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Companies have considerable incentives to optimize their end-to-end demand supply chains. Firms
approach this problem in two fronts: optimization of manufacturing functions on one hand and the
demand supply chains on the other. As such, several methods for demand supply network analysis have
been introduced in the literature. Most solutions use operations research paradigm—mixed integer
programming—or discrete simulation to analyze demand supply networks (Simchi-Levi et al., 2003;
Bramel & Simchi-Levi, 1997).
Recently, the industry has seen several examples of disasters brought up by broken demand supply
networks (Norrmann & Jansson, 2004). A logistics manager must know all the demand supply network
options available to reduce possible risks. This enumeration requires reachability analysis where each
path (i.e. a possible demand supply network setup) is explored. Also, dynamic analysis of demand sup-
ply networks is required to explore whether a chosen network responds well to fluctuating customer
demand. Mathematical optimization gives the optimal setup quickly via analytic or heuristic methods
(Powers, 1989). However, optimization methods do not support the analysis of network dynamics. Dis-
crete simulation, on the other hand, is excellent in dynamic analysis of a single demand supply network
(Bowersox & Closs, 1989). Yet, it lacks the capability of choosing the best network structures, given
by optimization. Thus, interplay of both techniques is required for a logistics professional to choose
the best possible network (Riddalls, Bennett & Tipi, 2000). Simulation-optimization (Azadivar, 1999;
Truong & Azadivar, 2003) has been developed to combine the advantages of optimization and simula-
tion. However, the modelling languages used in optimization and simulation are very different from
one another, and this creates a challenge for the co-use of the methods (Azadivar, 1999).
Petri Nets have been used successfully in modeling various kinds of systems, including telecom-
munication protocols and workflow systems (Jensen, 1996; van der Aalst, 1998). The hypothesis of this
research was that reachability analysis is adaptable to solving small and medium size demand supply
network optimization problems. As there are Petri net tools capable of dynamic simulation (van der
Aalst, 1992; ExSpect, 1999), such addition would provide a single methodology amenable to both, static
and dynamic analysis. Therefore, my research question became:
“How to apply reachability analysis
in demand supply network analysis?”
The result was a generic Petri Net model for describing arbitrary demand supply network options,
and a reachability analysis algorithm that computes the network setups and costs from the Petri Net
model. A Web-based analysis tool based on the methodology was constructed during 2004 and has been
in production use since February 2005.
The rest of the chapter is organized as follows: the remainder of the introduction reviews the current
approaches to demand supply network analysis. Section 2 gives the generic Petri Net model for demand
supply networks through example and formal definitions. Section 3 presents the reachability analysis
algorithm for the model. Section 4 presents a concrete Nokia case for the tool use. Section 5 concludes
with discussion and future work.
Literature r eview
The literature contains multiple methods for analyzing demand supply networks. Next, I describe Opera-
tions Research methods (Zeng & Rossetti, 2003; Thomas & Griffin, 1996; Vidal & Goetschalckx, 2001;
Fandel & Stammen, 2004), Analytic Hierarchy Processes (Wang, Huang,& Dismukes, 2004; Dotoli et
al., 2005), control theoretical methods (Ortega & Lin, 2004), discrete simulation methods (Persson &
Olhager, 2002), simulation optimization (Azadivar, 1999; Truong & Azadivar, 2003), and Workflow
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