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Supply chain optimization and resource allocation are challenging because of the complex dynamics of flows. We can classify these flows based on whether they perform value-added or nonvalue-added activities in our process. The aim of this article is to present a multilayered temporal network-based model for the analysis of network flows in supply chain optimization and resource allocation. Implementation of a multilayered network distinguishes value-added from nonvalue-added resource flows, enabling a comprehensive view of the flow of resources in the system. Incorporating weighted edges representing the probabilities of time-dependent flows identifies the resource needs and excesses at each supply site, addresses optimal transportation challenges for resource reallocation, and assesses the efficiency and robustness of the system by examining the overlaps in network layers. The proposed method offers a significant extension to the toolsets for network flow analysis, which has the potential to improve decision-making processes for organizations dealing with complex resource management problems. The applicability of the proposed method is demonstrated by analyzing the temporal network extracted from taxi cab flows in New York City. With the application of the method, the results indicate that significant reductions in idle times are achievable.

Effective information management is critical for the development of manufacturing processes. This paper aims to provide an overview of ontologies that can be utilized in building Industry 4.0 applications. The main contributions of the work are that it highlights ontologies that are suitable for manufacturing management and recommends the multilayer-network-based interpretation and analysis of ontology-based databases. This article not only serves as a reference for engineers and researchers on ontologies but also presents a reproducible industrial case study that describes the ontology-based model of a wire harness assembly manufacturing process.

A multilayer network model for the exploratory analysis of production technologies is proposed. To represent the relationship between products, parts, machines, resources, operators, and skills, standardized production and product-relevant data are transformed into a set of bi- and multipartite networks. This representation is beneficial in production flow analysis (PFA) that is used to identify improvement opportunities by grouping similar groups of products, components, and machines. It is demonstrated that the goal-oriented mapping and modularity-based clustering of multilayer networks can serve as a readily applicable and interpretable decision support tool for PFA, and the analysis of the degrees and correlations of a node can identify critically important skills and resources. The applicability of the proposed methodology is demonstrated by a well-documented benchmark problem of a wire-harness production process. The results confirm that the proposed multilayer network can support the standardized integration of production-relevant data and exploratory analysis of strongly interconnected production systems.