Resilience in supply chains - How additive manufacturing enables a resilient supply chain

Resilience - the ability to deal with crises and recover from their effects as quickly as possible - has been glorified since the COVID-19 pandemic as the new miracle cure against the effects for disruptions that occur in the future. Especially for Germany as an export-oriented location, the resilient design of supply chains is an economic success factor. However, a strategic anchoring of resilience thinking in management as well as the use of future-oriented technologies are necessary to harness the potentials of a robust, agile, adaptive and integrative supply chain. Additive manufacturing, due to its digital "DNA" and great design freedom, has the potential to more efficiently create or drive supply chain resilience. Redundancy due to inventories, for example, becomes obsolete due to the location-independent, flexible production of required products without long start-up times on the basis of computer-aided design files. Companies in a supply chain also do not have to bear the investment risk for additive manufacturing machines due to new, data-based business models. For many manufacturing companies and entrepreneurial alliances in the form of a supply chain, the question is therefore increasingly whether additive manufacturing technology can be increasingly used as an instrument to increase resilience along the supply chain in the future. The study "Resilience in Supply Chains - How Additive Manufacturing Enables a Resilient Supply Chain" sheds light on this economic and also ecologically valuable question and presents potential data-driven business models for the technology sector.

Resilient supply chain network design under disruption and operational risks

Nowadays, supply chain resilience has drawn widespread attention from academics and practitioners due to the high likelihood of operational risk and the destructive consequence of disruption risk. However, the studies on resilient supply chain design considering these two types of risks are limited. Furthermore, how to quantify the uncertainty arising from the lack of historical data in the planing stage is not sufficiently studied. Aiming at these problems, this paper presents two uncertain programming models that optimize the strategic decisions before disruptions and supply chain operations after disruptions. The presented models introduce p-robustness measure to bound the cost in disruption scenarios. Besides, uncertainty theory is adopted to handle parameter uncertainty in the absence of historical data. Later, these two programming models are converted into their corresponding deterministic equivalents, which can be solved by cplex. Finally, we illustrate the validity and feasibility of the proposed models and explore the impact of critical parameters on the optimal solution by implementing a series of randomly generated instances and a practical case. The observations may provide some interesting managerial insights for decision-making in reality.