Simulation study of large production network robustness in uncertain environment

Putnik, Goran D.; Škulj, Gašper; Vrabič, Rok; Varela, Leonilde; Butala, Peter

doi:10.1016/j.cirp.2015.04.118

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…The inner workings of companies can also be considered as networks of departments and individual people. These networks are not only technical in nature, but are, besides having an organisational role, often underlined by social relationships and motives (Škulj et al, 2014;Putnik et al, 2015;Mourtzis et al, 2016a). Modelling the inter-and intra-company relations as networks and analysing their structure can be used as a powerful tool for the management of various aspects of business operations (Mourtzis et al, 2013;Lanza and Moser, 2014;Becker et al, 2014;Kito et al, 2015;Neto et al, 2016;Zhong et al, 2015;Acar and Atadeniz, 2015).…”

Section: Introductionmentioning

confidence: 99%

Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources

Kozjek

Vrabič

Eržen

et al. 2018

International Journal of Production Economics

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People, companies, and institutions form networks as part of their technical, economic, and social activities. As a consequence, these networks have an influence on how companies conduct business. Recently, the Internet, professional, and scientific social networks have contributed to the ease and simplicity of the network forming process and the public availability of the corresponding data. We investigate whether useful information about the relationships between individuals, companies, and institutions for the domain of production can be extracted from publicly available, structured and unstructured data that is merged from various Internet sources. We demonstrate that relevant information about the structures of these networks can be obtained by merging publicly available data using a combination of advanced computational methods including web crawling, machine learning, and creating mash-ups of publicly available services. The feasibility and the applicability of the approach are shown for a case in the automotive domain. A potential use case of the resulting data is demonstrated, showing how the approach can be used to find specific skills and expertise for a scientific

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Section: Introductionmentioning

confidence: 99%

Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources

Kozjek

Vrabič

Eržen

et al. 2018

International Journal of Production Economics

Self Cite

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“…• Collecting data and defining a model: the model definition was based on previous studies. The works of Skulj et al (2014) and Putnik et al (2015) were selected for this definition, which is describe in the following sections of this paper;…”

Section: Methodological Proceduresmentioning

confidence: 99%

“…The production network model developed in this paper was based on Skulj et al (2014) and Putnik et al (2015). The network's nodes represent work units, which can be both producers and/or consumers.…”

Section: Methodological Proceduresmentioning

confidence: 99%

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How connectivity and search for producers impact production in Industry 4.0 networks

Pereira

Simonetto

Putnik

et al. 2018

BJO&PM

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Technological evolutions lead to changes in production processes; the Fourth Industrial Revolution has been called Industry 4.0, as it integrates Cyber-Physical Systems and the Internet of Things into supply chains. Large complex networks are the core structure of Industry 4.0: any node in a network can demand a task, which can be answered by one node or a set of them, collaboratively, when they are connected. In this paper, the aim is to verify how (i) network's connectivity (average degree) and (ii) the number of levels covered in nodes search impacts the total of production tasks completely performed in the network. To achieve the goal of this paper, two hypotheses were formulated and tested in a computer simulation environment developed based on a modeling and simulation study. Results showed that the higher the network's average degree is (their nodes are more connected), the greater are the number of tasks performed; in addition, generally, the greater are the levels defined in the search for nodes, the more tasks are completely executed. This paper's main limitations are related to the simulation process, which led to a simplification of production process. The results found can be applied in several Industry 4.0 networks, such as additive manufacturing and collaborative networks, and this paper is original due to the use of simulation to test this kind of hypotheses in an Industry 4.0 production network.

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“…The considered production system consists on a flexible flow shop (FFS) [8,9], which is a simplification of a flexible job shop system [10]. A simulation study [11,12] was carried out in this work by considering the production of three different products (Products: A, B and C), which are processed at three production stages (i = 1, 2, and 3). On each stage, there are three different parallel machines (Mi: 1, 2 and 3) that execute the same kind of operation for each type of product.…”

Section: Introduction 11 Problem Descriptionmentioning

confidence: 99%

Analysis of Production Activity Control Mechanisms for Industry 4.0

2019

IJOMAM

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Production Activity Control Mechanisms (PACM) play a crucial role in Industry 4.0 oriented Production Management strategies and approaches as through these PACM it turns out to be possible to ensure the accomplishment of production orders by meeting jobs' due dates, along with a variety of other kind of requirements and constraints that usually characterize production management problems, such as product quality and costs requirements, among a set of other more problem-specific conditions. The objective of this paper is to put forward a comparative analysis about the application of three different mechanisms of Production Activity Control in a Flexible Flow Shop, based on simulation. Thus, an extended comparative analysis, based on several different comparison criteria and parameters is presented in this paper.

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Simulation study of large production network robustness in uncertain environment

Cited by 13 publications

References 13 publications

Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources

Identifying the business and social networks in the domain of production by merging the data from heterogeneous internet sources

How connectivity and search for producers impact production in Industry 4.0 networks

Analysis of Production Activity Control Mechanisms for Industry 4.0

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