Matthias Schmidt

Environmental sustainability, in particular Life Cycle Assessment (LCA), is increasingly recognized as an important factor in holistic factory design. As it requires a lot of data, Building Information Modelling (BIM) is progressively seen as an important data source for building LCA. However, factory design is a very interdisciplinary process and different factors such as factory layout, energy system and building structure influence the most sustainable factory design. Therefore, the requirements for an appropriate BIM-LCA strategy differ from other building types. This paper examines current strategies for BIM-LCA integration to assess their suitability for the factory design process, with a particular focus on the specific challenges faced by small and medium-sized enterprises (SMEs). In order to achieve this objective, the requirements of the integral factory design with regard to an optimal integration strategy are identified. A literature review serves as the methodological basis for this analysis, providing insight into existing approaches, their strengths and limitations. The findings indicate that current BIM-based LCA strategies do not adequately address the specific requirements of factory design, revealing substantial usability and integration issues. A potential solution is an openBIM approach with semi-automated processes and real-time feedback, which could enhance usability for SMEs and support holistic lifecycle evaluations.

Manufacturing facilities are becoming increasingly vulnerable to erratic shocks and uncertainties that have the potential to exert disruptive effects on operations. Global crises, such as the COVID-19 pandemic, the energy crisis, and the Suez Canal blockade, highlight the vulnerability of value networks that extend beyond company boundaries. In the absence of effective strategies to address these disruptions, manufacturing companies may face the risk of losing their competitive edge. While flexibility may suffice for minor risks, more substantial impacts necessitate a significant degree of adaptability and robustness. The promotion of organizational resilience, defined as the capacity to adapt to evolving circumstances swiftly, constitutes an ongoing strategic process that is pivotal to the effective creation of operational value. This publication delineates the methodological approach of the PARMa research project. Within the research project, the vulnerability of manufacturing companies to internal and external uncertainties is being addressed. Moreover, PARMa aims to strengthen the adaptability of production companies through proactive resilience management. In the event of sudden shocks, this should ensure rapid and targeted action, prevent operational disruptions and maintain competitiveness.

The transition to a circular economy is widely regarded as a promising approach to promoting sustainable economic practices. Within this context, business model innovation plays a pivotal role in the implementation of circular strategies. However, practical adoption remains limited due to challenges in translating conceptual frameworks into practices. A critical barrier is the lack of frameworks for quantitatively assessing the feasibility of circular business models during their development. In this regard, evaluating economic viability constitutes a crucial component. Important in this process is the ability to forecast product returns in terms of volume, timing, and condition, which must be taken into account in remanufacturing processes. This is particularly relevant for factory planning and design, as accurate product return forecasts are essential for appropriately dimensioning and optimizing production systems. Consequently, such forecasts support the quantification of investment costs arising from circular production systems in the context of circular business models. Therefore, a successful transition to a circular economy requires a comprehensive approach that integrates the business model perspective, product return forecasting, and factory planning. The interdependencies between these elements are discussed in the present paper, which also derives a method for quantifying factory-related investments.

Sequence‐dependent setups in job‐shop manufacturing create a trade‐off: raising productivity through setup‐optimized sequencing tends to reduce schedule reliability. This study presents a modeling approach that quantifies this trade‐off at the workstation level. Building on the production operating curve and the boundary case with many setup families, the approach links sequencing effects to mean work‐in‐process (WIP) and utilization. The model yields expressions for the performance‐based and the effective productivity gains, which are bounded by the relative setup share and can only be realized during active production time. It also provides an expression for the sequence‐induced standard deviation of relative lateness as a function of WIP and utilization. Sensitivity results show proportional dependence on the setup share and diminishing gains with increasing numbers of setup families, while a two‐point comparison demonstrates the trade‐off. The model provides consistent metrics for positioning workstations within the productivity–schedule‐reliability trade‐off and for supporting sequencing and WIP‐related operating decisions.

Shorter product life cycles and an ever-growing variety of product variants require manufacturers to perform production ramp-ups more frequently. These ramp-ups, as well as the subsequent series production, are becoming increasingly complex. The associated productivity losses, especially during ramp-up, threaten companies’ competitiveness and must therefore be minimized. In largely manual processes, worker competence is the key lever for reducing these losses: the speed and quality with which workers perform a task depend primarily on their individual skills. This paper presents a methodology that integrates established learning- and forgetting-curve models to predict individual competence development as a function of the production program. Unlike existing approaches, it primarily focuses on the individual task-based execution time of one worker. Based on this, execution times can be examined in detail at the task, product, and workstation levels. The proposed methods and visualizations provide valuable insights for actively managing competence development and thereby effectively reducing productivity losses, enabling conclusions about task sequencing, employee assignment, and learning intervals that can accelerate ramp-up performance in practical applications.

In the face of turbulent market environments and shortening innovation cycles, the competitiveness of manufacturing companies increasingly depends on the synergetic planning of products, production processes, and spatial structures. However, traditional planning approaches remain largely isolated, resulting in inefficiencies, information gaps, and inconsistent decision-making known as ‘engineering over the wall’. This paper addresses these challenges by conducting a systematic literature review to evaluate the current state of research regarding isolated and integrated planning methodologies. The analysis reveals that while domain-specific methods are mature, a holistic framework effectively integrating product, factory, and spatial planning is lacking. To bridge this gap, this paper proposes a System-of-Systems approach that conceptualizes the three domains as autonomous yet tightly coupled planning systems. A methodological convergence framework is introduced to align these systems across three abstraction levels: objective setting and establishment of the project basis, concept planning, and detailed planning. Furthermore, the concept suggests employing formal integration artifacts, such as Interface Control Documents and a Common Data Environment, to systematically manage complex interdependencies and information flows. This comprehensive framework aims to enhance transparency, enable scenario-based decision-making, and support continuous adaptation throughout the planning processes.

The expansion of product portfolios, the reduction of product life cycles and the volatility of markets pose significant challenges for production systems and their control. Concurrently, these trends present a challenge in achieving an optimal balance between logistical performance and internal company costs. The discordance between the escalating demands of customers on logistics performance, exemplified by metrics such as throughput time and schedule reliability, and the cost-driven corporate objective of minimizing work-in-process and maximizing machine utilization, is becoming increasingly challenging to reconcile. The application of reinforcement learning (RL) is a significant machine learning (ML) approach for overcoming these challenges. In comparison with other ML approaches, RL facilitates direct interaction with production systems and is consequently well suited for controlling them in operational use. Despite the extensive body of research on RL approaches for production control tasks, there is a paucity of literature addressing the influence of these approaches on key logistical targets for logistics performance and costs. The article’s added value derives from the systematic literature review it conducts, which provides researchers and practitioners with an overview of how existing RL approaches influence central logistical target variables. Furthermore, it highlights blind spots in the research landscape. The results indicate the existence of a substantial number of approaches; however, their distribution across control tasks is disproportionate. Furthermore, it is evident that there are distinct discrepancies in the classification system with respect to the impact on logistical target variables.