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See detailComplex Networks in Manufacturing - Suitability and Interpretation
Omar, Yamila UL

Doctoral thesis (2021)

The fourth industrial revolution, and the associated digitization of the manufacturing industry, has resulted in increased data generation. Industry leaders aim to leverage this data to enhance ... [more ▼]

The fourth industrial revolution, and the associated digitization of the manufacturing industry, has resulted in increased data generation. Industry leaders aim to leverage this data to enhance productivity, boost innovation and generate new manners of competition. In this work, out of the many domains within the manufacturing sector, production will be explored. To this end, the mathematical tools of network science are utilized to characterize and evaluate production networks in terms of complex networks. In a manufacturing complex network, nodes represent workstations, and directed edges abstract the material flow that occurs among pairs of workstations. These types of complex networks are known as "material flow networks" and are used to study issues associated with manufacturing systems in the domain of production at the intra-enterprise level. While some research on the subject exists, this work will demonstrate that the use of complex networks to describe and evaluate manufacturing systems constitutes a nascent research field. In fact, the limited existing literature tackles a vast number of issues raising more questions than providing answers. This work aims to answer a number of those open questions. Firstly, which complex network metrics are suitable in the context of manufacturing networks will be determined. As a consequence, unsuitable metrics will be identified as well. To accomplish this, the flow underlying assumptions of popular complex network metrics is studied and compared to those of manufacturing networks. Furthermore, other existing complex network metrics with more appropriate underlying assumptions, but not yet explored in the context of manufacturing, are proposed and evaluated. Then, the appropriate interpretation of suitable complex network metrics in terms of Operations Research is provided. Finally, shortcomings of these metrics are highlighted to caution practitioners regarding their use in industrial settings. [less ▲]

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See detailA Survey of Information Entropy Metrics for Complex Networks
Omar, Yamila UL; Plapper, Peter UL

in Entropy (2020)

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it ... [more ▼]

Information entropy metrics have been applied to a wide range of problems that were abstracted as complex networks. This growing body of research is scattered in multiple disciplines, which makes it difficult to identify available metrics and understand the context in which they are applicable. In this work, a narrative literature review of information entropy metrics for complex networks is conducted following the PRISMA guidelines. Existing entropy metrics are classified according to three different criteria: whether the metric provides a property of the graph or a graph component (such as the nodes), the chosen probability distribution, and the types of complex networks to which the metrics are applicable. Consequently, this work identifies the areas in need for further development aiming to guide future research efforts. [less ▲]

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See detailMaximum flow of complex manufacturing networks
Omar, Yamila UL; Plapper, Peter UL

in Procedia CIRP (2019)

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See detailLessons from social network analysis to Industry 4.0
Omar, Yamila UL; Minoufekr, Meysam UL; Plapper, Peter UL

in Manufacturing Letters (2018), 15B

With the advent of Industry 4.0, a growing number of sensors within modern production lines generate high volumes of data. This data can be used to optimize the manufacturing industry in terms of complex ... [more ▼]

With the advent of Industry 4.0, a growing number of sensors within modern production lines generate high volumes of data. This data can be used to optimize the manufacturing industry in terms of complex network topology metrics commonly used in the analysis of social and communication networks. In this work, several such metrics are presented along with their appropriate interpretation in the field of manufacturing. Furthermore, the assumptions under which such metrics are defined are assessed in order to determine their suitability. Finally, their potential application to identify performance limiting resources, allocate maintenance resources and guarantee quality assurance are discussed. [less ▲]

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