Engineering changes (ECs) are inevitable for businesses due to increasing innovation, shorter lifecycles, technology and process improvements and cost reduction initiatives. The ECs could propagate and cause further changes due to existing system dependencies, which can be challenging. Hence, change management (CM) is a relevant discipline, which aims to reduce the impact of changes. EC assessment methods form the basis of CM that support in assessing system dependencies and the impact of changes. However, understanding of which factors influence the changeability across value chains (VCs) is limited. This research adopted a VC approach to EC assessment. Dependencies in products and processes were captured, followed by risk (i.e. likelihood x impact) assessment of ECs using change prediction method (CPM). Four industrial case studies were conducted (3x automotive, 1x furniture manufacturing) to identify design (product) and manufacturing (process) elements with high risk to be affected by ECs. Based on the case results, characteristics were identified that influence changeability across VC. This contributed to the CM domain while businesses could also use the results to assess ECs across VC, and improve the design of products and processes by increasing their changeability across VC e.g. by proactive decoupling or reactive handling of system dependencies.

In the ESA Support to Science Element GOCE+Antarctica, we study the influence of the lithospheric structure on estimates of GIA. From recent geophysical, especially seismological, studies new insights on the deep structure of the Antarctic continents are available. However, the seismological models differ in resolution and do not provide a consistent image of the lithosphere. This is critical in analysing the feedback between the lithosphere and glacial loading or unloading. To reduce such ambiguities, we combine the latest seismological models with gravity gradient data derived from the GOCE satellite mission. The gradients are in particular sensitive to the geometry and density variations of the main lithospheric layers, i.e. ice and sediment thickness, the Moho depth and the temperature and composition of the upper mantle. Initial results indicate that differences exist in the mode of compensation for West and East Antarctica related to different mantle properties. The impact of an improved lithospheric model on GIA modelling is estimated by testing the sensitivity to the new temperature and density distribution and by comparing 1D and 3D viscosity models, especially in areas of low viscosity as in the Amundsen Sea sector.