Conference Agenda

Efficiently managing supply chains is vital for modern defense and space operations to ensure security, rapid deployment, and resource maximization (Grala et al., 2024; Wang and Jin, 2020). This paper examines two critical planning layers: macro-level transport selection and micro-level cargo space optimization. Specifically, we investigate the integration of Data Envelopment Analysis (DEA)—a mathematical method used to calculate the relative efficiency of different options (Charnes et al., 1978; Lepchak and Voese, 2020)—and intelligent three-dimensional bin packing problems (3D-BPP), which involve placing items of different sizes into fixed containers (Lodi et al., 2002; Gonzalez-San-Martin, 2024). Crucially, we extend this model to the domain of space logistics, where extreme weight and volume limits, tight rocket launch windows, and massive transport costs amplify supply chain risks (Bacon et al., 2022; Grogan et al., 2018). Following a rigorous PRISMA-compliant screening protocol (Page et al., 2021) of peer-reviewed papers published between 2016 and 2026, we systematically map how multi-criteria efficiency tools interact with automated loading plans across earth and space-based environments.

Our systematic literature review (SLR) reveals a deep disconnect between these two planning layers. On one hand, DEA models are highly effective at establishing efficiency baselines to balance costs, orbital cargo risks, and strategic priorities when selecting transport modes (Chao and Yu, 2022). However, DEA outputs are rarely linked dynamically to physical loading constraints (Chu et al., 2025). On the other hand, computer-driven approaches to 3D-BPP—such as Deep Reinforcement Learning (DRL)—excel at generating tight loading layouts under strict safety regulations, including load stability, center-of-gravity calculations, and hazardous material separation (Gao et al., 2025; Kaleta, 2025). Yet, these packing algorithms lack the broad, multi-layered risk and strategic fleet evaluation inherent to DEA frameworks, particularly when navigating civil-military shared infrastructure under dynamic uncertainty (Pishvaee et al., 2011; Verma and Dynamic Group, 2023).

Based on our synthesis, we identify three critical research gaps in the current state-of-the-art:

1. A severe lack of real-time data integration under sudden operational and space-weather uncertainty (Ivanov et al., 2010; Szymczak, 2017).
2. Insufficient attention paid to shared civil-military fleet capacity allocation for space launch vehicles (Habibie, 2026).
3. A shortage of explainable AI (XAI) frameworks tailored to provide transparent justifications to human operators in safety-critical transportation environments (Gunning et al., 2019; Hu et al., 2026).

To bridge these gaps, we propose a new conceptual framework that feeds DEA efficiency scores directly as input states into reinforcement learning packing agents. This review establishes a comprehensive academic baseline for future empirical research, offering a structured, actionable pathway toward building autonomous, resilient, and eco-efficient earth and space-based defense supply chains (McKinnon, 2018; Sbihi and Eglese, 2010).

The increasing diffusion of Smart Connected Products (SCPs) is transforming how firms create, capture, and leverage data throughout product lifecycles. While SCPs have been extensively studied in the context of product innovation, servitization, and customer value creation, their implications for supply chain management (SCM) remain insufficiently understood. Existing SCM research widely focuses on improving information visibility through transactional data, such as point-of-sale information, to mitigate inefficiencies such as the bullwhip effect. SCPs introduce a new source of information by generating real-time usage and performance data during the post-purchase phase, potentially reshaping supply chain structures, information flows, and planning processes. Yet little is known about how organizations currently exploit this potential and how SCPs may transform supply chains in the future.

This study investigates the impact of SCPs on SCM through an exploratory multiple-case study. Four case studies were conducted in industries characterized by long product lifecycles and growing digital connectivity, including home appliances, building automation, and elevator systems. Data were collected through interviews with managers responsible for supply chain, service, and digitalization activities. Interviews were analyzed to identify current practices, challenges, and opportunities associated with the use of SCP-generated data in supply chain processes.

The findings reveal a substantial gap between the technological potential of SCPs and their current utilization in SCM. Although all companies recognize the value of SCP-generated data, none fully integrate these data streams into core demand planning processes. A major barrier is the activation gap between product purchase and customer activation of connected functionalities, which limits the availability and reliability of usage data for forecasting purposes. Consequently, traditional demand signals continue to dominate production and inventory planning. However, SCPs are already creating value in post-transaction activities, particularly predictive maintenance, fault detection, and spare-parts forecasting. Companies report improved visibility into component usage, more accurate service planning, and opportunities to reduce stockouts and excess inventory in after-sales operations. These findings are especially relevant given the persistence of bullwhip-effect dynamics in spare-parts supply chains.

Building on the empirical insights, the research illustrates how SCPs may reshape supply chains. SCPs enable the emergence of servitized supply chains, create new information flows from product usage back to manufacturers, and potentially alter power relationships among supply chain actors. Beyond supporting maintenance activities, SCP-generated data may enable forecasting of complementary products and services, thereby connecting previously independent supply chains. Such developments suggest a transition from transaction-driven supply chains toward data-driven supply chain ecosystems.

This study contributes to the growing literature on digital supply chains by highlighting SCPs as a novel source of supply chain management enhancement and by identifying the organizational and technological barriers that currently constrain their impact. The findings provide a foundation for future research on SCP-enabled forecasting, supply chain reconfiguration, and even data-centric business models, while offering managers practical insights into how SCP may enhance supply chain performance and resilience.

Background: Contemporary maritime logistics increasingly operates in close proximity to environmentally sensitive areas and regions highly dependent on tourism, where deficiencies in crisis management may lead to serious environmental and economic consequences. Despite the growing body of literature on maritime accidents and risk management, relatively little attention has been paid to the role of institutional non-action and delayed response as factors contributing to the escalation of risk.

Methods: This study is based on a qualitative case study of the VSG Glory incident off the coast of El Quseir in the Red Sea. The analysis draws on AIS data, secondary sources, and field observations conducted at the site of the event. The applied approach enabled the reconstruction of the sequence of events and the identification of key decision points at which the lack of institutional response influenced the further development of the situation.

Results: The findings indicate that the lack of coordinated action among key stakeholders—including the shipowner, the flag state, and coastal authorities—significantly contributed to the increase in environmental risk and threats to the tourism sector. The analysis shows that prolonged periods without intervention amplified potential damage and reduced the effectiveness of mitigation efforts. The case reveals a structural gap in existing governance frameworks, where responsibility for early intervention remains fragmented and ambiguous.

Conclusion: The study highlights the need to redefine crisis management approaches in maritime logistics by recognizing culpable omission as a distinct risk factor. The findings emphasize the importance of developing integrated, cross-sectoral coordination mechanisms linking logistics, environmental protection, and tourism management, particularly in ecologically sensitive regions. The study also indicates directions for future research on the role of institutional responsibility in preventing and mitigating the consequences of maritime incidents.