The Industry 4.0 vision is a frequently discussed topic in manufacturing enterprises in Europe, Asia, and North America. It is expected that advanced technologies such as Cyber-Physical Systems, Internet of Things, cloud computing, and big data technologies enable the emergence of smart manufacturing systems. A smart factory promises to bring transparency to manufacturing facilities by integrating technological advances in computer networks, data integration, and analytics. At the same time, critical questions are asked related to the benefits of Industry 4.0. It is mainly criticized that the requirements and consequences of Industry 4.0 regarding future production planning and control strategies are not fully understood or not even taken into account in the overall Industry 4.0 conception, i.e., many of key decision processes are not included.

The semiconductor industry is capital intensive. The manufacturing process is very complex due to reentrant flows in combination with very long cycle times and multiple sources of uncertainty. This industry is an extreme field for production planning and control solutions from an algorithmic point of view, as well as from a software and information systems point of view. The degree of automation was always – and is still – high compared to other industries. On the one hand, one can argue that in wafer fabs elements of smart manufacturing are already realized, namely most of manufacturing information is available in real-time, the manufacturing process is paperless, lots can be uniquely identified and located, and collaborative human-machine interaction exists. On the other hand, there are significant differences in automation efforts related to manual work-intensive industries such as automotive or aircraft manufacturing where assembly operations are performed in flow lines. In addition to shop-floor control concerns, supply chain management problems have become more and more important which necessitate a horizontal integration of the semiconductor supply chain and digital transformation for the industry ecosystem.

The major objective of this Dagstuhl Seminar is related to developing a research agenda for making smart semiconductor manufacturing decisions and the information systems to empower flexible decisions for smart production. The research agenda will be developed around the following two main topics:

Topic 1: Novel decision-making approaches that exploit the huge amount of available data and orchestrate the interrelated decisions

Topic 2: Future information systems for decision support and facilitating digital transformation

This includes innovative modeling approaches for supply chain planning and more detailed production planning and scheduling in semiconductor manufacturing and an analysis of requirements for next-generation information systems that support such decisions.

One of the expected outcomes of the seminar consists of developing a significant draft of a concept for a simulation testbed which allows for assessing smart planning and control decisions in the semiconductor industry. The purpose of this seminar is to bring together researchers from different disciplines including information systems, computer science, industrial engineering, supply chain management, data science, and operations research whose central interest is in decision-making for smart semiconductor manufacturing. Moreover, practitioners from the semiconductor industry who have frequently articulated their perception that academic research does not always address the real problems faced by the industry will bring in their domain knowledge to make sure that progress towards applicability and feasibility will be made during this seminar.

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Chen-Fu Chien, Hans Ehm, John Fowler, and Lars Mönch

• Dominik Bisslich (Infineon Technologies AG - Neubiberg, DE)
• William Bitsch (WHU - Vallendar, DE)
• Stéphane Dauzère-Pérès (Mines Saint-Etienne, FR) [dblp]
• Hans Ehm (Infineon Technologies - München, DE) [dblp]
• John Fowler (Arizona State University - Tempe, US) [dblp]
• Michael Hassoun (Ariel University, IL) [dblp]
• Jessica Hautz (KAI - Villach, AT)
• Cathal Heavey (University of Limerick, IE) [dblp]
• Raphael Herding (Westfälische Hochschule - Bocholt, DE) [dblp]
• Young Jae Jang (KAIST - Daejeon, KR) [dblp]
• Adar Kalir (Intel Israel - Qiriat-Gat, IL) [dblp]
• Rohan Korde (Arizona State University - Tempe, US)
• Peter Lendermann (D-SIMLAB - Singapore, SG) [dblp]
• Andrea Matta (Polytechnic University of Milan, IT) [dblp]
• Lars Mönch (FernUniversität in Hagen, DE) [dblp]
• Giulia Pedrielli (Arizona State University - Tempe, US) [dblp]
• Thomas Ponsignon (Infineon Technologies - München, DE) [dblp]
• Alexandru Prisacaru (Bosch GmbH - Stuttgart, DE) [dblp]
• Oliver Rose (Universität der Bundeswehr - München, DE) [dblp]
• Henrik Schmielau (Infineon Technologies - München, DE)
• Daniel Schorn (FernUniversität in Hagen, DE) [dblp]
• Masha Shekari (Polytechnic University of Milan, IT)
• Liji Shen (WHU - Vallendar, DE) [dblp]
• Marcel Stehli (Globalfoundries - Dresden, DE) [dblp]
• Gian Antonio Susto (University of Padova, IT) [dblp]