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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
