Multilayer Network‐Based Production Flow Analysis

Abstract

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.

Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production

“…Identifying the core nodes and investigating the critical edges or node-degree distribution within the network structure can provide internal information about the production process. e multilayer-based approach of manufacturing analytics and the case study, which has been presented, have been used in other papers written by the authors to verify production flow analysis [25] and solve assembly line balancing problems [26]. In the following, we detailed the wire harness manufacturing use case for ontology representation.…”

“…e challenge is that numerous activities and the highly manual assembly necessarily require optimum assembly line balancing [26]. e complexity of wire harness manufacturing is represented by a multilayer network [25] where the basic activities are connected to each layer. e information that needs to be acquired is a precise prediction of the duration of these activities [94], which can be measured by a fixture sensor.…”

“…We present a well-documented open-source OWL-XML (Web Ontology Language-Extensible Markup Language) based wire harness manufacturing benchmark. e presented case study has already been used to verify production flow analysis [25] and an assembly line balancing problem [26].…”

“…(i) Analyze the previously defined descriptive and influential factors with data queries and analyses. (ii) Because of the outstanding network analysis and visualization capabilities, it is worth generating labelled multilayer networks from these ontology models and graph databases [25,27].…”