Inventive Design (ID) methodology has been developed in four main phases, from initial situation analysis to solution concept, in order to overcome TRIZ limitations. However, this methodology needs to optimize its performance, because it takes a lot of time to obtain the best solution concepts. In addition, the ability of ID to give the best result depends on individual knowledge and experience of designer, and correctness of initial situation analysis. The proposed methodology in this article uses a collection of tools and combines a long-term vision for continuous improvement. This improvement method focuses on removing the non-value added activities during the process and maximizing the quality of the results. Integration of this proposed methodology with ID framework will eliminate the wastes, which occur in different phases of ID method and increase its overall efficiency and agility.

No abstract

Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.