The early-stage engineering design activities include conceptualising, identifying, and solving an engineering design problem. These activities are essential and standard roles of a design engineer. However, they seem to lack comprehensive practice within the engineering design community. In this study, semi-structured interviews conducted with 18 participants having engineering design backgrounds are presented. The aim of the interviews is to investigate the awareness and practice of the early stage engineering design activities. The participants interviewed practice in countries including France, the United Kingdom, and the United States. Contrary to standard expectations, the results of the interviews show that the early-stage engineering design activities are not comprehensively practised. The results suggest that design engineers' crucial role in identifying unknown problems lacks practice. Also, the data from the interviews provide empirical evidence on the determinants for the lack of comprehensive practice of early-stage engineering design activities. Recommendations on possible interventions to support the practice are presented to expedite innovations and inventions.

The availability of digital data in the fourth industrial revolution brings different trends with new opportunities and challenges for the engineering design community. As an opportunity, these trends would impact engineering design. However, the challenge is finding applications for these trends in engineering design. Crowdsourcing is one of the trends inspired by digital data. It is outsourcing an individually performed task to be mass-performed. This paper explores the application of crowdsourcing in identifying engineering design problems. Identifying an engineering design problem is an aspect of engineering design considered challenging but necessary for inventions. Secondary data from 63 invention-related cases and an interview with a renowned UK inventor are presented. The data contains scenarios on how the engineering design problems solved to qualify for a UK patent grant or application are identified. Lessons from the case studies are presented and discussed, especially regarding crowdsourcing engineering design problems. These seem to be promising ways of supporting the identification of new engineering design problems with inventive benefits once solved.

Creativity is required in engineering design. It is required in the aspects of problem-solving - conceptualizing a new solution to a problem, and problem-exploring - conceptualizing a new problem. Studies show that, in both aspects, creativity is a difficult task in practice. The aim of this study is to support the engineering design community by easing the difficulty in the problem-exploring practice. To achieve this, a computational problem-exploring (CPE) model is developed to mimic how design engineers identify a valid design problem. Consequently, a CPE tool - Pro-Explora V1 is developed based on the CPE model. The CPE model consists of a synergy of emergent computational technologies including data retrieval and machine learning. A Markovian model is employed in the CPE model to enable a data-driven random process for exploring design problems. In pilot test, Pro-Explora V1 generated some engineering design-related problems which are meaningful, unique, and could not be distinguished from naturally generated ones. It provides support to design engineers in problem-exploring at the early stage in engineering design. This study contributes to the global effort towards data-driven processes in the fourth industrial revolution.