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Journal articles on the topic "Manufacturing Network":
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Cheng, Yang, Sami Farooq, and John Johansen. "Manufacturing network evolution: a manufacturing plant perspective." International Journal of Operations & Production Management 31, no. 12 (November 15, 2011): 1311–31. http://dx.doi.org/10.1108/01443571111187466.
Thomas, Simone, Maike Scherrer-Rathje, Maria Fischl, and Thomas Friedli. "Linking network targets and site capabilities." International Journal of Operations & Production Management 35, no. 12 (December 7, 2015): 1710–34. http://dx.doi.org/10.1108/ijopm-10-2014-0464.
Purpose – To cope with the increasing challenges of globalisation, various manufacturing companies have established intra-company manufacturing networks. The purpose of this paper is to link the strategic network targets (i.e. accessibility, thriftiness represented by economies of scale and economies of scope, mobility, and learning) with the capabilities and characteristics of the constituting sites of the manufacturing network and thus facilitate the identification of mismatches and the discussion of possible development paths for the network. Design/methodology/approach – The authors derive a conceptual framework from the literature which is tested with a single case; a manufacturing network consisting of eight manufacturing sites. Case data were collected based on 24 interviews with operations managers at network level and a standardised online survey among the sites’ management teams. Findings – Results indicate that manufacturing network and site levels need to be related when striving for a fit between manufacturing strategy, network strategy, and site capabilities and characteristics. Research limitations/implications – Single case studies have only limited generalisability. Nevertheless, the case demonstrates the usefulness of several dimensions when analysing manufacturing networks on site and network level. Originality/value – This paper represents the first attempt to link strategic targets of intra-company manufacturing networks with site capabilities and characteristics. Thereby, it provides a theoretical basis for future research on the relationship between strategic targets of manufacturing networks and their fulfilment. The developed manufacturing site portfolio, moreover, extends the site role typology discussion by a multi-dimensional perspective and represents a starting point for a multi-faceted discussion of site roles.
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Dan, B., L. Li *, X. Zhang, F. Guo, and J. Zhou. "Network-integrated manufacturing system." International Journal of Production Research 43, no. 12 (June 15, 2005): 2631–47. http://dx.doi.org/10.1080/00207540500044892.
Zhang, Ying Xin, Chao Chen, and Jian Mai Shi. "The Property of Hierarchical Structure in Manufacturing." Advanced Materials Research 748 (August 2013): 1188–93. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1188.
Hierarchical structure is one of the most ubiquitous structures in various networked complex systems. In order to investigate the properties of the hierarchical structure, a deterministic hierarchical network model is first proposed. The statistical properties of the constructed networks are also discussed. The simulation results prove that the artificial networks simultaneously possess the small-world and scale-free properties well. This may be useful in furthering study of the topology properties of the hierarchical networks in real life.
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Becker, Till, Mirja Meyer, and Katja Windt. "A manufacturing systems network model for the evaluation of complex manufacturing systems." International Journal of Productivity and Performance Management 63, no. 3 (April 8, 2014): 324–40. http://dx.doi.org/10.1108/ijppm-03-2013-0047.
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.
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Poler, Raul, Jorge E. Hernandez, Josefa Mula, and Francisco C. Lario. "Collaborative forecasting in networked manufacturing enterprises." Journal of Manufacturing Technology Management 19, no. 4 (May 2, 2008): 514–28. http://dx.doi.org/10.1108/17410380810869941.
PurposeThis paper seeks to propose an overall model of collaborative forecasting for networked manufacturing enterprises.Design/methodology/approachContributions by several authors to collaborative forecasting have been analysed from different viewpoints. A collaborative‐forecasting model for networked manufacturing enterprises has been proposed and validated by means of a simulation study.FindingsThis model significantly reduces the inventory levels of the whole network and improves customer service.Research limitations/implicationsSimulation experiments were done with the enterprise network herein described. Future research will include the simulation of more complex enterprise network scenarios with different characteristics.Practical implicationsThe model can be implemented node‐to‐node, since not all the companies in the network have to participate, thus facilitating implementation and propagation throughout the network.Originality/valueThe paper proposes a new structured planning and forecasting collaboration model for networked manufacturing enterprises.
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Mo, Xin Ju. "Study on Information Security of Network-Based Manufacturing Environment." Applied Mechanics and Materials 484-485 (January 2014): 493–96. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.493.
with the rapid development and wide application of networking technology, information security increasingly prominent problems are evident, more and more attention. The article introduced the current situation of network information security, explored the meaning of network information security, major threat analysis network information security, and finally gives the implementation technology of network information security and preventive measures, to ensure the information security of computer networks, so as to give full play to role of computer networks.
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Lin, Yu-Cheng, and Toly Chen. "A ubiquitous manufacturing network system." Robotics and Computer-Integrated Manufacturing 45 (June 2017): 157–67. http://dx.doi.org/10.1016/j.rcim.2015.10.009.
Dissertations / Theses on the topic "Manufacturing Network":
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Yao, James Chung-Yo. "Leaders for Manufacturing Program electronic mail network." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/61055.
Wu, Dazhong. "Cloud-based design and manufacturing: a network perspective." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53029.
The motivation of this research is the need for reducing time and cost associated with maintaining information and communication technology infrastructure for design and manufacturing in digitally networked environments, enhancing design communication and collaboration in distributed and collaborative design processes, and adapting to rapidly changing market demands. The objective of this dissertation is to propose a new design and manufacturing paradigm, namely, Cloud-Based Design and Manufacturing (CBDM), for enhancing collaborative product development in distributed settings. In this dissertation, the following challenges pertaining to CBDM are addressed: the systematic development of a conceptual framework and a holistic vision for CBDM systems; the development of a new approach for visualizing distributed and collaborative design processes, measuring tie strengths in a complex and large design collaboration network, and detecting design communities with common design activities in cloud-based design (CBD) settings from a social network perspective; and the development of a new approach that helps identify potential manufacturing bottlenecks and scale manufacturing capacity in cloud-based manufacturing (CBM) settings from a manufacturing network perspective. The contributions of this dissertation are categorized in three research domains: (1) proposing the first definition, a holistic vision, and an example of application scenario for CBDM, (2) modeling and analyzing information flow in cloud-based design for improving design collaboration, and (3) modeling and analyzing material flow in cloud-based manufacturing for planning manufacturing scalability.
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Heinz, Michael Pierre. "Network master planning for a global manufacturing company." Thesis, Berlin dissertation.de, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2839589&prov=M&dok_var=1&dok_ext=htm.
A smart manufacturing network connects machines via sensing, communication, and actuation networks. The data generated from the networks are used in data-driven modeling and decision-making to improve quality, productivity, and flexibility while reducing the cost. This dissertation focuses on improving the data-driven modeling of the quality-process relationship in smart manufacturing networks. The quality-process variable relationships are important to understand for guiding the quality improvement by optimizing the process variables. However, several challenges emerge. First, the big data sets generated from the manufacturing network may be information-poor for modeling, which may lead to high data transmission and computational loads and redundant data storage. Second, the data generated from connected machines often contain inexplicit similarities due to similar product designs and manufacturing processes. Modeling such inexplicit similarities remains challenging. Third, it is unclear how to select representative data sets for modeling in a manufacturing network setting, considering inexplicit similarities. In this dissertation, a data filtering method is proposed to select a relatively small and informative data subset. Multi-task learning is combined with latent variable decomposition to model multiple connected manufacturing processes that are similar-but-non-identical. A data filtering and modeling framework is also proposed to filter the manufacturing data for manufacturing network modeling adaptively. The proposed methodologies have been validated through simulation and the applications to real manufacturing case studies. Doctor of Philosophy The advancement of the Internet-of-Things (IoT) integrates manufacturing processes and equipment into a network. Practitioners analyze and apply the data generated from the network to model the manufacturing network to improve product quality. The data quality directly affects the modeling performance and decision effectiveness. However, the data quality is not well controlled in a manufacturing network setting. In this dissertation, we propose a data quality assurance method, referred to as data filtering. The proposed method selects a data subset from raw data collected from the manufacturing network. The proposed method reduces the complexity in modeling while supporting decision effectiveness. To model the data from multiple similar-but-non-identical manufacturing processes, we propose a latent variable decomposition-based multi-task learning model to study the relationships between the process variables and product quality variable. Lastly, to adaptively determine the appropriate data subset for modeling each process in the manufacturing network, we further proposed an integrated data filtering and modeling framework. The proposed integrated framework improved the modeling performance of data generated by babycare manufacturing and semiconductor manufacturing.
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Onyshchenko, Elena. "Characterisation of network structure in recycled rubber." Thesis, London Metropolitan University, 2016. http://repository.londonmet.ac.uk/1226/.
Produced by existing size reduction technologies, rubber crumb is the most valuable type of recycled material. However, only a limited amount of untreated crumb can be used in new tyres due to significant property loss. The de-vulcanisation process offers the potential for providing high quality recycled material that can be used as a substitute for virgin rubber. Rubber materials produced during industrial trials that formed the development stages of the novel DeVulCO”2 de-vulcanisation technology, were used in this work. A practical way to characterise de-vulcanised and re-vulcanised waste tyre materials and blends with virgin compound was established. This work showed that simple to obtain and understand parameters, acquired from tests that are commonly used in the rubber industry, such as Mooney viscosity, Mooney relaxation and curing can be successfully used to indicate the efficiency of the de-vulcanisation process and hence, to assist the research and development stages of the de-vulcanisation process and to assure quality control. Good correlation was evident between these parameters and the network characterisation parameter (Vr) obtained from the swelling measurements that are known to be a reliable guide but are more time consuming to perform. In this work, the Mooney relaxation test was successfully extended to characterise some de-vulcanised compounds (automotive weather strip and clothing tape). In addition, the swollen-state 1H-NMR technique was successfully applied to characterise de-vulcanised and subsequently re-vulcanised “closed-loop” tyre materials. A new tool to measure the efficiency of the de-vulcanisation process has been devised, based on a comparison of the peak areas in the olefinic region of rubber extracts analysed by the liquid 1H-NMR technique. This technique is capable of characterising different components of a rubber blend (e.g. NR, BR) and is suitable for carbon black filled compounds. The results of this work have confirmed the potential of the DeVulCO”2 technology and have indicated that up to 20% of de-vulcanisate can be blended with virgin tyre compound without significant deterioration of the key performance properties.
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Wan, Yen-Tai. "Material transport system design in manufacturing." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-03282006-231022/.
Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2006. Dr. Yih-Long Chang, Committee Member ; Dr. Martin Savelsbergh, Committee Member ; Dr. Leon McGinnis, Committee Co-Chair ; Dr. Gunter Sharp, Committee Chair ; Dr. Doug Bodner, Committee Member ; Dr. Joel Sokol, Committee Member.
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Pardasani, Ajit Carleton University Dissertation Information and Systems Science. "Network flow optimization models for integrated flexible manufacturing systems." Ottawa, 1989.
The participation of small and medium enterprises in inter-firm collaboration can enhance their market reach while maintaining production lean. The conventional centralised collaboration approach is believed to be unsustainable, in today’s complex environment. The research aimed to investigate manufacturing network collaborations, where manufacturers maintain control over their scheduling activities and participate in a market-based event, to decide which collaborations are retained. The work investigated two pairing mechanisms where the intention was to capture and optimise collaboration at the granular level and then build up a network from those intermediate forms of organisation. The research also looked at two bidding protocols. The first protocol involves manufacturers that bid for operations from the process plan of a job. The second protocol is concerned with networks that bid for a job in its entirety. The problem, defined by an industrial use case and operation research data sets, was modelled as decentralised flow shop scheduling. The holonic paradigm identified the problem solving agents that participated in agent-based modelling and simulation of the pairing and the bidding protocols. The protocols are strongly believed to achieve true decentralisation of scheduling, with good performance on scalability, conflict resolution and schedule optimisation, for the purpose of inter-firm collaboration.
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Frels, Judy Kathleen. "Explaining adoption in network markets : the product ecology framework /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
Chhajed, Dilip. Flow network design for manufacturing systems layout. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1990.
Kokkinaki, A. I. Network architectures in computer integrated manufacturing: Antecedents and directions. Leicester: SERCentre, De Montfort University, 1996.
Corporation, General Motors. Manufacturing automation protocol: A communications network protocol for open systems intercommunication. Warren, Mi: General Motors Corporation, 1986.
Noori, Hamid, and W. B. Lee. "Dispersed Network Manufacturing: An Emerging Form of Collaboration Networks." In Dispersed Manufacturing Networks, 39–58. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-468-3_3.
Hyötyläinen, Raimo, and Magnus Simons. "The Network Cell as a Step to the Network Factory." In International Manufacturing Strategies, 369–84. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2795-1_22.
Pauksztat, Birgit, and Denise Salin. "Workplace Bullying: A Social Network Perspective." In Precision Manufacturing, 1–36. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5334-4_14-1.
Scholz-Reiter, Bernd, Christian Meinecke, and Daniel Rippel. "Network Collaboration." In Intelligent Non-hierarchical Manufacturing Networks, 169–84. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118607077.ch8.
Martin, P., and E. Arnould. "Introduction to CNMA Network Management." In Communications for Manufacturing, 162–79. London: Springer London, 1990. http://dx.doi.org/10.1007/978-1-4471-1820-6_14.
Thomopoulos, Nick T. "Distribution Network." In Elements of Manufacturing, Distribution and Logistics, 211–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26862-0_11.
Conference papers on the topic "Manufacturing Network":
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Moyne, James, and Dawn Tilbury. "Determining Network Control Solution Designs for Manufacturing Systems." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59492.
In addressing the need for manufacturing systems that are more reconfigurable and flexible, there is an ever-increasing focus on utilizing networks at all levels to support control, diagnostics and safety functionality. Issues of performance and cost must be addressed. Further, making an appropriate network choice often involves examining the application environment rather than just universally applying a set of heuristics. The result is that network decisions, such as protocol, medium, and partitioning, are often made with incomplete or inappropriately prioritized information, leading to sub-optimal solutions in terms of both cost and performance. Three perspectives of information are needed for effective decision-making in network control system (NCS) deployment, namely theoretical, experimental and analytical. Theoretical involves understanding network operation, identifying metrics for NCS performance in the application environment, and mathematically evaluating the solution candidates with respect to these metrics. Experimental requires collecting and evaluating experimental data on particular aspects of network operation that are important to the application environment. Analytical involves a weighted cost analysis of the tradeoffs involved in network decision making and incorporates the results of theoretical and experimental analysis. In combining these three information perspectives, a methodology for making NCS design decisions is provided that allows the user to appropriately incorporate the application environment. An application of this methodology to a reconfigurable factory testbed demonstrates its use and effectiveness.
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Pimentel, J. R. "Integrated manufacturing network architectures." In IEEE INFOCOM '89, Proceedings of the Eighth Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, 1989. http://dx.doi.org/10.1109/infcom.1989.101569.
Kafil, Hajlaoui,. "Neural Network Based Text Mining to Discover Enterprise Networks." In Information Control Problems in Manufacturing, edited by Bakhtadze, Natalia, Chair Dolgui, Alexandre and Bakhtadze, Natalia. Elsevier, 2009. http://dx.doi.org/10.3182/20090603-3-ru-2001.00140.
Telagi, Mruthyunjaya S., and Athamaram H. Soni. "Control Systems in Manufacturing." In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0365.
Abstract This paper reviews different control methodologies applied in manufacturing environment. Since comparatively newer control methodologies like — Neural networks, Fuzzy logic, and Cerebellar model articulation controller have gained more research interests in recent years, they have been dealt in more detail. With this, we have presented application of neural network for endeffector positioning of three degree planar robot and results have been evaluated. Finally the future research trends in these areas have been discussed.
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Lessard, Roger A., and Guylain Lemelin. "Computer-generated hologram (CGH) in optical computing and neural network." In Microelectronic Manufacturing, edited by Jagdish P. Mathur, John K. Lowell, and Ray T. Chen. SPIE, 1994. http://dx.doi.org/10.1117/12.186648.
Behnke, Daniel, Marcel Muller, Patrick-Benjamin Bok, Stefan Schneider, Manuel Peuster, Holger Karl, Alberto Rocha, Miguel Mesquita, and Jose Bonnet. "NFV-driven intrusion detection for smart manufacturing." In 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2019. http://dx.doi.org/10.1109/nfv-sdn47374.2019.9039956.
Su, Wei, Shuangxi Huang, Rong Wang, and K. L. Mak. "Optimal Design of Manufacturing Services Network." In 2012 IEEE International Conference on Services Computing (SCC). IEEE, 2012. http://dx.doi.org/10.1109/scc.2012.67.
Rajagopalan, R., and P. Rajagopalan. "Applications of neural network in manufacturing." In Proceedings of HICSS-29: 29th Hawaii International Conference on System Sciences. IEEE, 1996. http://dx.doi.org/10.1109/hicss.1996.495430.
Molnar, Mike. Network Charter: Manufacturing USA Program. Gaithersburg, MD: National Institute of Standards and Technology, December 2016. http://dx.doi.org/10.6028/nist.ams.600-2.
Molnar, Michael F. Network charter: Manufacturing USA program (2018). Gaithersburg, MD: National Institute of Standards and Technology, September 2018. http://dx.doi.org/10.6028/nist.ams.600-4.
Atcitty, Stanley. Tribal Colleges and Universities/Adavanced Manufacturing Network Initiative FY20 Q3 Progress Report. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1643747.
Atcitty, Stanley. Tribal Colleges and Universities/Advanced Manufacturing Network Initiative FY20 Q4 Progress Report. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1718979.
Kelley, Maryellen, and Cynthia Cook. The Institutional Context and Manufacturing Performance: The Case of the U.S. Defense Industrial Network. Cambridge, MA: National Bureau of Economic Research, March 1998. http://dx.doi.org/10.3386/w6460.
Atcitty, Stanley. Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance FY21 Q1 Progress Report. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1764619.
Atcitty, Stanley. Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance FY21 Q2 Progress Report. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1785064.
Atcitty, Stanley. Tribal Colleges and Universities/Advanced Manufacturing Network Initiative Phase II Sandia Technical Assistance FY21 Q3 Progress Report. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1817971.
Balza, Lenin H., Camilo De Los Rios, Alfredo Guerra, Luis Herrera-Prada, and Osmel Manzano. Unraveling the Network of the Extractive Industries. Inter-American Development Bank, April 2021. http://dx.doi.org/10.18235/0003191.
This paper analyzes extractive industries in Colombia and their connections to other economic activities in the country. We use detailed social security data on all formal employees to create an industry-relatedness measure using labor flows between industries. Drawing on the vast network analysis literature, we exploit centrality measures to reveal the importance of the extractive sector among Colombian industries. Our results show that extractive industries are well connected within the Colombian industrial network, and that they are central overall and within their clusters. We also find that extractive industries have stronger linkages with manufacturing and agriculture than with other sectors. Finally, a higher relatedness to extractive activities is correlated with lower levels of employment, specially of female workers.
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Muto, Kazuo. Trend of 3D CAD/CAE/CAM/CAT/Network Systems and PLM System in Advance Technology for Manufacturing Engineering Development. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0137.
