Analytical Performance Models

Models of supply chains in a dynamic and stochastic environment consider the network as a discrete event dynamic system. Such systems can be studied as Markov chains, stochastic Petri nets and queueing network models [#!VISWA92!#,#!ragthesis!#]. Buzacott [#!BUZACOTT97!#] has used certain well-known queueing models and queueing theoretic formulae to assess the impact of the nine principles of re-engineering enunciated by Hammer and Champy [#!hammer93!#]. The main conclusion of his study is that the re-engineering principles do improve the performance in a marked way, especially under significant variability of activity times. Malone and Smith [#!malo88!#], in their study, have looked at organizational and coordination structures, which constitute a key element of any business process. Again using simple queueing models, they have compared the efficacy of various organizational structures under a variety of conditions. Raghavan and Viswanadham [#!rpifs!#] discuss performance modeling and dynamic scheduling of make-to-order supply chains using fork-join queueing networks. Viswanadham and Raghavan compare make-to-stock and assemble-to-order systems using generalized stochastic Petri net models [#!ragOMW!#]. They also use integrated queueing and Petri net models for solving the decoupling point location problem, i.e. the point (facility) in the supply chain from where all finished goods are assembled to confirmed customer orders [#!ragthesis!#]. Models discussed above are high abstraction models for business processes under simplifying assumptions such as Markovian dynamics. To obtain high-fidelity models, one has to represent many realistic details, which is possible in simulation models.