Supply Chain Innovation

About the lab

With our research, we target supply chain being a sustainable long-lasting competitive advantage in our domain. Major topics cover knowledge management, advanced data analytics, artificial intelligence, discrete event-, agent based- and system dynamic simulation, modelling, digitalization, process mining and automation, solving supply chain problems with quantum annealing and universal computing, and applying semantic web technologies for semantic data integration. We provide education to our peers, optimization solutions to operations experts in practice and at the same time establish and maintain strong relationships to academia. We are involved in multiple cross-industry and cross-domain funded projects that drive digitalization efforts in Europe and the semiconductor industry.

Featured projects (11)

SC³ (Semantically Coordinated Semiconductor Supply Chains) aims at streamlining the complex semiconductor industry: There’s nothing simple about today’s semiconductor industry. With several hundred processing steps involved in manufacturing, it is considered to be one of the most complex industries. Moreover, the supply chain is complicated by low-level linearity, short product cycles and long throughput times. The EU-funded SC3 project aims to reverse this trend by enabling a collaboration of industrial and academic stakeholders to encourage interoperability between semiconductor companies and further industrial domains. To that end, it will develop a framework to ensure an agile development, validation and refinement loop for top-level ontologies such as digital reference, which consists of a combination of different ontologies of semiconductor supply chains and supply chains containing semiconductors. To deliver the demonstrators needed as proof-of-concept, the project will follow a piloting methodology.
Integrated Development 4.0 Digitalization and Industry 4.0 are enablers of fundamental business innovation and disruption. By closely interlinking development processes, logistics and production with Industry 4.0 technologies, iDev40 achieves a disruptive step towards speedup in time to market. By developing and implementing a digitalization strategy for the European electronic components and systems industry a “breakthrough change” is initialized. Integrated Development 4.0 leads the digital transformation of singular processes towards an integrated digital value chain based on the “digital twin” concept. Development, planning and manufacturing will benefit from the “digital twin” concept in terms of highly digitized virtual processes along the whole product lifecycle.
The Arrowhead Tools project aims for digitalisation and automation solutions for the European industry, which will close the gaps that hinder the IT/OT integration by introducing new technologies in an open source platform for the design and run-time engineering of IoT and System of Systems. The project will provide engineering processes, integration platform, tools and tool chains for the cost-efficient development of digitalisation, connectivity and automation system solutions in various fields of application.
In a cooperation between Germany and Austria, universities, companies and public authorities are conducting research to make food production and food logistics more resilient by using Distributed Ledger Technology. In NutriSafe, technologies, data models, service architectures, business processes and business models are developed on the basis of Blockchain technology, supplemented by legal and security considerations and made available as modular toolkit under an Open Source license. Further topics are resilience of the supply situation in a metropolitan region, identity management, serious games and resilience analyses of supply chains in food production and food logistics.
BRAINE (Big data pRocessing and Artificial Intelligence at the Network Edge) provides a new vision for utilizing edge resources by providing novel network-edge workload distribution schemes. Predicting resource availability and workload demand, identifying trends, and taking proactive actions are all aspects of the novel workload distribution. The workload distribution technology developed in the context of BRAINE can be transferred to many other edge/fog computing environments to achieve different goals. Last but not least, BRAINE will have an important positive impact on the environment. Through BRAINE, edge computing can reduce this projected energy consumption by offloading many of the AI functions next to the end-users.

Featured research (98)

Latest governmental policies aim to mitigate the carbon impact on climate and accelerate the transition towards carbon neutrality by imposing stronger regulations for companies. The semiconductor industry emits carbon dioxide caused by its large amounts of consumed energy. At the same time, machine sensors tracking consumption are rare, and the share of fixed and variable energy consumption is often unknown. To detect the individual energy consumption types of a wafer fab, a process-and infrastructure-oriented discrete-event simulation model is developed that serves as a tool to determine the plant energy consumption within a fab. The obtained shares are validated with existing data. In parallel, a novel enery efficiency curve is constructed and verified by extending the concept of the well-studied Operating Curve. It incorporates the relationship between utilization and energy efficiency and adds an ecological viewpoint to the so far only economically motivated concept.
COVID-19 pandemic, in the past 2 years, has affected all aspects of life, as well as businesses with different extents. The fifth wave, pushed by the omicron variant, seems to pave way to a new course of development. In this study we present a hybrid simulation modelling approach using agent-based simulation (AB) and system dynamics (SD). This hybrid model is used to evaluate the pandemic dynamics and its impact on the supply chain (SC) of a semiconductor company. We modelled the infection waves, governmental stringency values, and their impact on demand for several semiconductor applications. Additionally, we simulated vaccination, mutation factors and other recent developments of the pandemic. The results of the epidemiological model show that while the COVID-19 evolved in multiple waves, government restrictions and vaccinations are keys to control the spread of the virus. Moreover, the possible endemic nature of the pandemic fuels the importance of the continuation of our work, as this work will be the backbone of a SC risk management framework: resilient SCs need to be equipped with mitigation measures, to face future challenges. The results of the SC model suggest that mitigation of the COVID-19 disruption could be achieved by having high inventory and/or high global flexibility capacities.
COVID-19 has posed unprecedented challenges to global health and the world economy. Two years into the pandemic, the widespread impact of COVID-19 continues to deepen, impacting different industries such as the automotive industry and its supply chain. This study presents a hybrid approach of simulation modeling and tree-based supervised machine learning techniques to explore the implications of end-market demand disruptions. Specifically, we apply the concept of born-again tree ensembles, which are powerful and, at the same time, easily interpretable classifiers, to the case of the semiconductor industry. First, we show how to use born-again tree ensembles to explore data generated by a supply chain simulation model. To this end, we demonstrate the influence of varying behavioral and structural parameters and show the impact of their variation on specific key performance indicators, e.g., the inventory level. Finally, we leverage a counterfactual analysis to identify detailed managerial insights for semiconductor companies to mitigate adverse impacts on one echelon or the entire supply chain. Our hybrid approach provides a simulation model enhanced by a tree-based supervised machine learning model that companies can use to determine optimal measures for mitigating the adverse effects of end-market demand disruptions. We close the loop of our analysis by integrating the findings of the counterfactual analysis backward into the simulation model to understand the overall dynamics within the entire multi-echelon supply chain.
The purpose of this study is to investigate interface management approaches within supply chains that are under disruption, to identify optimization potentials and to develop a framework to improve the dyadic relationship management in supply chains concerning disruptions. This study concentrates on interfaces, i.e. dyadic relationships, within semiconductor supply chains under disruption of the COVID-19 pandemic. It consists of case research at a large European semiconductor manufacturer preceded by a systematic literature review, revealing mechanisms, outcomes and coping strategies during the pandemic from a semiconductor manufacturer's point of view.

Lab head

Hans Ehm
  • Supply Chain Innovation
About Hans Ehm
  • My research interest is in semiconductor supply chains and supply chains containing semiconductors. I am using methods like discrete event-, agent based- & system dynamics simulation, complexity management, semantic web and ontologies, Machine Learning, AI and Human AI interaction as well as Quantum Annealing and Universal Quantum Computing. I am heading supply chain Innovation of Infineon. I am active at associations, universities and companies related to my research around the globe.

Members (6)

Thomas Ponsignon
  • Infineon Technologies
Nour Ramzy
  • Leibniz Universität Hannover
Abdelgafar Ismail
  • Infineon Technologies
Patrick Moder
  • Infineon Technologies
Natalie Gentner
  • Infineon Technologies
Marco Ratusny
  • Munich University of Applied Sciences