Project duration | 20 November 2017 - 20 November 2020 | |
Proroga | 20 febbraio 2021 | |
Research Team | MICHELINO Francesca (Project Coordinator) Celone Andrea (Researcher) |