As climate challenges intensify, ecological objectives are gaining importance alongside traditional objectives in distributed scheduling, giving rise to distributed green scheduling problems. However, current models and objectives fail to capture key characteristics of geographically distributed manufacturing systems, particularly the emission intensity of electricity generation and the distribution of goods. Since the environmental impact of electricity consumption varies with local emission factors, they are critical in distributed permutation flowshop scheduling problems. Further, the validity of ecological optimization can be compromised, as energy savings may be offset by increased transportation-related emissions. Based on an experimental analysis calibrated to real-world European production networks and including makespan as an economic objective, we find that optimizing total energy consumption results in an average hypervolume RPD of 42.12%, questioning its validity as an indicator of environmental performance in distributed scheduling. Moreover, focusing solely on production-related emissions still results in an average deviation of 26.13%, highlighting the bias caused by neglecting the distribution stage — an effect that becomes more pronounced with increasing product weight. To further enhance real-world applicability, we assess the impact of eligibility constraints — arising from limited redundancy in tools and raw materials — on the potential to minimize both makespan and carbon emissions, and propose distance- and emission-aware strategies for factory qualification. Finally, the problem is solved using a novel parameter-less iterated greedy algorithm that incorporates problem-specific knowledge into speed factor adjustment, removes the need for parameter tuning, and demonstrates strong solution quality in extensive computational experiments.

Purpose This study investigates logistical challenges hindering sustainable online grocery retailing (SOGR) in Germany. It identifies critical factors for SOGR and improvement measures that interrelate these factors to strengthen economic, ecological and social sustainability simultaneously. An active-passive matrix determines the most impactful and receptive factors for cross-dimensional improvement.Design/methodology/approach Using a Grounded Theory approach, the study draws on fourteen semi-structured expert interviews. Sustainability challenges were coded into seven critical factors across the Triple Bottom Line (TBL). An active-passive matrix structures the recommended measures and illustrates their interrelationships across the three sustainability dimensions.Findings Optimized order picking, innovative food packaging, as well as punctuality and reliability emerged as the most impactful sustainability factors, each representing a different TBL dimension. The active-passive matrix shows how improvements in these factors generate positive effects across multiple other factors. Food waste was identified as having the greatest improvement potential. A previously unarticulated social factor (people's benefits) appeared during analysis, revealing latent impacts visible only through system-level evaluation.Practical implications The active-passive matrix offers retailers a practical tool to prioritize high-impact sustainability initiatives. By focusing on order picking, packaging and delivery punctuality and reliability, firms cannot only improve cost efficiency and reduce food waste but also enhance service quality, customer satisfaction and employee well-being. The findings highlight the importance of managing interdependencies across supply chain processes and offer guidance for different fulfillment models.Originality/value This study advances the literature by providing an integrated perspective on sustainability in online grocery retailing, linking economic, ecological and social dimensions within a single framework. It bridges home delivery and click-and-collect models, conceptualizes interactions between operational measures, and offers new insights from Germany's cost-sensitive market. By identifying leverage points that generate cross-dimensional synergies rather than trade-offs and distinguishing between the most impactful and most improvable factors, the study provides a more nuanced understanding of sustainability management in logistics-intensive retail contexts.

The rapid growth of e-commerce has intensified packaging waste, highlighting the need for sustainable alternatives. Reusable transport packaging (RTP), rooted in circular economy principles, presents a promising solution but faces challenges in adoption within the German online retail market. Addressing a gap in theory-driven research on RTP, this study extends the theory of planned behavior (TPB) by integrating environmental concern, personal innovativeness, perceived usefulness, return convenience, and shopping frequency to explain consumers’ intention to adopt RTP. Survey data from 792 German online shoppers were analyzed using partial least squares structural equation modeling. The results show that environmental concern and perceived usefulness are key drivers of adoption intention, whereas personal innovativeness and return convenience have no effect. Mediation analyses reveal that attitude and perceived usefulness fully mediate the impact of return convenience on intention and partially mediate the effect of environmental concern. Shopping frequency does not impact the relationship between attitude or perceived usefulness and intention, but it moderates the environmental concern-intention relationship, weakening its influence among frequent shoppers. Theoretically, the study advances TPB by incorporating underexplored psychological and situational factors relevant to low-complexity sustainable innovations. Managerially, it suggests that retailers and packaging providers should emphasize the functional and environmental benefits of RTP to target both environmentally conscious and habitual online shoppers.

The rise of Industry 4.0 technologies is transforming the automotive industry. Additive manufacturing is one innovation that could prove pivotal in the automotive spare parts supply chain. On-demand production, enabled by additive manufacturing, could lead to significant cost savings by reducing inventory. The complex structure of the automotive supply chain offers many opportunities for the positioning of additive manufacturing and realizing the potential for production closer to the point of use. While several scenarios for the positioning of additive manufacturing have been proposed in the literature, there is a lack of empirical research and case studies evaluating the practicality of the scenarios. An embedded single case study was conducted with eight key informants from the automotive industry to extend the theory and examine the feasibility of the scenarios from the literature in practice. The findings suggest the implementation of additive manufacturing in a regional distribution center or outsourcing to additive manufacturing service providers. Both scenarios have shortcomings that are of practical importance. Implementing additive manufacturing in various regional distribution centers would require greater investment by the original equipment manufacturer while outsourcing to additive manufacturing service providers would entail costly certification procedures. With this in mind, a two-stage implementation scenario was developed and validated. This scenario proposes the production of spare parts in a regional distribution center or at an additive manufacturing service provider, depending on the complexity of the manufacturing process. The responsibilities of each participant in the automotive supply chain are discussed, and an exemplary process flow is presented.

Predictive maintenance (PdM) is a data-driven maintenance strategy that aims to avoid unplanned downtimes by predicting the remaining lifetime of maintenance objects. Thus, unnecessary replacements of spare parts and critical process disturbances due to breakdowns can be avoided. Despite the widely recognized advantages of this technology, the number of successful applications in practice is still very limited. Our study aims to address the theory-practice gap by conducting a comprehensive case study involving 15 expert interviews with industry professionals to uncover critical factors that hinder the successful implementation of PdM. Our findings shed light on the underlying reasons for a hesitant PdM implementation, including challenges related to digital readiness, data quality and accessibility, technological integration, and maintenance organization. By providing an in-depth analysis of these factors, our study offers valuable insights and guidelines to improve the implementation success rate of PdM in the industrial context. Based on the empirical findings, we present critical implementation factors and develop a framework with ten propositions that aim to dismantle barriers in the industrial application process of PdM and stimulate further research in academia.

The number and variety of cyber threats have increased massively in recent times, challenging organizations and supply chains across all industries and sizes. This concerning development highlights the need for cybersecurity in our hyperconnected world and is further accelerated by the COVID-19 pandemic, geopolitical conflicts, and the implementation of Industry 4.0 technologies. While preventive measures can increase the robustness of the respective organizations and supply chains, cyber-attacks cannot always be prevented. The ability to recover quickly from these new threats becomes critical in the face of digital disruption. In this paper, we conduct a case study that provides in-depth experiences on how one company recovered its disrupted digital supply chain after a severe ransomware attack. We include the perspectives of supply chain partners and describe the whole recovery process of the affected company. The findings are presented along the four phases of resilience (readiness, response, recovery, and growth). Using the dynamic capabilities view and the relational view as theoretical underpinnings, we derive intra- and inter-firm capabilities that contributed to the recovery process and develop eight propositions. In this regard, we highlight not only the role of technical measures but also organizational and social capabilities in the recovery process. Our paper aims to complement and extend previous research with a novel approach and to provide several managerial contributions.

The application of Artificial Intelligence (AI) approaches in industrial maintenance for fault detection and prediction has gained much attention from scholars and practitioners. This survey systematically assesses and classifies the state-of-the-art algorithms applied to data-driven maintenance in recent literature. The taxonomy provides a so far not existing overview and decision aid for research and practice regarding suitable AI approaches for each maintenance application. Moreover, we consider trends and further research demand in this area. Finally, a newly developed holistic maintenance framework contributes to a practice-oriented implementation of AI and considers crucial managerial aspects of an efficient maintenance system.

Digitalization changes the formal structures and procedures of supply networks and provides better abilities for interfirm governance. At the same time, digital supply networks require fewer human interactions, which reduces the relevance of social aspects for safeguarding against opportunism and effective coordination. Prior research examined selected digital technologies and their influence on specific governance dimensions. However, these findings exist in isolation, and a comprehensive understanding of how digitalization impacts different aspects of governance is missing. In this article, the analysis of 156 articles in a systematic literature review presents an integrative perspective on the effects of digitalization on interfirm governance, with technology amplifying, simplifying, and deteriorating governance. The study concludes with an agenda for future research on interfirm governance and managerial implications for companies governing digital supply networks. JEL Classification: D23

This paper explores problems of pharmaceutical supply chains, such as drug counterfeiting, and the opportunities of using digital technologies to overcome them. It also evaluates the risks associated with the use of digital technologies. Expert interviews were conducted with pharmaceutical supply chain stakeholders in the first step. The data obtained was assessed through qualitative content analysis. In addition to the lack of transparency, strong individual interests of the supply chain actors and different implementation levels of digitisation were revealed. It is also noticeable that the industry is still paper-oriented. Nevertheless, the chosen experts are optimistic about the increased use of digital technologies. Combining blockchain with other technologies, such as IoT and AI, can improve efficiency, traceability, and trust in pharmaceutical supply chains. A digitalised supply chain concept is developed based on these technologies, enhancing transparency and information availability in the supply chain. In addition, communication and processes along the entire pharmaceutical supply chain are optimised.

When serious material flow disruptions occur in supply networks, complex interdependencies can lead to unpredictable effects which propagate through the entire network and endanger the existence of participating enterprises. Practitioners seek risk analysis methods that quantify risk propagation in the network as realistically and extensively as possible and present the results with sufficiently clear key figures and diagrams. Existing approaches, however, reveal their weaknesses precisely in these areas. While existing Bayesian network approaches only model the results of forward propagative effects through subjectively estimated and assumed conditional probabilities, current Agent-based Models provide insight into the dynamic interactions for specific research purposes, but fail to present the results of propagations concisely and to consider the network in total. This paper highlights the synergy effects of a combination of Agent-based Modeling and Bayesian networks not previously considered in the literature. A risk analysis methodology is presented that captures the complex interrelationships of various risk scenarios in disrupted supply networks. It offers both forward and backward risk propagations and direct and indirect risk consequences in a way that is easily digestible by management. For this purpose, three novel risk metrics for Bayesian networks are presented that quantify the exact risk propagation pattern in the network and provide a more accurate planning basis for mitigation strategies. Our methodology is applied to an excerpt of a supply network in the consumer goods industry and illustrates the approach and its benefits.

The digital transformation (DT) is reshaping the economy and society. In supply chains (SCs), DT involves adopting digital technologies to collaborate. DT opportunities are particularly diverse in globally distributed manufacturing networks (GDMN) and SCs. Here, DT influences both internal and external collaboration activities, i.e., configuration and coordination of the intra-firm network and network relationships, structure, and governance in the inter-firm network. This study investigates if and how DT changes relationship dynamics and collaboration efficiency in SCs and distributed manufacturing networks through information sharing and jointly used digital technologies. While existing studies have mainly focused on individual digital technologies and their potential for SCs and manufacturing networks, this study contributes to a better understanding of the adoption process and the change in relationship dynamics through DT and joint use of digital technologies. The methodology follows a qualitative approach in a multi-case study setting with six multi-national manufacturing companies operating extensive intra-firm and inter-firm networks. A theoretical framework based on organisational information processing theory guides the study. Data is collected in semi-structured interviews and enriched by secondary data from internal company documents and publicly available sources. The results indicate that digital tools are triggering a centralisation trend in intra-firm networks that leverages efficiencies but is met with stakeholder scepticism. SC collaboration is becoming increasingly dynamic through digital tools, which the SC partners often promote. Non-adopters are not being dropped yet, but the pressure of digital transformation is increasing and becoming more of a threat to small businesses.

One prospect of additive manufacturing (AM) is location-independent and on-demand production from digital files. Implementing the technology influences production in the company but also simplifies and shortens the holistic supply chain. While these impacts have been increasingly studied, there is a lack of contributions that look at the impact of AM on internal logistical processes. For this reason, an empirical study was conducted, whereby semi-structured expert interviews were chosen as the survey instrument to extend the existing theory. As a result, procurement, warehousing, production, and distribution effects can be shown. The results also compare current potentials and barriers in practice with the literature. The contribution is rounded with practical implications and further research to advance the development in the AM field.

Energy efficiency is a topic that has become central among many consumers and industries. Companies need to minimize production costs and, at the same time, reduce energy consumption and follow policy measures to reduce greenhouse gas emissions. One possibility to contribute to the sustainable design of production processes is the inclusion of energy consumption as a parameter in the optimization technique used during production scheduling. Although energy-efficient scheduling and optimization have become a scientific focus in production scheduling, practical applications are limited. The motivation of this paper is to extract energy-efficient production scheduling mechanisms from the literature using a literature review and to discuss the concepts with experts from the field. Finally, discrepancies between the needs of the industry and the scientific literature are revealed, and solution approaches to remedy the differences are proposed.

This work aims at conducting a comprehensive investigation of network structures of humanitarian organisations and elaborating the resulting influences on their logistical activities. This is the first work that connects the organisational structures of humanitarian organisations with logistical aspects. Organisational components and their relationships, as well as influencing factors, are identified, and essential scientific and practical implications derived. For this purpose, a multiple case study based on ten humanitarian non-governmental organisations (NGOs) was conducted. Interviews and surveys, as well as publicly available and internal documents of the organisations, served as research material. In this case study, four components of networks were identified, and their interrelationships described. The logistical influences were consequently analysed. It is shown that the dimensions of formalisation, centralisation, and standardisation in humanitarian organisational networks have an impact on logistics activities. This paper further provides theoretical and practical implications.

Lot-to-order matching (LTOM) is a crucial process in semiconductor manufacturing since inefficient allocation and order release have strong adverse effects on factory performance. Although prior research proposes several heuristics for the mathematical optimization of the LTOM process, successful real-world implementations following practical and comprehensive approaches are scarce. Our longitudinal case study addresses that issue by summarizing the results of an extensive research project on the automation and optimization of the LTOM process for 200 mm and 300 mm wafers at Infineon Technologies Dresden. Grounded in Action Design Research, we integrated different research methods to provide meaningful insights into the benefits, challenges, and best practices of our approach. Thereby, we also compare the results for 200 mm and 300 mm wafers. The project had positive impacts on multiple quantitative and qualitative key performance indicators, e.g., throughput, on-time delivery, tool utilization, cycle and working time savings, collaboration, and employee satisfaction. Finally, we provide managerial guidance for similar projects and implications for future research.

Robotic Process Automation (RPA) has received growing attention within the digital transformation as this cutting-edge technology automates human behavior and promises high potentials. However, the adoption in purchasing and supply management (PSM) is still in its infancy and has hardly been explored, particularly in the public sector. Based on a multiple case study including 19 organizations of the public and private sector, this paper narrows that gap and presents comprehensive insights into potentials, barriers, suitable processes, and best practices and components for RPA implementation. The findings indicate that adoption depends on the orga-nizations' digital procurement readiness and maturity. Application areas of RPA enlarge with increasing expe-rience and range from transactional and operative tasks within the procure-to-pay process to more strategic use cases in sourcing and supply relationship management. Potentials mainly comprise employee reliefs, cost sav-ings, and increased operational efficiency and quality. We uncover multiple technical, organizational, and environmental barriers related to IT infrastructure and human resources, internal communication, financial resources, top management support, organizational structures, supplier-related issues, and government regula-tions. Furthermore, our study indicates several differences between the private and public sectors for RPA implementation. We outline implications for the emerging research on RPA and pivotal directions for organi-zational practice.

ZusammenfassungDer Anteil der Wertschöpfung an Produkten durch Lieferanten hat in den vergangenen Jahren stetig zugenommen. Dies bedingt eine hohe Komplexität von Lieferketten und stellt das strategische und operative Beschaffungswesen vor große Herausforderungen. Gleichzeitig steht heute eine Vielzahl an Technologien zur Verfügung, um diese Komplexität zu bewältigen, Informationsasymmetrien abzubauen und transparente, fehlersichere Prozesse zu ermöglichen.Während die Potenziale digitaler Lieferantennetzwerke weitgehend evident sind, sind digitale Technologien wie Internet of Things (IoT), Blockchain oder künstliche Intelligenz (KI) bisher kaum praktisch in Unternehmen implementiert.Mit Hilfe einer empirischen Fallstudie wurde untersucht, inwieweit die Digitalisierung als strategisches Unternehmensziel verfolgt wird und welche Hindernisse bei der Einführung digitaler Lieferantennetzwerke bestehen. Dazu wurden elf Experten aus der strategischen und operativen Beschaffung von acht Unternehmen des produzierenden Gewerbes in Form einer leitfadengestützten Interviewstudie befragt und deren Erfahrungen ausgewertet.Die Ergebnisse implizieren, dass in vielen Fällen grundlegende Voraussetzungen für eine erfolgreiche digitale Transformation fehlen und in vielen Unternehmen keine ausreichenden Ressourcen dafür zur Verfügung stehen. Weiterhin wurde festgestellt, dass der Nutzen digitaler Technologien in der Beschaffung häufig sehr einseitig ist und bisher nur selten Netzwerkvorteile genutzt werden. Um dieser zögerlichen Entwicklung entgegenzuwirken, werden Handlungsempfehlungen für eine erfolgreiche Implementierung und stärkere Kooperation in der Lieferkette aufgestellt und weiterer Forschungsbedarf identifiziert.