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Research Article Multilayer Network-Based Production Flow Analysis Hindawi Complexity Volume 2018, Article ID 6203754, 15 pages https://doi.org/10.1155/2018/6203754 Research Article Multilayer Network-Based Production Flow Analysis Tamás Ruppert, Gergely Honti, and János Abonyi MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Veszprém, Hungary Correspondence should be addressed to János Abonyi; [email protected] Received 24 February 2018; Revised 18 May 2018; Accepted 27 May 2018; Published 16 July 2018 Academic Editor: Dimitris Mourtzis Copyright © 2018 Tamás Ruppert et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. 1. Introduction systems [14]. This trend highlights the importance of the relationship between flexibility and complexity [15]. Industry 4.0 is a strategic approach to design optimal produc- The complexity of production systems can be divided tion flows by integrating flexible and agile manufacturing into the physical and functional domains [16]. To analyze systems with Industrial Internet of Things (IIoT) technology this aspect, our focus is on the production flow analysis of [1] enabling communication between people, products, and production systems as production analysis has multiple per- complex systems [2–4]. The integration of manufacturing spectives according to the hierarchical decomposition of the and information systems is, however, a challenging task [5]. production system: (1) production flow analysis studies the Horizontal and intercompany integration should connect activities needed to make each part and machines to be used the elements of the supply chain [6], while vertical integra- to simplify the material flow. (2) Company flow analysis tion should connect information related to the entire product studies the flow of materials between different factories to life cycle [7]. According to this new concept, the improve- develop an efficient system in which each facility completes ment and optimization of production technologies based all the parts it makes. (3) Factory flow analysis plans the divi- on cyber-physical systems (CPS) are realized by the simul- sion of the factory into groups or departments each of which taneous utilization of information related to production manufactures all the parts it makes and plans a simple unidi- systems [8], products, models [9], simulators, and process rectional flow system by joining these departments. (4) data [10, 11]. Group analysis divides each department into groups, each CPS- and Industry 4.0-type solutions also enable the of which completes all the parts it makes—groups which compositions of smaller cells providing more flexibility with complete parts with no backflow, crossflow (between regard to production [12]. This idea leads to decentralized groups), and no need to buy any additional equipment. (5) manufacturing [13] and emerging next generation machine Line analysis analyzes the flow of materials between the 2 Complexity machines in each group to identify shortcuts in the plant An entirely reproducible benchmark problem was layout, and (6) tooling analysis tries to minimize setup time designed to demonstrate our methodology. As an example, by finding sequences that minimize the required additional the problem of process flow analysis of wire-harness produc- tooling for the following job [17]. tion was selected as this product is complex and varies signif- Production flow analysis (PFA) is a technique to identify icantly [30] as the geometries and components of the harness both groups and their associated “families” by analyzing the vary depending on the final products [31]. Since there are information in component process routes which show the challenges in the selection of the cost-effective design [32] activities (often referred as operations) needed to make each and the demand for flexibility and a short delivery time urge part and the machines to be used for each activity [18, 19]. the definition of product families produced from the submo- Every production flow analysis begins with data gathering dules [33], the problem requires the advanced integration of during which nonvalue adding activity should be optimized process- and product-relevant information. [20]. When dealing with large quantities of manufacturing The remaining part of the paper is structured as follows. data, a representational schema that can efficiently represent In Section 2, a multilayer network model is formalized that structurally diverse and dynamical system have to be taken was developed to represent production systems. In Section 3, into consideration. Standards like ISO 18629, 10303 (STEP), how production flow analysis problems can be interpreted as and 15531 (MANDATE) support information flow by stan- network analysis tasks is discussed. Section 3.1 describes the dardizing the description of production processes [21]. Based applicability of network science in PFA. Section 3.2 formalizes on these standards and web semantics, a manufacturing sys- the projection of the multilayer networks and studies how tem engineering (MSE) knowledge representation scheme, conditional connections can be defined, while Section 3.3 called an MSE ontology model, was developed as a modeling applies this projection to calculate the node similarities. The tool for production [22]. The MSE ontology model by its very group formation task is described in Section 3.4, where the nature can be interpreted as a labeled network. results of this approach on benchmark examples are also pre- A simple multidimensional representation is proposed sented. The detailed case study starts in Section 4 with the def- that can unfold the complex relationships of production sys- inition of the wire-harness production use case. The details of tems. Network models are ideal to represent connections the problem are given in the Appendix. Section 4.1 demon- between objects and properties [23]. However, as a multidi- strates the applicability of similarity and modularity analysis. mensional problem that requires flexibility due to the contin- The workload analysis is given in Section 4.2, while inter- uously growing amount of information is in question and a esting applications related to the evaluation of the flexibil- new multidimensional approach in the form of a multilayer ity of operator-task assignment problems are discussed in network [24] is presented. Section 4.3. Finally, conclusions are drawn in Section 5. For the analysis of the resultant ontology-driven labeled multilayer network, techniques to facilitate cell formation and competency assignment for operators were developed. 2. Multilayer-Network Representation of Manufacturing cell formation aims to create manufactur- Production Systems ing cells from a given number of machines and products by partitioning similar machines which produce similar prod- Essential information about the products to be assembled, parts to be manufactured, materials to be used, methods and ucts. Standard cell formation problems handle products fi and machines while their connections are represented by techniques to convert the material to the required nished two-layered bipartite graphs or machines-products incidence components, and manpower to operate the plant is usually matrices. Classical algorithms are based on clustering and available to a company, but rarely in an appropriate form for seriation of the incidence matrices. Recently, various alterna- ease of digestion by the manager [34]. In this section, we pro- tive algorithms have been developed, for example, self- pose a network-based model to study the relationship between organizing maps [25] of fuzzy clustering-based methods these elements. [26]. What is common in most of these approaches is that As can be seen in Figure 1, the proposed network consists they only take two variables into account [27]. However, of a set of bipartite graphs representing connections between the sets of products p = p , … , pN , machines/workstations complex manufacturing processes should be characterized 1 p w w … w c C … C by numerous properties, like the type of products and = 1, , Nw , parts/components = 1, , NC , resources, and the required skills of operators should be also a a … a activities (operations) = 1, , Na , and their categorical taken into account at successful line
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