TY - CHAP A1 - Chen, Mei-Hui A1 - Bremer, Peik A1 - Schmitt, Jan A1 - Tsai, Kune-muh T1 - Reentrant Flow Shop Production Control with the Theory of Constraints for Wafer Fabrication Having Parallel Machines T2 - Proceedings of the 28th International Symposium on Logistics (ISL 2024) Building Sustainable Connectivity through Logistics and Supply Chains N2 - Purpose of this paper: In a production line with capacity constraint resources (CCR) or bottleneck stations, the production control and the throughput rate are determined by the CCR station. In this study, we consider a production system with parallel machines and there are many rounds of reentrants during the production processes, and can be regarded as a reentrant flow shop process (RFS). In most RFS systems, the release of jobs is a push type without considering the WIP in each layer of reentrance. To resolve the excessive WIP in an RFS system, we consider the theory of constraints (TOC) and implement the DBR (drum, bottleneck and rope) to model RFS scheduling and to define appropriate buffer sizes at the CCR stations of diverse reentrant layers. Design/methodology/approach STS: We obtain production data of a type of semiconductor chip where the photolithography process is the most frequent reentrant. We designed nine scenarios based on the current RFS of the case company by applying the TOC and considering the time differences in parallel machines. A simulation model for the photolithography process was established using AutoMod simulation. Thirty simulation runs were performed for the mean and standard deviation. The goal is to identify the best scenario among the nine proposed ones. Findings: The simulation results demonstrated that Scenario 9, which involves dynamic buffers and assigning higher priority to products with more processing reentrance layers, achieved the highest throughput of finished products. The throughput of finished products in Scenario 9 is 22% higher than in the base scenario. It also shows improvements in maximum product stack-up and input-output ratio compared to the base scenario. Value: The paper studies dispatching rules for reentrant flow lines having parallel machines with data from the semiconductor industry, and this type of production system is common for producing a wide range of electronic devices. With limited buffer sizes on the shop floor, the number of WIPs is significant, especially for reentrant flow shops, to avoid being clogged with too many WIPs. Implementing DBR modeling from TOC, the nine scenarios demonstrate the effects of controlling buffer sizes on system performances. Research limitations/implications: The nine scenarios are designed based on the case company situations and may not be readily implemented in other industries. Moreover, the DBR modeling of the buffer sizes and CCR station schedule control should also be adjusted when employed in other applications. KW - Flow Shop KW - Production Control KW - Theory of Constraints KW - Semiconductor Y1 - 2024 SN - 978-0-85358-354-7 ER - TY - CHAP A1 - Meyer, Maximilian A1 - Pechtl, Lin A1 - Bremer, Peik A1 - Schmitt, Jan T1 - Towards position-based technologies for digitized process management on the shop floor T2 - Proceedings of the 24th International Symposium on Logistics - Supply Chain Networks vs Platforms: Innovations, Challenges and Opportunities, Würzburg N2 - Position-based technologies, e.g Ultrawideband (UWB), WiFi or Bluetooth, for indoor localisation purposes are already commercially available. With the highly increasing digitalization of industrial processes, the potential of these technologies comes into focus of process management research. This paper aims to structure position-based technologies according to their potential to support shop-floor process management. For this purpose, a framework with the following dimensions is developed: technical performance properties of the IPS, economical aspects, relevant use-case requirements, and appropriate data aggregation strategies. Following this, two representable use-cases show the implementation of an indoor-positioning-system and the data aggregation in order to derive relevant process data from position data. Y1 - 2019 UR - https://isl21.org/wp-content/uploads/2020/04/Full-paper-20_07_2019-Final-V2.pdf SP - 232 EP - 241 ER -