Multilayer Network-Based Production Flow Analysis

Introduction

Industry 4.0 is a strategic approach to design optimal production flows by integrating flexible and agile manufacturing systems with Industrial Internet of Things (IIoT) technology enabling communication between people, products, and complex systems. The integration of manufacturing and information systems is, however, a challenging task. Horizontal and intercompany integration should connect the elements of the supply chain, while vertical integration should connect information related to the entire product life cycle. According to this new concept, the improvement and optimization of production technologies based on cyber-physical systems (CPS) are realized by the simultaneous utilization of information related to production systems, products, models, simulators, and process data.

CPS- and Industry 4.0-type solutions also enable the compositions of smaller cells providing more flexibility with regard to production. This idea leads to decentralized manufacturing and emerging next generation machine systems. This trend highlights the importance of the relationship between flexibility and complexity.

The complexity of production systems can be divided into the physical and functional domains. To analyze this aspect, our focus is on the production flow analysis of production systems as production analysis has multiple perspectives according to the hierarchical decomposition of the production system: (1) production flow analysis studies the activities needed to make each part and machines to be used to simplify the material flow. (2) Company flow analysis studies the flow of materials between different factories to develop an efficient system in which each facility completes all the parts it makes. (3) Factory flow analysis plans the division of the factory into groups or departments each of which manufactures all the parts it makes and plans a simple unidirectional flow system by joining these departments. (4) Group analysis divides each department into groups, each of which completes all the parts it makes – groups which complete parts with no backflow, crossflow (between groups), and no need to buy any additional equipment. (5) Line analysis analyzes the flow of materials between the machines in each group to identify shortcuts in the plant layout, and (6) tooling analysis tries to minimize setup time by finding sequences that minimize the required additional tooling for the following job.

Production flow analysis (PFA) is a technique to identify both groups and their associated "families" by analyzing the information in component process routes which show the activities (often referred as operations) needed to make each part and the machines to be used for each activity. Every production flow analysis begins with data gathering during which nonvalue adding activity should be optimized. When dealing with large quantities of manufacturing data, a representational schema that can efficiently represent structurally diverse and dynamical system have to be taken into consideration. Standards like ISO 18629, 10303 (STEP), and 15531 (MANDATE) support information flow by standardizing the description of production processes. Based on these standards and web semantics, a manufacturing system engineering (MSE) knowledge representation scheme, called an MSE ontology model, was developed as a modeling tool for production. The MSE ontology model by its very nature can be interpreted as a labeled network.

A simple multidimensional representation is proposed that can unfold the complex relationships of production systems. Network models are ideal to represent connections between objects and properties. However, as a multidimensional problem that requires flexibility due to the continuously growing amount of information is in question and a new multidimensional approach in the form of a multilayer network is presented.

For the analysis of the resultant ontology-driven labeled multilayer network, techniques to facilitate cell formation and competency assignment for operators were developed.

Manufacturing cell formation aims to create manufacturing cells from a given number of machines and products by partitioning similar machines which produce similar products. Standard cell formation problems handle products and machines while their connections are represented by two-layered bipartite graphs or machines-products incidence matrices. Classical algorithms are based on clustering and seriation of the incidence matrices. Recently, various alternative algorithms have been developed, for example, self-organizing maps of fuzzy clustering-based methods. What is common in most of these approaches is that they only take two variables into account. However, complex manufacturing processes should be characterized by numerous properties, like the type of products and resources, and the required skills of operators should be also taken into account at successful line balancing since the skills of the operators are influencing the speed of the conveyor belt. Dynamic job rotation also requires efficient allocation of the assembly tasks while taking into account the constraints related to the available skills of the operators.

To handle these elements of the production line, the traditional cell formation problem was extended into a multidimensional one. The main idea is to represent these problems by multilayered graphs and apply modularity analysis to identify the groups of items that could be handled together to improve the production process.

An entirely reproducible benchmark problem was designed to demonstrate our methodology. As an example, the problem of process flow analysis of wire-harness production was selected as this product is complex and varies significantly as the geometries and components of the harness vary depending on the final products. Since there are challenges in the selection of the cost-effective design and the demand for flexibility and a short delivery time urge the definition of product families produced from the submodules, the problem requires the advanced integration of process- and product-relevant information.

The remaining part of the paper is structured as follows. In Section 2, a multilayer network model is formalized that was developed to represent production systems. In Section 3, how production flow analysis problems can be interpreted as network analysis tasks is discussed. Section 3.1 describes the applicability of network science in PFA. Section 3.2 formalizes the projection of the multilayer networks and studies how conditional connections can be defined, while Section 3.3 applies this projection to calculate the node similarities. The group formation task is described in Section 3.4, where the results of this approach on benchmark examples are also presented. The detailed case study starts in Section 4 with the definition of the wire-harness production use case. The details of the problem are given in the Appendix. Section 4.1 demonstrates the applicability of similarity and modularity analysis. The workload analysis is given in Section 4.2, while interesting applications related to the evaluation of the flexibility of operator-task assignment problems are discussed in Section 4.3. Finally, conclusions are drawn in Section 5.