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 allocations, 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, outsiders, 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 technology acceptance model (TAM) has been used by several researchers to explain the usage and adoption of technologies. Interestingly, this framework has not yet been applied to digital logistics in the context of low-income countries. This study used TAM to study empirically the intention of stakeholders in Ethiopian supply chains to adopt digital technologies. It used a modified TAM by adding factors specific to firms in low-income countries. Data were obtained through an extensive survey of logistics professionals. The results indicate that, in addition to usefulness and ease of use, external factors such as the presence of infrastructure, availability of human resources, accessibility of the technologies and existence of relevant policies significantly contribute to stakeholders’ adoption of digitalisation technologies in their supply chain. The study emphasises the importance of awareness creation programmes and the development of a digital strategy to ensure successful digital transformation in low-income countries. Its findings are useful for policymakers and practitioners as they enable them to understand the relationship between technology adoption, user perceptions and external facilitating conditions.

In those turbulent times of disruptive changes, companies need to safeguard their material replenishment. This can be achieved by implementing resilient supply chains. Supply chain resilience (SC resilience) is the adaptive capabil-ity of the supply chain to prepare for unexpected events, respond to disrup-tions, and return to a normal or better state. This presentation introduces a framework, the supply chain resilience navigator, which can help companies to achieve supply chain resilience.

On the one hand, our findings are based on qualitative empirical data we conducted in an in-depth workshop with 25 purchasing and supply manage-ment executives from an agricultural machinery manufacturer. They collect their lessons learned from the experience with the Covid crisis. On the other hand, we analyzed the literature on SC resilience.

Our framework consists of three layers. Ground laying are the four principles of SC resilience: risk management culture, collaboration, agility, and trans-parency (layer 1). The principles are the backbone of the two management categories for achieving SC resilience. Supplier Management means analyz-ing, adapting and changing supplier strategies; risk management deals with developing and implementing risk management strategy, organization and tools (layer 2). For each category, we developed five courses of action to ac-complish SC resilience. An example in the category supplier management is a regional multiple sourcing strategy, which offers several recommendations how to implement the corresponding course of action (layer 3).

Our framework can help academics to better understand the measures of re-silience. Practitioners get concrete recommendations to make their supply chain more resilient.

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 distri-bution 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 distribu-tion, such as coordination of ingredient sourcing, packaging facilities, and demand-driven allocation issues, and showed that the decentralized distri-bution structure led to inefficiencies. Better communication and use of ex-isting 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 pro-vide 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 man-agement. 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 ma-jor 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 sus-tainability as challenges in practice. Due to these external influences, they also consider the increasing importance of the supplier-buyer relationship to be a ma-jor challenge.

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 address 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 those tasks separately. Yet, the application of appropriate Demand Forecast-ing Methods promises meeting inventory-related target values while reducing Inventory Costs. A significant difficulty consists in identifying the appropri-ate 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 the 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 regard-ing the appropriate Forecasting Method and Inventory Policy will be devised and empirically tested. However, due to capacity restrictions, only two of the three suggested Forecast Methods will be investigated further. The applica-tion of the framework regarding sales data of a biotech company shows that it enables a significant reduction of stockouts which translates to higher ser-vice levels. The proposed methods therefore contribute to efficient and eco-nomically sustainable warehouse operations and inventory control manage-ment concepts.

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. Yet, such green hydrogen manufacturing and distribution concepts are not evaluated in a comprehensive sustainability perspective. In order to avoid unintended sus-tainability effects, an ex-ante evaluation regarding the three triple bottom line perspectives of environmental, economic and social sustainability is advisa-ble. 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. Alt-hough dedicated evaluation and decision support methods in the fields of LCA and SLCA are available for sustainability evaluation issues, the ques-tion of selecting matching method frameworks for a future offshore-based green hydrogen supply chain is yet to be answered. This contribution is pro-vided 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 use 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 \coe 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 a 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.

Secondary trading of clothing can support the transition to the circular economy by prolonging the lifespan of products through reuse. From a consumer point of view, second-hand consumption promotes a reduction in first-hand purchasing. From a company point of view, resale allows to take care of end-of-life products, according to the increasing environmental responsibilities requested to manufacturers, but also to monetize with left-over products, overstock, and returned goods. Indeed, resale models offer the chance to take control over a product’s lifecycle and to dispose of slow-moving inventory, unsold products at the end of the season, and returns with sustainability-focused solutions.

With the growing interest in re-commerce, firms have increasingly entered this market over the last decade, anyway, it proved to be both expensive and logistically challenging for brands used to sell only new items. The objective of this exploratory study is to analyze the end-of-life product management of luxury companies. Specifically, through multiple case studies based on semi-structured interviews of businesses operating in the luxury second-hand market, we explore the strategic partnership developed by companies and second-hand trading platforms to manage used products or returns and embrace circular economy.

The results will show how resale can be both an environmental opportunity to embrace circularity and an economical one. Managerial insights of this research will guide managers towards practices to promote efficient logistic coordination and achieve cost-minimizing and profit-maximizing, which are the key factors used in determining processing options for returned and unsold goods.

This talk investigates nodes in a transport network that do not handle transport items but the vehicles circulating to carry passengers or goods between different node work nodes along the network arcs. These nodes are called depots and serve as parking lots or maintenance yards.

Internal depot processes within transport networks face several challenges, like limited vehicle hosting capacity and restricted operation times to name just some predominant examples. The primary need of a depot manager is to coordinate the arriving vehicles with the currently stored vehicles and to ensure that vehicles can leave the depot so early that subsequent vehicle deployments can start as planned. Every depot is a production system comprising several workstations connected by a road or rail infrastructure. Often a depot turns out to be a bottleneck in the transportation processes within a network.

A significant driver for the complexity of planning depot operations arises since vehicles must travel through this production system in a vehicle-specific machine sequence considering vehicle-specific availability and due dates.

We propose a generic mathematical model that aims to connect the vehicle arrivals in front of a depot with vehicle departures from the exit gate by proposing internal vehicle movement processes inside the depot area. The central idea is to set up a time-space network graph where vehicle operations take place. We report initial results from the model application in the context of commuter rail yards confronted with heterogeneous requests to guide vehicles through the depot facilities under time constraints.

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 have potential to deal with uncertain scenarios and create a flexible context for the use of TAS. In this context, 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 designed. 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 ex-act arrival time, but worse than the regression algorithms that predict the time window of truck arrivals.

International rail 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 transport planning activities has not yet been studied extensively. This paper presents an Integrated Planning Method (IPM) for international rail transport focusing on information integration. Advanced mathematical models for Decision Support Systems (DSS) based on information integration are also suggested.