DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

Considering the broad spectrum of a supply chain, no model can capture all the aspects of
supply chain processes. The modeling proceeds at three levels:
• Competitive Strategic analysis, which includes location-allocation decision, demand
planning, distribution channel planning, strategic alliances, new product development,
outsourcing, IT selection, pricing, and network structuring.
• Tactical problems like inventory control, production/distribution coordination, material
handling, layout design.
• Operational level problems, which includes routing/scheduling, workforce scheduling
and packaging.
The models in supply chains can be categorized into four classes (Min and Zhou 2002):
• Deterministic: single objective and multiple objective models.
• Stochastic: optimal control theoretic and dynamic programming models.
• Hybrid: with elements of both deterministic and stochastic models and includes inventory
theoretic and simulations models.
• IT driven: models that aim to integrate and coordinate various phases of supply chain
planning on a real-time bases using application software, like ERP.
Mathematical programming techniques and simulation have been primarily two approaches for
the analysis and study of the supply chains models. The mathematical programming mainly takes
into consideration static aspects of supply chain. The simulation on the other hand studies
dynamics in supply chains and generally proceeds based on “system dynamics” and “agent
based” methodologies. System dynamics is a continuous simulation methodology that uses
concepts from engineering feedback control to model and analyze dynamic socioeconomic
systems (Forrester, 1961). The mathematical description is realized with the help of ordinary
differential equation. An important advantage of system dynamics is the possibility to deduce the
occurrence of a specific behavior mode because the structure that leads to the system dynamics is
made transparent. We present some nonlinear models in Section 5 which are useful for
understanding the complex interdependencies, effects of priority, nonlinearities, delays,
uncertainties and competition/cooperation for resource sharing in supply chains. The drawback of
system dynamics model is that the structure has to be determined before starting the simulation.
Agent-based modeling (a technique from complexity theory) on the other hand is a “bottom up
approach” which simulates the underlying processes believed responsible for the global pattern,
and allows us to evaluate what mechanisms are most influential in producing that emergent
pattern. In (Schieritz and Grobler, 2003) a hybrid modeling approach has been presented that
intends to make the system dynamics approach more flexible by combining it with the discrete
agent-based modeling approach. Such large-scale simulations with their many degrees of freedom
raise serious technical problems about the design of experiments and the sequence in which they
should be carried out in order to obtain the maximum relevant information. Furthermore, in order
to analyze data from such large-scale simulations we require systematic analytical and statistical
methods. In Section 8, we describe two such techniques: Nonlinear Time Series Analyses and
Computational Mechanics.
A useful paradigm for modeling a supply chain, taking into consideration the detailed pattern of
interaction is to view it as a network. A network is essentially anything that can be represented by
a graph: a set of points (also generically called nodes or vertices), connected by links (edges, ties)
representing some relationship. Networks are inherently difficult to understand due to their
structural complexity, evolving structure, connection diversity, dynamical complexity of nodes,
node diversity and meta–complication where all these factors influence each other. Queuing
theory has primarily been used to address the steady-state operation of a typical network. On the
other hand techniques from mathematical programming have been used to solve the problem of
resource allocation in networks. This is meaningful when dynamic transients can be disregarded.
However, present day supply chain networks are highly dynamic, reconfigurable, intrinsically