In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.

The present era is characterised by many events that have influences on supply chains and supply networks. This concerns, e.g., war, epidemics, natural disasters, accidents, strikes, political instability, and political sanctions. These are generally grouped under the term “disruption”. In order to avoid the risk of supply chain disruption, major disruption of supply networks, or loss of customers associated with disruptions, it is necessary to take preventive and proactive measures in supply chain management in terms of planning. This paper is intended to briefly summarise the current state of knowledge with the most important facts and derive a new definition from it. In addition, an analogy to maintenance is established for the first time. In doing so, a comparison of the concepts and a listing of the important proactive measures derived from them for increasing resilience are made. In the course of this, the field of action considered is extended from the exchange of suppliers through the entire supply chain network to the exchange of customers.

Traditional academic discourse has long prioritized published sources—such as monographs, peer-reviewed journal articles, conference proceedings, and legal or regulatory documents—as the sole authoritative references for scientific inquiry. While these sources undoubtedly provide a validated foundation of disciplinary knowledge, they also represent the codification of past insights and often lag behind emerging developments. This paper critically examines the limitations of this conventional epistemic framework and argues for its deliberate extension. In an era characterized by rapid information dissemination and knowledge creation across diverse platforms, a significant proportion of relevant expertise now resides outside the boundaries of traditional literature. Insights from domain experts, practitioners, real-time media (e.g., news reports, podcasts, video content), original data collection, experimental inquiry, and scholarly dialog increasingly constitute valuable sources of scientific knowledge. Drawing a parallel to data-driven disciplines, where historical records are complemented by real-time analytics and user-derived insights, this article outlines the categories of such contemporary knowledge that warrant academic recognition and proposes rigorous methodologies for their systematic integration into scholarly work.