SIMULATION

The supply chain management (SCM) field of research has developed a valuable set of concepts and strategies to incorporate the core competencies of suppliers into industrial systems, a trend followed by the construction industry. Research into quantifying the benefits of implementing SCM in construction has been very limited; the literature for the construction industry generally discusses how SCM concepts can be adopted, or what problems and challenges inhibit such adoption, without analyzing and quantifying the effects of these techniques on an actual construction project. No analytical approach or special-purpose simulation tool has yet been developed to facilitate such quantitative analysis for construction projects. This paper presents a construction supply chain simulation toolkit that is capable of modeling different supply chain problems and is compatible with other construction simulation tools. A detailed simulation model of the effects of supply chain issues on the productivity of a real-life construction project, constructed using this toolkit, is also presented.

Decision making inside a company is usually performed by means of decision support tools at three different levels (strategic, tactical and operational) according to the working horizon. Unfortunately, the consequences of any decision at the strategic level are propagated to the tactical and even the operational levels. In order to support highly efficient flexibility while avoiding infrastructure over-sizing, resource saturation and poor coordination of activities and resources, it is important to consider the cause—effect interaction of strategic decisions with operational ones. In this paper, a discrete-event system simulation approach to evaluate the influence of the internal interactions among material, people, information and financial resources on the success of a Spanish optician supply chain is introduced. The emergent dynamics are analyzed and a redesign of the business and distribution network is proposed to foster synergy among supply chain actors.

In this paper we present a methodology and simulation environment for solving multi-echelon supply chain planning and optimization problems for industries with batch and semi-batch processes. The introduced methodology is aimed to analyze efficiency of a specific planning policy over the product life cycle within the entire supply chain for automated switching from a non-cyclic to cyclic and to optimize the cyclic planning policy for products at the maturity phase. For optimization of a multi-echelon cyclic schedule, the simulation optimization algorithm developed is based on integration of the multi-objective genetic algorithm (GA) and response surface-based local search to improve GA solutions. The comparative analysis of planning policies is based on estimation of the difference between mean values of their total costs by using the Paired-t confidence interval method and evaluation of an additional cost of the cyclic schedule. The simulation environment allows one to describe input data to build the supply chain network and store it in an external file, computing effective planning policies, automatically generating and running a network simulation model, generating production rules for switching from one planning policy to another and optimizing parameters of a multi-echelon cyclic schedule. Finally, a business case is described that illustrates the practical application of the presented methodology.

While significant effort has been expended on the analysis of new security technologies and policies at ports of entry, the effort has focused on the effectiveness of the security, not on the impact of security on commercial operations. This paper addresses the requirements to model the impact of security measures on port operations, using an existing port simulation. The goal of the simulation is to assess different strategies from an economic viewpoint. To contrast different strategies, we need to be able to rapidly prototype the strategies in an existing operational model. Both proactive and reactive security measures are addressed, as well as responses to natural disasters. The modeling techniques required to rapidly incorporate new security strategies into the simulation are identified. Then these capabilities are demonstrated using the Port Simulation (PORTSIM) tool.

The impact of the global economic crisis on the Mexican automotive suppliers and its effects on the labor capital are analyzed. Owing to the complexity of the subsystems involved in the automotive industry, a system dynamics approach was selected to develop the simulation model based on a case study. Nevertheless, because of the standard structure of the proposed model, it can be generalized to other automotive companies. The model provides a detailed causal analysis of how supply networks and local conditions interact. The results show the strong relationship existing between the local suppliers and the OEM at the core of the cluster, which can generate competitive advantages in the region. However, it can also create a gap in the local supply chains and in the labor capital when the demand of the OEM decreases due to factors such as the global economic crisis. The results reveal how clusters with a strong centralized structure in one industry make them highly specialized. However, the lack of effective public support policies makes them weaker in front of the variability imposed by globalization.

Large capital intensive projects, such as those in the mineral resource industry, are often associated with diverse sources of both endogenous and exogenous risks and uncertainties. These risks can greatly influence the project profitability. Having the ability to plan for these uncertainties is increasingly recognized as critical to long-term mining project success. In the mining industry in particular, the relationships between input variables that are controllable, and those that are not, and the physical and economic outcomes are complex and often nonlinear. The value of managerial flexibility is assessed using data on prices, costs, discount rates, grades, ore extraction, and metal output. Monte Carlo simulation of the mean reversion process is used to forecast revenue data based on an initial metal price, by using annualized volatility. Monte Carlo simulation of the Geometric Brownian Motion is used to forecast operating costs. To quantify the uncertainty in the parameters within a project such as capital investment, ore grade, and mill recovery, we used triangular, uniform, and normal statistical distribution, respectively. To decrease uncertainty related to selection of the appropriate discount rate, we have applied the concept of fuzzy sets theory. The result is a Net Present Value (NPV) based on the cash flows generated by the simulation over the timeframe of the project. When using fuzzy numbers, the fuzzy NPV itself is the payoff distribution from the project. The model explains investment behavior satisfactorily, both from a statistical and from an economic point of view.