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Item type:Publication, Access status: with files , The key to improving the trade-off between service and inventory levels in make-to-stock production: sequencing and capacity controlSafety stocks play an important role in make-to-stock production, since they ensure supply to customers despite uncertainties in production and demand. A large literature exists on safety stock calculations, but it typically assumes exogenous stationary replenishment times. In most real-world settings, capacity is a constraint leading to non-stationary replenishment times that are under the control of management. In response, we design a new production planning and control approach for make-to-stock production based on cumulative quantity logic and show how companies can use sequencing and capacity control based on current demand to improve customer supply and to reduce safety stocks. Results from discrete event simulations highlight the potential of the new approach and indicate that the proposed manufacturing control reacts robustly against wrongly set planned demand rates. This contributes to theory, while the comparatively low information processing requirements and robustness of the approach make it an important potential solution for practice.Publicationtype: Journal Article17 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Digital assistance systems for just-in-sequence packing: key enablers for productive shipyard assembly(Springer, 2026-09-18); ; ; ; Assembly personnel in one-of-a-kind shipbuilding dedicate only a small proportion of their on-site time to value-adding activities, with non-productive tasks consuming up to 78% of their working hours. Considerable effort is expended on material procurement, information retrieval, and preparation of work sites. In addition, evolving customer requirements and internal planning deviations lead to frequent design changes in the production phase, resulting in substantial rework and elevated scrap rates. This is particularly evident for pipes, fittings, and related accessories, which are integral to numerous onboard systems throughout the vessel. Coordinating their delivery through traditional sequential scheduling results in redundant handling due to time-consuming retrieval processes for pre-manufactured parts by suppliers, and complex on-site material provision by shipyard logistics. A promising approach to overcome these challenges is to schedule frequent, small-batch deliveries in line with construction progress. While synchronizing supplier production and delivery with the assembly progress has proven to be an effective solution in automotive serial production, its application in one-of-a-kind shipbuilding is severely limited by the poor availability and accessibility of up-to-date information. To bridge this gap, this paper examines the picking and packing of installation-ready units by suppliers through the systematic use of prototype digital assistance systems integrated with a consistent database. Ultimately, this approach aims to significantly enhance the productivity of shipyard assembly workers and accelerate overall ship delivery schedules.Publicationtype: Conference Paper49 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Evaluating the robustness of production planning and control proceduresProduction Planning and Control (PPC) procedures are very important for a stable production performance but are traditionally evaluated under the assumption of good input data quality. This reliance creates a vulnerability, as industrial environments frequently suffer from high uncertainties and systematic parameter biases that cause continuous, cumulative deviations from logistical target achievement. To address this research gap, this paper introduces a three-step evaluation framework to assess the structural robustness of PPC procedures. The proposed framework combines qualitative analysis, empirical stress testing via discrete event simulation, and mathematically sound analytical modelling using the throughput diagram. The methodology is successfully applied to evaluate the Planned Output Control (POC) procedure. The results show mathematically and empirically that closed-loop control architectures possess a fundamental advantage over open-loop systems in autonomously mitigating parameter drift. This framework enables researchers and practitioners to systematically benchmark and design robust PPC procedures prior to industrial implementation.Publicationtype: Conference Paper32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Logistic operating curves for closed queueing systems: Enhancing container terminal productivity through shared queuesContainer terminals are vital nodes in global supply chains, linking maritime and land transport. Terminal operators face the critical challenge of balancing vessel handling times and operational costs – a conflict of objectives. Building on our previous work that introduced an engineering-oriented approach using Logistic Operating Curves and an extended Kingman approximation for ship-to-shore crane productivity under fixed vehicle allocations, this paper advances the model to accommodate shared vehicle queues. In such configurations, terminal trucks serve multiple ship-to-shore cranes from a common pool, preserving the closed queueing system characteristics inherent to terminal operations.Publicationtype: Conference Paper31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , PPS-Report 2025 Wirtschaftsstandort Deutschland: Studienergebnisse(Technische Universtiät Hamburg, 2026-09); ; ; ; Der PPS-Report 2025 liefert eine umfassende Analyse der aktuellen Lage produzierender Unternehmen am Standort Deutschland. Die Ergebnisse verdeutlichen, dass die logistische Leistungsfähigkeit – insbesondere kurze Durchlaufzeiten und hohe Termintreue – für fast alle Teilnehmer ein kritischer Wettbewerbsfaktor ist. Im Bereich Energie und Nachhaltigkeit zeigt sich eine deutliche Belastung: Steigende Energiekosten werden von der Mehrheit der Unternehmen als Problem wahrgenommen. Viele Unternehmen ergreifen bereits Maßnahmen, um gegenzusteuern. Auch die Anforderungen an die ökologische Nachhaltigkeit rücken zunehmend in den operativen Fokus. Das Themenfeld Digitalisierung und Konnektivität offenbart Licht und Schatten. Während digitale Assistenzsysteme und intelligente Agenten hohes Potential für die Zukunft versprechen, bilden heterogene Datenmodelle und fehlende Schnittstellen weiterhin signifikante Hürden bei der Umsetzung digitaler Zwillinge und der Automatisierung von Planungsaufgaben. Im Innovations- und Portfoliomanagement kämpfen Unternehmen mit einer steigenden Variantenvielfalt, die zu höheren Rüstaufwänden und Lagerbeständen führt. Hier wird deutlich, dass eine enge Verzahnung zwischen Produktentwicklung und PPS essenziell ist, um die Komplexität am Standort Deutschland beherrschbar zu halten.Publicationtype: Report51 35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Resilience, sustainability and Industry 4.0 in PPC: A survey of german manufacturers(publish-Ing., 2026-09-01); ; ; ; Manufacturing companies face increasing demands related to supply-chain resilience, sustainability, and digitalisation, creating new requirements for production planning and control (PPC). This paper presents selected findings from the 2024 PPC Report, a recurring survey of German manufacturing companies conducted by four production technology research institutes. Based on 39 completed questionnaires, the study provides a descriptive benchmarking snapshot of resilience, sustainability practices, and Industry 4.0 adoption and complements descriptive results with selected bivariate subgroup comparisons. The findings show that most respondents expect their production systems to be similarly or better positioned for future crises than during past disruptions, while no clear relationship with company size emerges. Respondents reporting the use of risk-management approaches predominantly assess their future resilience as stable or improved. Material and energy efficiency are among the most frequently selected measures for reducing the carbon footprint of production, while the reported composition of energy-data practices differs across company-size groups, with operational uses accounting for larger shares among medium-sized and large companies. Regarding Industry 4.0, advanced integration remains limited: mostly or fully connected OPC UA implementations and Asset Administration Shell adoption are still uncommon. An analysis of software use in commissioning further reveals descriptive differences between implementation approaches. Across the three areas, the findings highlight the growing importance of combining organisational practices, data availability, and system integration within PPC. Given the exploratory design and small sample, the results should be interpreted as a benchmarking snapshot rather than as representative of German manufacturing as a whole.Publicationtype: Journal Article32 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Analysing sequencing in make-to-order companiesSufficient order sequencing configuration and implementation are key to achieving high schedule reliability and, consequently, customer satisfaction in make-to-order companies. This paper proposes a multi-method analysis approach to evaluate sequencing in make-to-order production. The approach consists of two phases: (1) data collection, including interviews, an order-level feedback data analysis, the value stream analysis for make-to-order companies, and process observations, and (2) an interpretative analysis to identify configuration and implementation deficits. As part of the feedback data analysis, a rank-based correlation analysis is introduced and validated using simulation data, providing visually accessible insights into sequencing implementation through characteristic correlation patterns. The multi-method analysis is validated in a case study, demonstrating its practical applicability and the benefits of integrating multiple analytical methods.Publicationtype: Conference Paper30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Generalization of LSTM and CNN autoencoders for anomaly detection across orthogonal and longitudinal turningMachine learning models enable automated process monitoring in manufacturing and support adaptive quality assurance and predictive maintenance systems. Autoencoders have emerged as a promising approach to unsupervised anomaly detection in processes. However, their application to turning processes, particularly their generalizability across different process variants such as varying machine kinematics, cutting geometries, and process variability, remains largely unexplored. Existing studies mainly focus on controlled laboratory environments using single machine tools and standardized parameters, limiting industrial scalability. This work addresses this gap by systematically comparing long short-term memory (LSTM) and convolutional (CNN) based autoencoders developed in collaboration with two research projects. The models are trained and evaluated on two harmonized datasets derived from orthogonal and longitudinal turning processes. The datasets differ with respect to machine tools, kinematics, and cutting tools. Each model is trained on both its native process data and on the respective other dataset to evaluate cross-domain performance. The results on normalized reconstruction errors show that LSTM-based autoencoders yield higher temporal sensitivity, whereas CNN-based autoencoders exhibit greater reconstruction stability but reduced sensitivity to transient dynamics. Moreover, relative reconstruction errors are additionally evaluated in the original physical force units to improve process interpretability. This analysis reveals larger relative deviations for LSTM models due to their heightened sensitivity. Furthermore, the variability of the training dataset is identified as a key factor influencing out-of-distribution generalization performance. The findings demonstrate the generalizability of autoencoder based monitoring approaches across different cutting conditions and provide insights for data-driven and grey-box modeling frameworks in manufacturing.Publicationtype: Journal Article29 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Assignment of orders in horizontal transport at sea-port container terminals(Springer Nature Switzerland, 2026-07-09); ; ; ; Quay cranes (QCs) represent the primary operational bottleneck at seaport container terminals due to their high capital intensity and role as critical nodes in global supply chains. Their productivity is strictly dependent on the efficient coordination of horizontal transport, specifically the assignment of transport orders to terminal trucks (TTs). While many terminals still rely on fixed TT-to-QC assignments, this approach often lacks the flexibility to handle disruptions and fluctuating workloads. This paper investigates the potential of a flexible, multi-criteria assignment procedure – considering work-in-process, travel distances, and QC waiting times – compared to traditional fixed assignments. The performance is evaluated using Logistic Operating Curves (LOCs), which map QC productivity against the number of deployed TTs. The results demonstrate that flexible assignment significantly outperforms fixed assignment in underload scenarios (low TT availability or high travel speeds), showing productivity gains of up to 17.85% during vehicle disruptions. However, this advantage diminishes as transport capacity increases. In high-utilization overload zones, the flexible method falls slightly below fixed assignment performance. This discrepancy is attributed to the timing of order assignment; making decisions when a previous task is completed, rather than at the last possible second, limits the system’s ability to react to real-time fluctuations. The study concludes that flexible assignment increases robustness during transport shortages, but achieving theoretical productivity benchmarks requires shifting toward late-assignment strategies.Publicationtype: Conference Paper74 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Feeling machines: Prediction of thin-walled workpiece deflection by monitoring variations in tool bending moment pattern during circumferential milling(Elsevier, 2026-06-30); ; ; In circumferential milling, the in-process deflection of thin-walled workpieces induced by feed normal forces results in reduced material removal, leading to geometric errors after machining. With the increasing use of additive manufacturing technologies and the production of near-net-shape structures, this problem is gaining increasing importance. Various strategies attempt to compensate for this geometric errors, for example by modeling the occurring cutting forces and workpiece deflection and then applying countermeasures during machining. However, this requires a high level of process understanding, and multiple parameters need to be considered, including material properties, cutting edge geometry as well as wear state of the tool. This paper examines a novel approach to predict workpiece deflection state by analyzing the bending moment pattern measured by an in-process sensor system located in the tool holder. It could be demonstrated that features extracted and evaluated from the bending moment signal can represent the current deflection state of the workpiece in an offline approach, enabling future in-situ toolpath adaptation to reduce geometric errors without relying on time-consuming simulation of the workpiece stiffness and with minimal signal processing effort.Publicationtype: Journal Article38 16