DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

ealizable. But the inherent complexity of supply chains makes the efficient utilization of
information technology an elusive endeavor. Tackling this complexity has been beyond the
existing tools and techniques and requires revival and extensions.
As a result we emphasized in this paper, that in order to effectively understand a supply chain
network, it should be treated as a CAS. We laid down some initial ideas for the extension of
modeling and analysis of supply chains using the concepts, tools and techniques arising in the
study of CAS. As a future work we need to verify the feasibility and usefulness of the proposed
techniques in the context of large scale supply chains.
Acknowledgements
The authors wish to acknowledge DARPA (Grant#: MDA972-1-1-0038 under UltraLog Program)
for their generous support for this research. In additions the partial support provided by NSF
(Grant#:DMII-0075584) for Professor Kumara is greatly appreciated.
References
Abarbanel, H.D.I, 1996, The Analysis of Observed Chaotic Data, Springer-Verlag, New York.
Abarbanel, H. D. I. and Kennel, M. B., 1993, Local False Nearest Neighbors and Dynamical
Dimensions from Observed Chaotic Data, Phys. Rev. E, 47, 3057-3068.
Adami, C., 1998, Introduction to Artificial Life, Springer-Verlag.
Albert, R. and Barabasi, A. L., 2002, Statistical Mechanics of Complex Networks, Reviews of
Modern Physics, 74, 47.
Albert, R., Barabási, A. L., Jeong, H. and Bianconi, G., 2000, Power-law distribution of the
World Wide Web, Science, 287, 2115.
Albert R., Jeong, H., Barabasi, A. L.,2000, Error and attack tolerance of complex networks,
Nature, 406, 378-382.
Balakrishnan, A., Kumara, S. and Sundaresan, S., 1999, Exploiting Information Technologies for
Product Realization, Information Systems Frontiers, A Journal of Research and Innovation,
1(1), 25-50.
Barabasi, A.L., July 2000, The Physics of Web, Physics Web.
Barabasi, A. L., Albert, R., and Jeong, H., 2000, Scale-free characteristics of random networks:
The topology of the World Wide Web, Physica A, 281, 69-77.
Baranger, M., Chaos, Complexity, and Entropy: A physics talk for non-physicists,
http://necsi.org/projects/baranger/cce.pdf.
Bar-Yam, Y., 1997, Dynamics of complex systems, Reading, Mass, Addison-Wesley.
Bollobas, B., 1985, Random Graphs, Academic Press, London.
Callaway, D. S., Newman, M. E. J., Strogatz, S. H. and Watts, D. J., 2000, Network robustness
and fragility: Percolation on random graphs, Phys. Rev. Lett. 85, 5468-5471.
Carlson, J. M., Doyle, J., 1999, Highly optimized tolerance: a mechanism for power laws in
designed systems, Physics Review E, 60(2), 1412-1427.
Casdalgi, M., 1989, Nonlinear prediction of chaotic time series, Physica D, 35, 335-356.
Choi, T. Y., Dooley, K. J., Ruangtusanathan, M., 2001, Supply networks and complex adaptive
systems: control versus emergence, Journal of Operations Management 19(3), 351-366.
Cooper, M. C., Lambert, D. M., and Pagh, J. D., 1997, Supply chain management: More than a
new name for logistics, The International Journal of Logistics Management, 8(1), 1-13.
Crutchfield, J. P., 1992, Knowledge and Meaning … Chaos and Complexity, in Modeling
Complex Systems, L. Lam and H. C. Morris, editors, Springer-Verlag, Berlin, 66 -101.
Crutchfield, J. P., 1994, The Calculi of Emergence: Computation, Dynamics and Induction,
Physica D, 75, 11-54.
Crutchfield, J. P. and Young, K., 1989, Inferring Statistical Complexity, Physical Review Letters,
63, 105-108.