A manufacturing systems network model for the evaluation of complex manufacturing systems

Abstract

Purpose – The topology of manufacturing systems is specified during the design phase and can afterwards only be adjusted at high expense. The purpose of this paper is to exploit the availability of large-scale data sets in manufacturing by applying measures from complex network theory and from classical performance evaluation to investigate the relation between structure and performance. Design/methodology/approach – The paper develops a manufacturing system network model that is composed of measures from complex network theory. The analysis is based on six company data sets containing up to half a million operation records. The paper uses the network model as a straightforward approach to assess the manufacturing systems and to evaluate the impact of topological measures on fundamental performance figures, e.g., work in process or lateness. Findings – The paper able to show that the manufacturing systems network model is a low-effort approach to quickly assess a manufacturing system. Additionally, the paper demonstrates that manufacturing networks display distinct, non-random network characteristics on a network-wide scale and that the relations between topological and performance key figures are non-linear. Research limitations/implications – The sample consists of six data sets from Germany-based manufacturing companies. As the model is universal, it can easily be applied to further data sets from any industry. Practical implications – The model can be utilized to quickly analyze large data sets without employing classical methods (e.g. simulation studies) which require time-intensive modeling and execution. Originality/value – This paper explores for the first time the application of network figures in manufacturing systems in relation to performance figures by using real data from manufacturing companies.

Topological Structure of Manufacturing Industry Supply Chain Networks

“…Although the divergence between the observed centrality distribution and the randomised ensemble average is generally found through visual inspection of the plots [17,32], in this study we have used the Kolmogorov-Smirnov test (KS test) to establish any statistically significant deviations. The KS test is a powerful statistical test that allows one to compare two distributions (the null hypothesis of the test is that there exists no difference between the two distributions).…”

Complex networks in advanced manufacturing systems

“…With regard to more advanced topological measures, a model using complex network figures is designed to improve heuristics for manufacturing system design [63]. As to the stability and reliability of manufacturing systems, robustness evaluation and measures, characterization and classification [64], as well as cascading failures on dynamic models of complex manufacturing network [65] are analyzed respectively. In order to evaluate a manufacturing system and its dynamic behaviors, a network motif is applied as a similarity indicator to assess the similarity and dissimilarity of manufacturing systems [57].…”

“…Finding: Known from the above 11 papers (7.3% of selected papers) [57][58][59][60][61][62][63][64][65][66][67] on this topic, these researches are mainly to create a manufacturing systems network model to address the following three issues: (1) how to model a manufacturing system as a network, (2) what dynamic behaviors in such a network are measured, and (3) how to detect and assess these behaviors. It shows that the network modeling of manufacturing systems is feasible and is particularly helpful for analysis of large manufacturing systems.…”

Lessons from social network analysis to Industry 4.0

“…The clustering coefficient describes the type of manufacturing network under study [9]. High values indicate highly interconnected workstations typical of cellular manufacturing, while low values are characteristic of rather serial 100 manufacturing plants.…”

“…Originally introduced to quantify the importance of an individual in a communication network in terms of controlling information flows [8], in the context 75 of manufacturing networks this metric indicates the centrality of a node and its potential to impede or facilitate materials flow through the network [9].…”