Technology Forecasting Model based on Trends of Engineering System Evolution (TESE) and Big Data for 4IR

Headed by Dr Ang Mei Choo.

Skim Geran Penyelidikan Fundamental (FRGS)

The 4th industrial revolution (4IR) is already here and the technological disruptions it brings to industries that are not prepared are severe. The ability to forecast the trends of technological evolution is crucial to increase their competitiveness. Industries need to use Trends of Engineering System Evolution (TESE) and Big Data Technologies to their advantage to innovate their products to face 4IR. TESE has hierarchical levels of multiple trends and sub-trends for forecasting the technological evolution but has no link to the data in patent information. Patent data is growing exponentially annually and is a Big Data that can be mined and to be integrated with TESE. This research work embarks on these objectives: (1) to determine the feasibility and ways of linking TESE with Big Data Technologies; (2) to adopt Big Data technologies for extraction of patent data; (3) to derive a novel model that can forecast the trends of technological evolution for product innovation based on TESE and patent information and (4) to validate the efficacy of the novel model. The methodology will involve extensive studies on patent information that can be used to forecast the trends of technological evolution for a product before adopting suitable data mining technologies to derive a novel model for technology forecasting. The methodology consists of five phases: Phase I: Preparation (Comprehensive study and analysis), Phase II: Analysis and evaluate Big Data technologies for mining data from Patent information, Phase III: Derivation of the Novel Technology Forecasting Model, Phase IV: Validation of the derived novel model via experiments using a prototype, and Phase V: Reflections on the novel model. The significant output from this research is a novel model that can forecast the trends of technology evolution for product innovation based on TESE and Big Data Analytics on patent information.