Simulation might be an effective decision support tool in supply chain management. The review of supply chain simulation modeling methodologies revealed some issues one of which is the practicability of simulation in the supply chain environment. The supply chain environment is dynamic, information intensive, geographically dispersed, and heterogeneous. In order to develop usable supply chain simulation models, the models should be feasibly applicable in the supply chain environment. Distributed simulation models have been used by several researchers, however, their complexity and usability hindered their continuation. In this paper, a new approach is proposed. The approach is based on Ontologies to integrate several supply chain views and models, which captures the required distributed knowledge to build simulation models. The Ontology core is based on the SCOR model as the widely shared supply chain concepts. The ontology can define any supply chain and help the user to build the required simulation models.
Manufacturing enterprise decisions can be classified into four groups: business decisions, design decisions, engineering decisions, and production decisions. Numerous physical and software simulation techniques have been used to evaluate specific decisions by predicting their impact on the system as measured by one or more performance measures. In tbis paper, we focus on production decisions, where discrete-event simulation models perform that evaluation. We argue that such an evaluation is limited in time and scope, and does not capture the potential impact of these decisions on the whole enterprise. We propose integrating these discrete-event models with system dynamic models and we show the potential benefits of such an integration using an example of semiconductor enterprise.
Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology.
Traditionally decisions made based on simulation models have been the outcomes of complicated statistical analyses and having confidence in them is a subjective matter. Hybrid simulation offers an improved approach to better model real life systems and increase confidence in their outcomes. In particular hybrid discrete-continuous simulation has the potentials to reduce the impact of statistics in building models in addition to other significant benefits. In this paper we use hybrid models of discrete-event simulation and system dynamics to analyze global supply chain decisions. And to increase the decision makers' confidence as well as to make use of their experiences we apply the Analytic Hierarchical Process (AHP) analysis to the simulation results in order to reach better decisions. We describe the benefits of the use of the hybrid simulation and the added advantages of using AHP in order to maximize shareholder value.
Certain business objectives cannot be met without the interaction and communication between different systems. An interesting concept called system of systems (SoS), which aims to describe this interaction between systems has been gaining attention in the last few years. In this paper an extensive review of the literature is performed to capture the main characteristics associated to this concept in order to propose a new, more complete definition. This paper also proposes the use of distributed simulation through the High Level Architecture (HLA) rules to model and simulate systems of systems. We illustrate our idea with two different examples; a simplified supply chain network of a computer assembly and an aircraft initial sizing scenarios. The paper concludes with a discussion of some of the significant advantages distributed simulation could offer over traditional simulation for the analysis of such complex systems.
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