A dynamic manufacturing network (DMN) is a coalition, either permanent or temporal, comprising production systems of geographically dispersed
small and medium enterprises and/or
original equipment manufacturers that collaborate in a shared value-chain to conduct joint manufacturing.[1][2]
The dynamic manufacturing networks are an approach that helps to
manage risks and increase benefits in the
manufacturing sector. The DMNs are a proposed solution to increase the efficiency and reduce the time needed to design and operate a new manufacturing network, or to reconfigure an existing one.[3]
During the last decade,the effort is mainly focused on the dynamic management of the manufacturing networks, as proven by several studies published by Accenture,[6] MIT
[7] and the University of St. Gallen [8]
References
^Papakostas, N. et al. (2012). On the configuration and planning of dynamic manufacturing networks, Logistics Research Journal, ISSN 1865-035X, September 2012
[1]
^IMAGINE Project: Innovative End-to-end Management of Dynamic Manufacturing Networks. Description of Work
www.imagine-futurefactory.eu
^Nikolaos Papakostas, Konstantinos Georgoulias, Spyridon Koukas, George Chryssolouris. Organisation and operation of dynamic manufacturing networks, International Journal of Computer Integrated Manufacturing , Volume 28, 2015 - Issue 8 ,Received 29 Oct 2012, Accepted 31 May 2014, Published online: 26 Jun 2014,
[2]
^IndustryWeek (2009). Product development assistance from manufacturing networks
[3]
^Deflorin, P, Scherrer-Rathje, M, Dietl, H. (2009). The competitive advantage of the lead factory concept in geographically distributed R&D and production networks. European Operations Management Association (EUROMA). Göteborg, Sweden
^Accenture (2012). Developing Dynamic and Efficient Operations for Profitable Growth - Research Findings from North American Manufacturers
[4][permanent dead link]
^Williams, G.P. (2011). Dynamic order allocation for make-to-order manufacturing networks: an industrial case study of optimization under uncertainty, Massachusetts Institute of Technology, 2011
[5]