Dynamics in Logistics

This research applies a qualitative, exploratory approach and uses the research methodology of design science to analyze power dynamics in a digitalized SCM landscape. Through 15 expert interviews and comprehensive case studies, the study develops a design theory approach that offers insights into how digitalization influences power structures, strategies, and interactions within supply chains, bridging the research gap in this domain. A three-level supply chain model is introduced to represent interactions among supply chain insiders, enablers, and the digital supply chain backbone. Furthermore, a power model adapted for the digitalized environment was developed. Case studies featuring Amazon, SAP, Mercedes Benz, NVIDIA, VW, and Prevent validate the relevance of the design theory. Findings emphasize the need to differentiate power dynamics in analogue and digital contexts within SCM, contributing to theory development and understanding of the evolving power landscape in the digital era. The findings contribute to the advancement of supply chain management theory in digital transformation and shed light on the intricate relationship between power structures and digitalization within supply chains.

Global logistics and supply chain standardization involves strategically coordinating processes across diverse subsidiaries to achieve global efficiency and local responsiveness, fostering worldwide knowledge exchange. However, this entails overcoming foreign process variations and diverse subsidiary mindsets across different locations while accommodating local dimensions. Our study, based on a strong theoretical foundation and action research strategy, aims to create a logistics standardization framework for modelling and defining operations, and measuring process deviations globally. We employ a maturity-oriented strategy, conducting interviews and meticulous examinations of 10 European plants in various sectors. We developed a framework with 16 processes and 113 designated achievements at different maturity levels, along with performance metrics for each process. Further, we provide a roadmap for continual improvement, emphasizing the importance of metrics in evaluating standardized procedures. Notably, we highlighted the processes of material planning, inbound transport management, and inventory management, which were found to be the top priorities from our findings. By elucidating the key components and considerations in crafting such frameworks, our findings equip practitioners and scholars with a structured approach to addressing the challenges associated with standardizing logistics processes on a global scale.

The upstream segment of the minerals supply chain –MiSC– has a crucial role in a sustainable global future by securing the supply of minerals –commodity– for developing renewable-energy technologies. However, due to its nature, the MiSC’s upstream segment is prone to negative events. These could disrupt the commodity supply's security and prevent it from achieving global sustainable goals. Therefore, an in-depth understanding of how a disruptive state is perceived in this segment of the MISC is necessary to develop more resilient strategies, thus ensuring sufficient commodity supply. This study aims to understand disruption in the MiSC’s upstream segment, supporting a multiple case study methodology carried out in the Chilean MiSC’s upstream segment context. Findings establish that a disruption in the MISC's upstream segment perceives as the impact of any event in the mining life-cycle generating a momentary or indefinite operational continuity suspension of its business processes, resulting in negative business performance. Also, two disruption scenarios are inferred, “production discontinuity” and “production closure”. This study contributes to the current literature on Risk and Resilience supply chains –SC–, expanding the knowledge of disruption in a new industrial context, such as MiSC’s upstream segment. Furthermore, future researchers are encouraged to extend the knowledge of Risk and Resilience SC in the same industrial context of this work.

This paper examines the optimization potential within the physical distribution of vaccines, focusing on the case of COVID-19 vaccine distribution in Germany. The analysis uses a literature-based potential audit consisting of five steps: analysis of requirements, performance, processes, structures, and benchmarking. The analysis identified bottlenecks in vaccine distribution, such as coordination of ingredient sourcing, packaging facilities, and demand-driven allocation issues, and showed that the decentralized distribution structure led to inefficiencies. Better communication and use of existing supply chain structures could have improved the distribution of COVID-19 vaccine in Germany.

Humanitarian logistics and supply chain (HLSC) management involves networks of people, organizations, goods, services, and processes to provide aid to those impacted by disasters and other emergency situations of differing scales. This study outlines the HLSC challenges faced by an organization providing humanitarian aid and illustrates how these challenges can be categorized as either internal or external, depending on the extent of the organization's influence.

This paper aims to identify current challenges in the management of food supply chains. We present results from a literature review as well as findings from a survey of 17 experts who are practically involved in food supply chain management. In order to identify current challenges, we focused on the years 2020–2023 in the literature review. Finally, we contrast both the results from literature review and the results from the expert interviews. Researchers consider that major challenges include food loss and waste, disruptions such as the COVID-19 pandemic, ecological and social requirements regarding sustainability, and the need to optimize processes in food supply chain management and operations. The experts also mention the impact of political crises and disruptions and sustainability as challenges in practice. Due to these external influences, they also consider the increasing importance of the supplier-buyer relationship to be a major challenge.

The aviation sector employs innovative technical involvements, applications, and operational practices. As a result, unmanned aerial vehicles that are remotely piloted from a ground station usher in the next phase of both passenger and freight transportation. This study is focused on freight transportation using drones. Although many studies in the past have focused on various drone delivery configurations, this study finds a critical research gap when evaluating the drone port location problem for a set of centralized ports where service is shared among multiple demand generators. Addressing the research gap, this study adapts the approach of continuous approximation (CA) in model development to find the optimum area allocated to a centralized drone port in an urban area. Findings indicate that the drone service range is a limiting factor for the optimal service area of the drone port. Furthermore, it was revealed that the optimal service area and the minimum total delivery operation cost have a low sensitivity to factors such as the shape of the service area, demand density and travel cost per unit distance.

Vans, cargo bikes, or even autonomous delivery vehicles are used in urban parcel delivery. A fleet consisting of these vehicles is called heterogeneous, differing in several technological dimensions including speed, range, as well as the impact on the delivery process. This research analyzes the operations of such a heterogeneous fleet in express urban parcel delivery. The heterogeneous vehicles are assigned to serve dynamically requesting customers within a delivery promise. We propose and analyze the strengths and weaknesses of selected policies assigning heterogeneous vehicles to serve customer requests in delivery districts with differing characteristics.

Heavy-duty battery electric commercial vehicles may be limited to short routes without a secondary power source. Therefore, concepts for commercial vehicles with fuel cell range extenders have been proposed. This paper presents an agent-based supply chain GIS model developed to simulate urban and regional freight distribution employing fleets of electric trucks equipped with fuel cell range extenders. The model was implemented in AnyLogic to simulate the transport of goods from regional distributors to urban stores. It solves a vehicle routing problem and identifies optimised routes by running a simulated annealing algorithm complemented by several heuristic methods. This model was used to optimise vehicle concepts for a particular freight distribution task. A reduction in battery capacity minimised costs directly related to the vehicles, whereas an increase in both battery and load capacity minimised fleet size. Additional power generation in the range extenders compensated for the reduction of battery capacity.

Resilience of critical infrastructure such as road networks is crucial to maintain provision of essential logistics services even and especially during disruptive events. This paper proposes a new method for assessing the resilience of urban road networks using shortest path analysis. The method is based on representative routes which connect selected Points of Interest with service providers. By comparing reachability and shortest path lengths for these routes in an intact road network with those in a compromised network, weakly connected areas are detected and the overall network resilience against the respective disruption analysed. To that end, the paper proposes the Robustness of Accessibility index as a novel score for the resilience of critical infrastructure. To demonstrate the proposed method, a case study of flooding in Trier, Germany, provides insights into the vulnerability of the city’s road network in terms of potential response delays in emergency logistics. Such an analysis can help policymakers and planners improve the robustness and reliability of critical infrastructure and logistics processes.

Digitalization is playing an increasingly important role in the information flow of today’s supply chains. In particular, in logistics, the adoption of digital technologies such as the Internet of Things, cloud computing, blockchain, or machine learning offers the potential to increase data availability and quality. Using inter-organizational communication systems, private and public stakeholders can integrate their information flows. Within this paper, we analyze with transport planning, tracking, and cargo monitoring three use cases for the adoption of decentralized systems focusing on maritime logistics. For this purpose, a software artifact was developed using the Design Science Research (DSR) approach. During the development process, four central design principles could be identified: user orientation, interoperability, data security, and decentralization. Based on these principles, a concept was developed for a decentralized information system that contributes to further automation and standardization of the information flow, while considering requirements such as confidentiality, neutrality, and accessibility.

The prediction of accurate vessel arrival times is essential and challenging at the same time to plan vessel arrivals with sufficient accuracy, coordinate berthing manoeuvres and monitor ship traffic efficiently. This paper investigates a new approach by using clusters consisting of deep artificial neural networks (DNNC). For this purpose, the considered coverage area of the Weser river was divided into geospatial domains. An also developed linear regression model (LNNC) served as a reference model, which was generated analogously to the machine learning approach on the clusters. The estimated time of arrival prediction was evaluated at a distance of 50 km between the estuary of the Weser river into the North Sea and the target industrial port. It could be shown that the mean deviation from the actual travel time at a distance of 50 km is −19.80 min for the DNNC and 67.90 min for the LNNC. At a distance of 33 km from the industrial port, the mean deviation of the DNNC decreases to 2.85 min and for the LNNC to 54.40 min. Furthermore, it has been observed that the shorter the distance to the destination port, the more accurate the predictions become.

Vessel delays and increased terminal call sizes negatively impact the ability to properly plan daily operations at seaport container terminals. Such traffic patterns lead to, among others, infrequent peak loads at the seaside of container terminals, complicating terminal operations. Thus, relying on annual or monthly statistics fails to account for these day-to-day fluctuations. When container terminals are planned, be it a greenfield or brownfield terminal, these variations in operations need to be accounted for. The traditional formula-based approach to design terminals uses annual statistics. In this study, it is first used to produce estimates for the required yard capacity for three existing exemplary container terminals. These are then compared to the results of numerical experiments using the synthetic container flow generator ConFlowGen. The findings reveal that yard capacity requirements fluctuate considerably depending on the timing of vessel arrivals and their call sizes. This dynamic modeling proved particularly beneficial for planning gateway traffic, offering more accurate storage capacity predictions. Suggestions are made for how to further develop ConFlowGen for handling transshipment traffic better in future versions.

Ferry traffic is particularly dominant in inland seas such as the Baltic Sea, where it can exploit its advantage of high departure frequency and short journey times, thus enabling fast-moving traffic throughout Europe. Roll on/Roll off (RoRo) and Roll on/Roll off Passenger (RoPax) ports, however, are confronted with increasing competition for port expansion areas from various developments such as the rezoning to urban areas. Therefore, maintaining adequate access to RoRo/RoPax ports is becoming increasingly challenging and can only be achieved through the interaction of different stakeholders such as port authorities, ferry companies or city planners. In urban areas in particular, traffic situations increasingly occur that make it difficult for trucks or other vehicles to reach the port reliably and thus place a heavy load on terminal and shipping company resources.After analysing the literature on simulation approaches with respect to truck arrival management in the RoRo/RoPax and the container terminal segment, the application of a pre-gate concept to the RoPax port Turku (Finland) in combination with a call-off structure was analysed by a simulation.In the paper three different scenarios were compared regarding the positioning of pre-gate parking spaces according to the parameters: travel time, vehicle arrival time at the terminal and queue length at a prominent intersection.The approach adopted offers a controllability that can be actively used by the terminal operators and stevedoring to make terminal operations and vehicle handling more efficient.

Transporting goods via inland waterways offers significant advantages over transport via road or rail. The inland waterway vessel is more environmentally friendly, reliable, and quieter than other transport modalities. This paper presents a framework based on this motivation to develop so-called MicroPorts to strengthen inland waterway transport and increase its attractiveness. MicroPorts are new small-scale transshipment facilities for inland waterways based on the conversion of existing infrastructure. Through this, the network of transshipment points on inland waterways can be expanded while keeping construction costs and impact on nature low and the transported goods closer to their destination, shortening the last few kilometers by road or rail. The MicroPorts framework was developed through several workshops with an inland shipping owner that offers transportation by inland vessels, process analysis at an inland waterway port and literature analyses. It comprises five elements: characteristics, operational requirements, technical requirements, location, and assessment parameters. Based on the framework, a method was derived to develop MicroPorts. The method contains four steps: 1. Identify potential locations, 2. Selection of possible operational concepts, 3. Selection of possible technical implementations, and 4. Evaluation of feasibility. The method can be used for the identification of new transshipment locations and planning of new MicroPorts. This paper also presents first developed MicroPorts concepts alongside an exemplary route.

The dynamic and rapidly evolving business environment poses numerous complexities for production logistics. The increasing frequency of product and model changes, coupled with the growing variability of components, underscores the urgency for adaptive measures to address reducing product life cycles and advancing customer demands. Technological advancements have enhanced logistical productivity, but it is important to comprehensively tackle these challenges. To overcome these limitations, the concept of “transformability” is explored as a central cornerstone of the solution.A design framework is proposed to increase the transformability. This paper systematically captures and models the production logistics system to achieve the goal. Key change enablers are identified and aligned with the production logistics system to develop specific change enablers for each production logistics area. These serve as the foundation for formulating transformation measures that can be incorporated into logistics planning. The results guide companies for successful implementation and long-term competitiveness by offering potential users a wide range of possibilities to increase their transformability within planning activities. This work contributes to raising awareness of the importance of transformable production logistics and offers practical recommendations for action. By embracing this approach, companies can proactively and effectively respond to dynamic fluctuations in the production environment, ensuring long-term competitiveness and sustainability.

This study set out to develop a Reinforcement Learning (RL) agent for solving an extended Permutation Flow Shop Scheduling Problem (PFSSP). From the domain perspective, we see a lack of realistic constraints for synchronized, human-centered assembly lines. Moreover, objective functions must be provided to enable stress-reducing as well as robust planning under uncertainty. From a methodical perspective, RL has received more and more attention for problems of this type. However, we cannot identify applicable RL concepts for our extended PFSSP with multicriteria objectives. We propose a generic RL agent, which operates on an abstract representation of the schedule and with an objective-independent reward function. Our numerical experiments demonstrate that the agent successfully generalizes a policy and achieves better scores than a Simulated Annealing (SA) metaheuristic.

Manual work is a significant cost driver in manufacturing and logistics. However, research on the methods for analyzing manual processes utilizing sensor technologies, apart from technical feasibility, is scarce. Motion-Mining® is a technology that uses motion sensors, Bluetooth, and pattern recognition to enable highly automated process mapping and analysis of manual work. The aim of this paper is to evaluate the benefits and limitations of applying this technology in manual production and logistics processes. To this end, Motion-Mining® is compared with traditional and low-tech Lean management tools for capturing and analyzing manual activities.Ten semi-structured expert interviews as well as case studies in four companies were conducted. The results indicate that Motion-Mining® differs from Lean tools for process analysis mainly in terms of the effort required for data collection, the amount of data obtained, the representativeness of the data, the level of detail, and the insights gained.

This real-world simulation study analyzes a newly planned factory layout of a production company. Therefore, the first goal is the validation of the layout concerning bottlenecks, e.g., buffers, machines, and the planned transportation organization. The second goal is the analysis of different possible improvements, regarding scheduling (different priority rules) and automatic transportation with Automated Guided Vehicles (AGVs). The best number of AGVs in terms of cost and logistic service level for the selected scenario is determined by the simulation study. Scheduling methods for jobs and AGVs are also compared, since they have high impact on the goal criteria, e.g., lead times. The study shows, that the selected layout including machine capacities is able to handle the estimated amount of occurring jobs in the future. Further, an effective setup for the scenario could be found, which also supports the requirements of flexibility.

Steelmaking through Electric Arc Furnace (EAF) is known to be energy and cost intensive therefore, any improvement in processes will result in economic and environmental benefits. This study aims to improve the efficiency of the EAF process by predicting the most feasible steel grade which can be obtained with minimum purification based on endpoint parameters. Naïve Bayes classifier algorithm was employed to categorize EAF operational data. The operational data consists of 16 parameters with more than ten thousand data samples which classified into 6 possible steel grades, the carbon content of molten steel is defined as the decision variable to classify operational data. Finally, the results are also compared with MS Excel to examine how well the machine learning algorithm can be obtained. The results show the algorithm can classify data with more than 90% accuracy.

The widespread adoption of Industry 4.0 technologies is resulting in a wide availability of real-time data gathered on the shop floor. This data, once properly elaborated, can be used to support dynamic decision-making, improving manufacturing companies’ capability to deal with uncertainty and thus leading to potential benefits in their performance. This paper presents a simulation model to assess the changes in manufacturing systems performance resulting from the use of real-time data in the dynamic scheduling of in-plant logistics activities. The model was developed considering a general factory layout and implemented in Python, a widely used open-source programming language. Therefore, the model can be used and extended by a wide community of researchers, serving as a base for future studies, and adapted to be applied to a large number of factories, thus favoring a more widespread adoption of dynamic scheduling systems in practice. In this study, the model was applied to the setting of a factory in the food industry in which a fleet of mobile robots supply materials to production stations and retrieve finished goods, carrying them to the factory warehouse. Results show that a dynamic scheduling system, in which in-plant logistics activities are scheduled considering real-time data on the status of shop floor resources, leads to better performance, in terms of production stations uptime, compared with the static system currently adopted by the company.

Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are flexible and reliable options for material handling automation. The integration level with the production/logistic systems is crucial for performance and investment costs. Proper design of loading/unloading points is essential as they impact the number, level of automation, sorting/buffering level, and vehicle requirements. This paper presents an innovative approach combining virtual-interactive simulation and mathematical modeling to optimize loading/unloading points for maximum operational and economic performance. This approach simulates different scenarios and identifies the best loading/unloading points configurations optimizing the whole system’s performance. A numerical analysis is reported to demonstrate the practical implications.

The integration of Autonomous Guided Vehicles (AGVs) into smart factories is transforming modern manufacturing, creating coexistence between humans and robotic systems. In this evolving landscape, one critical aspect is the seamless coordination of AGVs and human workers within factory settings. To achieve this, our research presents a portable indoor localization system that utilizes ESP32 microcontrollers as compact access points. Using Wi-Fi Fine Time Measurement (FTM) with smartphones, the system estimates worker positions through multi-lateration techniques in conjunction with advanced filtering methods. This localization system serves as a pivotal bridge, ensuring that AGVs can interact with and respond to the movements of workers within warehouses. A field study in an actual warehouse environment validates the system’s performance, demonstrating 1.13 m accuracy in lateral movements. Furthermore, its localization capabilities within specific warehouse areas showcase its potential to enhance order picking processes and optimize human-AGV interaction.

Green hydrogen production, distribution and use is seen as a central element of a carbon-neutral economy. Specifically, the establishment of offshore green hydrogen production facilities amidst wind energy parks is seen as a promising concept for European countries like Germany, which documented the first offshore wind energy hydrogen production during 2023 and plans significant production volume extensions in this regard. Yet, such green hydrogen manufacturing and distribution concepts are not evaluated in a comprehensive sustainability perspective. In order to avoid unintended sustainability effects, an ex-ante evaluation regarding the three triple bottom line perspectives of environmental, economic and social sustainability is advisable. As especially offshore green hydrogen production and transportation concepts are completely new, even the evaluation concept to be used for such a required comprehensive sustainability check is largely missing. Although dedicated evaluation and decision support methods in the fields of LCA and SLCA are available for sustainability evaluation issues, the question of selecting matching method frameworks and relevant evaluation categories for a future offshore-based green hydrogen supply chain is yet to be answered. This contribution is provided by this paper in a conceptual approach based on existing method sets and analytical results for neighboring application fields like solar or biogas green hydrogen production and distribution.

Green hydrogen, produced mainly by electrolysis, is a promising energy carrier to de-fossilise different economy sectors, from heavy industry to logistics. A fully transformed economy would use hydrogen as a process gas and a fuel for heat generation and vehicles. However, since the technology to produce green hydrogen has yet to be available at an industrial scale, there are no projections for forming regional hydrogen hubs. This article contributes to synthesising a holistic framework to specify and optimise hydrogen-based applications in logistics from an ecological and economic perspective. These applications utilise logistics macrostructures, like logistics hubs. Alternatively, they may utilise industrial supply chains, like direct reduced iron (DRI) based steel plants, which modify their operations and transform their logistic ecosystems. The framework includes a configuration of policies and economic boundary conditions that influence the logistic hubs’ transformation paths. The article describes the synthesis of the framework based on an initial problem analysis and a systematic literature review. The framework helps policymakers and planners evaluate and optimise the composition and design of hydrogen and logistics hubs.

Animal feed supply networks heavily rely on just-in-time deliveries between raw material producers, retailers, manufacturers, and customers. Accordingly, transportation contributes largely to this industry’s CO2e footprint. This article extends an existing simulation model with capabilities to track the CO2e footprint of individual products across the supply network. It further integrates the capability to simulate the use of electric transport vehicles. This article presents a simulation study to investigate using electric trucks instead of diesel trucks in terms of CO2e and kilometers traveled. The results show that the animal feed distribution is particularly suitable for electric vehicles due to the comparably localized area covered by these supply networks and can achieve reductions of up to 70% CO2e for a well-utilized fleet.

Amidst rising stakeholder expectations and recent disruptive events, manufacturing firms are re-evaluating strategies, focusing on sustainability and resilience. Operations managers face a resource allocation challenge balancing these priorities.This conceptual paper delves into sustainable resilience, exploring the relationships between congruent operations, network capabilities, and sustainable firm performance, considering uncertainty and sequential capability-building. Through conceptual literature review, this paper presents a conceptual model and associated hypotheses, laying the groundwork for an extensive empirical study.Building on the cumulative capability theory, we provide a nuanced perspective on the traditional Sand Cone model, emphasising sequence testing of operations and network capabilities. This approach paves the way for a multi-dimensional understanding of sustainable resilience. Addressing paradoxical tensions from trade-offs, our model outlines a path for subsequent research, aiming to guide firms through the journey of multiple priorities in today’s volatile environment.

In the logistics arena, green warehousing has been achieving increasing attention from both practitioners and academia. On the one hand, practitioners have started to search for solutions to decrease the environmental impact of their logistics facilities and incorporate practices towards greener warehousing processes. On the academic side, a rising – though limited – number of papers have been found addressing the impact of the green warehousing practices in place, together with the related effects on warehouse consumption and environmental performance. In this context, conditioned warehouses represent a key challenge due to their temperature constraints and the ever-demanding logistics performances, and related studies are still lacking. This paper aims to addressing this research gap by proposing a simulation-based approach where multiple scenarios of a real conditioned logistics facility are discussed, grounded on a conceptual framework of green warehousing practices selection process. Three different scenarios are proposed, and the related performances are examined in terms of energy consumption and CO2eq emissions. Implications of the results are discussed and streams for future investigation are identified.

Manual picker-to-parts order picking systems remain predominant in retail warehousing and have been identified as one of the comparatively most labor-intensive processes. While previous studies have delved into the effects of work intensity and worker experience on performance, they have typically examined each construct separately while neglecting workload-related experience. Given that the interaction remains under-explored, we here investigate how workload-related experience could possibly mitigate the negative performance effects of work intensity. We obtain a unique longitudinal real-world retail warehouse data set including 1,739,352 storage location visits performed by 74 order pickers from January to April 2023. We apply a mixed-effects model allowing for random intercepts for each order picker and utilize order picking task performance time as our dependent variable. We find that work intensity increases task performance time at increasing rates and that workload-related experience can mitigate this effect. Our research informs operations managers under which conditions they can capitalize on the positive effects of workload-related experience while mitigating the negative consequences of work intensity.

Despite the evident connections between Demand Forecasting and Inventory Control, both researchers and practitioners tend to perform and analyze these issues separately. Yet, the application of appropriate Demand Forecasting Methods promises meeting inventory related target values while reducing inventory costs. A significant challenge consists in identifying the appropriate Demand Forecasting Method. Thus, practitioners require a framework that supports the decision process of selecting said method. Depending on the chosen forecasting method, different configurations of an inventory control policy might be suitable. The aim of this work is to facilitate the complex task of connecting the forecasting method selection and inventory control policy configuration for a group of numerous and heterogeneous products. Thus, a simple framework that generates recommendations regarding the appropriate forecasting method and inventory policy will be devised and empirically tested. Two of the three suggested forecast methods will be investigated further. An example application shows that the concept enables a significant reduction of stockouts which translates to higher service levels. The proposed methods therefore contribute to efficient and economically sustainable warehouse operations and inventory control management.

Industry 4.0 represents a novel paradigm centered around digital factories, capable of integrating information technologies and machines with intelligent products. In this context, this article addresses the added monetary value resulting from adopting Industry 4.0 technologies in the development of a scraped surface heat exchanger equipment. This research aims to estimate the added value of a technology-based redesign of a food processing machine, considering the willingness to pay. The methodology employed to evaluate the integration of these technologies into the product is based on the Stated Preference (SP). The findings reveal a hierarchy among the enhancement opportunities that Industry 4.0 technologies bring to the product. Consequently, in this case, incorporating features from Industry 4.0 that encompass the maintenance aspects contributes significantly to the product’s value.

Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.

The convergence of Value Stream Management with cutting-edge technologies represents a dynamic area of research, as underscored by recent studies. These studies reveal a growing emphasis on digitalization and share a common goal: proposing data-driven techniques to enhance and optimize conventional Value Stream Management struggling to adapt in rapidly changing environments. By the present paper, a digital Value Stream Map according to the Digital Twin (DT) concept is investigated. This Digital Value Stream Twin (DVST) is based on the orchestration of multiple DT, representing core elements of Value Stream Management such as material flowing through the value stream and related resources. Overcoming the fixed structure of the automation pyramid, business application systems and machine signals are merged as data sources into one model, verified by a business scenario, mainly carried out in an SAP S4/HANA (ERP - enterprise resource planning) test environment. In this context, the present study is built upon a validation using a digital value stream model according to the Digital Shadow (DS) approach. Conceptually, the expansion of the DS into a DT is described. From this, potentials regarding the value stream method are derived and investigated.

With the increasing popularity of Augmented Reality (AR) applications, especially for mobile devices, the technology supports several construction projects. Here, AR helps to communicate planned construction projects, as its visualization increases the immersion and is better understood than common approaches. However, these use cases are mainly outdoors, which pose special requirements. For most, the (geo-referenced) 3D models of planning projects must be aligned correctly in natural environments, which is a challenge, as many AR devices and standard methods are not working for (large) outdoor environments. For this reason, new research approaches based on different algorithms and sensors arise. This paper defines requirements for developing geo-referenced outdoor AR applications by a structured literature analysis and developing an application with the key requirements: accurate 3D model placement and integration. Creating the mobile outdoor AR application further provides insights for developing such systems. The application considers several outdoor activity requirements and addresses different approaches to geo-referencing with internal and external sensors. This paper also presents two model integration methods: a 2D and a 3D environment scan and algorithmic processing.

Shelf replenishment is a repetitive, manually executed, and time-consuming task in retail. This paper addresses this issue with an intelligent pointer unit that helps staff reduce the orientation time for small and similar products in the shelf replenishment process. The user wears a ring scanner to scan an article or its box, whereon the pointer unit illuminates the target shelf position received from a digital store model. A comprehensive evaluation extracts the performance of the pointer unit within two user groups. The results show a reduction of the orientation time of 88.5% for beginners, respectively 75% for experienced staff members. Furthermore, accounting for the times needed for handling and alignment, a reduction of the overall search time of 71.5% for beginners, respectively 22.5% for experienced staff members, has been achieved.

In order to manage the demand and control the flow of cargo arriving at terminals, the port sector created a dynamic Truck Appointment System. However, disruptive events can cause a delay or an early arrival of trucks at the port terminal, leading to long waiting times, queues, and the need to reschedule trucks in other time windows when they arrive off the scheduled time. Smart technologies offer the potential to deal with uncertain scenarios and create a flexible context for the use of TAS. The main objective of this study is to compare regression and classification Machine Learning algorithms to predict truck arrival times. By comparing the predictions with the original appointment, a flexible Truck Appointment System is built. Four different ML approaches were evaluated, which have been implemented in Python: Linear Regression, Random Forest, Gradient Boosting Regression, and Decision Tree. Considering the disruptive arrivals, we identified that the classification algorithms performed better than the regression algorithms predicting the exact arrival time, but worse than the regression algorithms that predict the time window of truck arrivals.

International rail freight transport plays an essential role in reducing carbon footprint, with Eurasian rail transport as a successful example. However, multiple interfaces across totally different rail systems increase the complexity of information exchange. The quality of information directly influences the quality of planning. The impact of information exchange and its integration into international rail freight transport planning activities has not yet been studied extensively. This paper presents an integrated tactical rail planning method (ITPM) for international rail freight transport focusing on information integration. Advanced mathematical models for Decision Support Systems (DSS) based on information integration are also suggested.