Qualitative System Dynamics Cycle Network of the Innovation Process of Product Service Systems

Abstract

The innovation process of Product Service Systems (PSS) is affected by a vast number of internal and external influences. Especially the management of timely or structurally repeating influences, so called cycles, shows a great potential for improving the innovation process. Thereby two types of cycles can be differentiated. Firstly, internal cycles during the innovation process itself going from engineering change cycles, to manufacturing resource cycles or even team building processes. Secondly, the external, environmental cycles that have an impact on the innovation process, i.e. government-and customer-related demands and dependencies. A lot of these dependencies, most likely external ones, contain uncertainties, that have to be handled for successful innovation of PSS.To cope with these struggles, methods and tools have to be developed, to allow analysis and forecast of the cycles in the innovation process. Especially the high degree of cross-linking between the different cycles indicates the need of integrated modeling and analysis. An interdisciplinary cycle network of 30 relevant cycles and external influences as well as 51 interconnections so far, was set up in a System Dynamics environment. Though not all influences can be determined yet, the created causal loop diagram can already serve as a framework for analyzing the innovation process of PSSs and support deeper understanding of the interdisciplinary interdependencies.

Nurture: A novel approach to PSS-rebound effect identification

“…System dynamic is one of PSS's three effective modelling methods (Weidmann et al, 2015). While Grüneisen et al (2015) have attempted to represent PSS in system dynamics to enhance the knowledge of PSS by combining multiple multidisciplinary fields, further research is required. Lee et al (2012) employed system dynamics (SD) to examine the dynamics from a triple bottom line (TBL) perspective to cover the multidimensionality of PSS sustainability, and their findings were positive.…”

Review: Model-based Systems Engineering and Artificial Intelligence for Engineering of Sustainable Systems – What contribution can systems engineering and artificial intelligence provide for the engineering of sustainable systems as of today?

“…The model considers 30 cycles and can be used to support decisions. The model contains a "Repair" and a "Recycle" cycle (Grüneisen et al, 2015). On this basis, further mutually influencing cycles could be created to approximate the optimal product design.…”