Modelling Sociotechnical Aspects of R&D in the Dutch High Tech Equipment Industries

Nowadays high-tech companies are struggling to remain competitive given the high pace in which the market complexity increases and customer demands change. With the human Research and Development (R&D) resource pool shrinking, and company-specific knowledge scattered across individuals, there is a strong focus on technological solutions to manage these challenges. For instance, generative Artificial Intelligence (AI) can be a game-changer, promising more predictive and efficient product development. However, introducing and embedding technologies like AI will have consequences beyond the technical realm. Traditional systems engineering approaches—highly technical, linear, and reductionist—can no longer cope with the intertwined technical, human, and organizational challenges faced by modern high‑tech ecosystems. To remain effective and relevant, organizations need to integrate sociotechnical principles into their innovation processes. To do so, it is important to understand the origins of the raised issues and the consequences of technical solutions from various perspectives. The approach we adopted is to consider R&D and the resulting products in the high-tech equipment industry as complex systems, in particular sociotechnical systems. This means we explicitly consider technical systems, organization, processes, human factors, and their interrelations, as a whole. Using this approach, we have investigated threechallenging issues at R&D departments of large companies:
· How to improve the Systems Engineering - Software Engineering interplay?
· How to improve engineering model usage and model management?
· How to support a complex and highly regulated industrial workflow using AI?
We have found that investigating these complex issues that are experienced by multiple companies, requires participation of all involved stakeholders from thesecompanies. The investigation itself is already an interventionas multiple perspectives are put next to each other, contested, and refined, and starts the thinking process by those involved. The causal modelling techniquewe applied delivered a deeper, shared understanding of why recurring symptoms persist. However, the technique demands quite some time, attention, and facilitation. It is cognitively demanding at the start, and forms a stepping stonetowards successful interventions and changes in organizational structures and architectures.