Automated Dispensing Machines (ADMs) are critical to ensuring safe and timely medication delivery in hospitals. As demand variability increases and storage capacity remains fixed, optimizing the utilization of existing ADMs has become a key operational challenge. While prior research has focused on system implementation or isolated improvements, limited work addresses the integrated optimization of deployed ADMs under real-world constraints. This paper proposes a data-driven framework that integrates machine learning–based demand forecasting, a periodic review (R, s, S) inventory policy, and capacity-constrained medication allocation. Using detailed ADM transaction data, the approach models medication demand at the medication–machine level, capturing temporal and operational variability. Forecast outputs inform inventory parameters under service-level targets, which are then linked to a storage allocation model that accounts for physical constraints and medication characteristics. The proposed framework enables coordinated decision-making across forecasting, inventory control, and allocation, improving space utilization and service performance without requiring additional hardware. Results from a real hospital case study demonstrate its practical effectiveness in enhancing ADM efficiency under demand uncertainty and capacity limitations.

Traditional factory planning assumes stable supply chains and static locations – assumptions that fail in disaster zones, conflict areas, and extraterrestrial settlements. This article introduces the hyperflexible production system (HFPS) paradigm to enable value-creation beyond traditional factory settings and constraints, defined by six system characteristics: software-defined, spatially relocatable, modular, circular, autonomous, and rapidly reconfigurable. A major challenge of the HFPS paradigm is the lack of planning methods to operationalize this paradigm. To address this challenge, this article applies the VDI 5200 factory planning standard to a fictional Martian use case.

Despite the recognised centrality of the water-energy-food (WEF) Neuxs approach and WEF security to sustainable development and global economic prosperity within international development agendas amid adverse impacts of climate change, a common framework that guides the comprehensive assessment of WEF security and health; and the multidimensional factors that induce vulnerability of populations on the household and community level has not been entirely realised. This paper presents a systematic literature review of the theoretical and methodological framings of WEF security, health and climate change impacts specifically in South Africa, was undertaken using SCOPUS as a core database for retrieval of multidisciplinary, peer-reviewed literature. Results indicate a critical dearth of empirical application of conceptual or model-based analysis of WEF security and climate change impacts on a local level, with only six studies conducted in South Africa. Findings further reveal: (1) WEF security and (public) health are implicitly and explicitly linked, with relative inaccessibility and unavailability of resources posing significant risk to poor health outcomes; (2) centralizing WEF security reveals secondary, cascading impacts that exacerbate underlying resource scarcity and socio-economic inequalities; (3) socio- and socio-political factors, governance, institutional factors and anthropogenic pressures i.e. rapid urbanisation and rising population growth emerged as macro-level drivers that transcend spatio-temporal scales and intensify direct and indirect effects of resource insecurity on the local level; (4) current quantification of WEF security is often simplified on a national, regional or sub-regional level dataset; (5) WEF resources are sectorally governed, with no single policy that addresses WEF security in South Africa. Overall, the results of this review demonstrated that within the existing research and management practice landscape there remains a need for integrated theoretical and methodological framings of the interrelations between WEF security, public health and climate change impacts on a local level, particularly in the South African context.

High-risk distributed training ecosystems, including those in the Chemical, Biological, Radiological, and Nuclear (CBRN) domain, require rapid, reliable, and intuitive access to structured domain knowledge to support preparedness and coordinated decision-making. While formal semantic knowledge models provide robust representations of domain entities and relationships, their operational utility is often constrained by direct exposure of low-level query interfaces and underlying schema structures. This paper presents a web-based ontology exploration application that enables non-expert users to navigate structured CBRN knowledge through guided interaction.
By combining semantic storage with a mediation service layer and an interactive exploration interface, the system enables workflow-oriented access to the information captured in the ontology while shielding users from underlying technical complexity. The proposed approach is implemented and validated within a collaborative CBRN training network, demonstrating a transferable architectural pattern for enabling controlled and user-oriented access to semantic knowledge systems in complex, multi-stakeholder environments.