Michael Henke’s research while affiliated with Fraunhofer Institute for Material Flow and Logistics and other places

During the COVID-19 pandemic, many medical device manufacturers faced a significant and unexpected increase in demand. To handle this sudden spike in demand, a ramp-up of production and inbound material flow had to be organized. At the same time, the pandemic caused issues in the supply of materials for their medical products. Due to disruptions in global transportation, local unavailability of parts, and the urgency of the demand, companies established public–private partnerships to allow the organization of sufficient part supply for the production of medical products. In order to engage in the public–private partnership and to specify the necessary support, it is required to identify bottlenecks within the supply chain and options to address them. One of the methods commonly used to specify bottlenecks in a dynamic environment is event discrete simulation. To support the sudden simulation requirement by companies, a generalized simulation model is set up as a service to support, in particular, small and medium enterprises. The service allows to identify part bottlenecks and evaluate options in a rapid mode. The resulting service enables companies to address supply chain bottlenecks to ensure a fast ramp-up of production.

In the wake of technological innovation, supply chain transparency has become a concrete avenue for long-awaited improvements to supply chain processes and practices. Transparency is of particular interest to Supply Chain Finance; however, it is yet empirically unknown whether and how benefits would materialise. Being at the interface of finance and operations, Supply Chain Finance aims at improving the inter-company management of financial flows. Nowadays, different financing schemes are offered foremost by cooperation between financial institutions and fintechs. Uncertainty remains on whether and how supply chain transparency would benefit these schemes to allow the adoption of more sophisticated, higher-value schemes. By applying the Gioia method, we analysed a series of interviews with experts from the Supply Chain Finance industry regarding transaction cost economics, agency theory, and dynamic capability. The resulting series of pragmatic, theory-grounded but practice-inspired propositions untangle the relationship and barriers between supply chain transparency and Supply Chain Finance. The propositions provide advances in the theoretical understanding of Supply Chain Finance and uncover relevant directions for future research. This poses the base for actionable results in future research and leads to an impact in research by addressing the concerns and tensions raised by Supply Chain Finance experts.

A significant challenge in bootstrapping a jointly used infrastructure such as Data Spaces is to incentivize the participants to invest in setting up the infrastructure. In this chapter, we investigate this challenge and possible solutions, focusing on an approach called “Tokenomics.” The incentivization scheme should be utilized by governance frameworks, in which the participants of Data Spaces remain capable of action and independent through automated, effective, and fair decision-making processes. Also, potential participants should be motivated to participate in the establishment and further development of the system, while on the other hand, undesirable behavior should be penalized. In combination with distributed ledger technology (DLT) and machine-readable, legally compliant smart contracts, participant behavior can be affected in such a way that both data quality and quantity are improved for the whole Data Space. To derive possible design options for Tokenomics approaches, we examine different token frameworks and their impact on participants. The investigation of the frameworks is carried out taking into account five significant domains: technical, behavior, inherent value, coordination, and pseudo-archetypes. Furthermore, we investigate which token designs provide smaller or larger incentives in order to join or maintain a DLT-based ecosystem.