Conference Agenda

Digitalisation in manufacturing refers to integrating digital technologies and solutions into various aspects of the manufacturing industry. It involves digital tools, data, and automation in projects to optimise and enhance processes, improve productivity, increase efficiency, and enable better decision-making. Effective project risk management encompasses identifying risk factors, analysing their potential impact, and implementing strategies to mitigate, avoid, transfer, or accept risks as appropriate. While risk factors are typically associated with potential negative impacts on a project, they can also present opportunities or advantages that, if properly managed, can lead to positive outcomes or benefits. In this sense, risk factors can be viewed as opportunities rather than solely as threats. Risk factors associated with people in organisations refer to potential issues, challenges, or uncertainties arising from the organisation's human aspect. Understanding and effectively managing these risk factors are essential for maintaining a productive and positive work environment and achieving organisational goals. The main objective of the present study has been to systematically identify, analyse, and leverage the opportunities arising from the human aspect of the digital transformation process in an organisation. For that, a literature review has been conducted, investigating digitalisation challenges with a transdisciplinary mindset, ultimately leading to recommendations for industrial partners. This assessment aims to proactively create awareness of opportunities associated with the workforce during digitalisation initiatives. Identifying and addressing these factors will enable organisations to maximise the benefits of digitalisation and foster a positive and empowered workforce.

Digital technologies have the potential to deliver significant economic, environmental, and societal benefit, however, their adoption poses significant transdisciplinary (TD) challenge for industry. Given the multiplicity of digital solutions the question is which better meets the needs of industry, its workforce, society and sustainability. Digital adoption decisions are complex and require participatory support to be TD, as digitalisation impacts societal groups comprised of different and divergent perspectives. In this paper we use the legitimacy of technology as a solution to support TD discussions and decisions. A legitimacy lens helps divergent questioning of the acceptance of norms and values for societies, public bodies, or organisations. We present a set of discussion cards, designed to structure conversations to evaluate the legitimacy of digital technologies in a workplace setting. Our preliminary study uses a workshop, with 9 students from differing disciplinary backgrounds, exploring topics related to the adoption of “Chat GPT” technology. Evidence collected from this workshop is used to re-evaluate the design and the usefulness of the discussion cards in enabling TD discussions. The results show that using the legitimacy cards supports holistic discussion and guides the topics considered, but the time needed to conduct a holistic discussion was vastly underestimated. Conclusions find the language on the cards needs to be simplified and use more examples for guidance, together with allowing more time to discuss only key topics. Future work looks to incorporate learning from this workshop to redesign the cards, then re-evaluate them with industry participants.

Transdisciplinary engineering bridges the gap between diverse fields of expertise, fostering innovative solutions to complex problems. This study investigates the synergy of human expertise and artificial intelligence (AI) in the field of electricity prediction. The accurate forecasting of electricity demand is a critical component of sustainable engineering practice, particularly in energy-intensive economies like Japan. This research tackles the pressing challenge of utilizing algorithms for future electricity demand forecasting by AI-driven predictions. This research explores the efficacy of a transdisciplinary human-AI collaborative approach in enhancing the precision and reliability of electricity demand forecasts, considering the shortcomings of algorithmic predictions in dealing with sparse datasets and unforeseen events. By revisiting the actual past cases Tokyo electricity demand prediction and providing participants test subjects with foundational industry knowledge and an interactive data analysis interface, the research collects comprehensive data throughout the exercise from both technical and non-technical individuals in the forecasting task in the user experiments. The findings indicate that human-AI collaboration can significantly refine forecast accuracy under certain conditions. A notable enhancement is observed when AI predictions are constrained by data limitations and unexpected events. Additionally, individuals with strong technical backgrounds excel in augmenting AI forecasts, although the risk of human-induced biases and over-adjustments presents a challenge. Confirming the benefits of a human-AI collaborative model, we identify potential strategies for AI to better support human decision-making in energy engineering.

As the proportion of older adults increases globally, raising healthy life expectancy is becoming an urgent issue. To address this, shifting the medical system from conventional disease treatment to disease prevention, early diagnosis, and early treatment is desirable. It is necessary to utilise digital technologies not only to acquire and visualise health-related data, but also to link it to people’s behavioural changes. However, there is a limit to the extent that individuals can change their behaviour and maintain their health through their own efforts. A social system is required to achieve behavioural changes with the support of the surrounding environment. This should be a platform service encompassing various stakeholders. In this study, we perform a system design on how to construct a behavioural change platform (BCPF) service to promote behavioural changes based on the support of the surroundings, and how to make it into a platform service. First, the system structure of the BCPF service is described using object process methodology to identify main objects, processes, and system boundaries. Second, customer value chain analysis is conducted to identify the primary value streams among service providers and users of the BCPF service as stakeholders. Finally, the number of BCPF service users over time is simulated using system dynamics. We find that the number of BCPF service users effectively increases by improving vital data which directly related to the reduction of the morbidity rate of target diseases and by conducting appropriate promotions that lead to the reputation of this service.