A manufacturing systems network model for the evaluation of complex manufacturing systems. Issue 3 (8th April 2014)

• Record Type: Journal Article
• Authors: Becker, Till; Meyer, Mirja; Windt, Katja
• Is Part Of: International journal of productivity and performance management. Volume 63:Issue 3(2014)
• Page Start: 324
• Page End: 340
• Publication Date: 2014-04-08
• Subjects: Networks -- Performance measures -- Manufacturing systems -- Data analysis -- Complex network measures -- Large-scale data; Industrial productivity -- Periodicals; Performance -- Management -- Periodicals; Performance -- Measurement -- Periodicals; Time study -- Periodicals; Motion study -- Periodicals; 658.3125
• DOI: 10.1108/IJPPM-03-2013-0047
• Languages: English
• ISSNs: 1741-0401

Abstract

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.