The Analysis and Simulation of a supply chain with Arena

Today, companies around the globe are competing for a cutting edge advantage by streamlining their supply chain performance. Global supply-and-manufacturing networks need driving strategies to manage the product, information and financial flows on which these businesses run. This is a challenge for supply chain management.
Supply chain management is confronted with more than just the task of fulfilling demand; a supply chain has to be cost effective, since the costs are reflected in the price of the end product.
The Bullwhip Effect is an observed phenomenon in forecast-driven distribution channels, where changes in order amounts are amplified through the system. The consequence of the variation is spiraling stock levels and difficulty to restore stability. The effect is costly – to high stock piles or demand not met. Variation has behavioral and operational causes. Supply chains are inherent to the causes of variation - the underlying demand characteristics and replenishment lead times. Normal logical behavior, without thorough knowledge of a supply chain and its mechanisms, can cause The Bullwhip Effect. Knowing the causes helps specifying remedies.
Different remedies and strategies, together with a thorough knowledge of the supply chain mechanism aids in successful management. Such strategies are Information Transparency, Retailer Managed Inventory and Supplier Managed Availability.
The discovery through computational modeling and simulation has become the third pillar of science, alongside theory and experimentation. As computational power increases, simulation has gained in importance and has become a major research area, where highly parallel computation is utilized. Science turns to simulation, when the valid models become too complicated or exact mathematical solutions are not worked out.
Modeling and simulation has shown to have an own development process in which data is collected, a model is built and the process specified. The significance of a simulation depends on the validity of the data, the model and process. Consequently the development process emphasizes the process analysis, the modeling and the feedback of the result of the implementation, the simulation and optimization, back into process analysis and modeling.
A supply chain seems to be predestined to be simulated – the processes and models can be well defined – date and variability is given in specific ranges – demand needs to be met and cost minimized. This provides the opportunity to see which effects strategies and other choices could have on a system, as well as to test the effectiveness of a supply chain design or changes, before it has been implemented.
This thesis has aimed to provide a thorough understanding of the complexity of supply chains and to provide an introduction into the simulation development process. This knowledge is applied in the simulation development of a supply chain in ARENA, the model being The Beer Distribution Game. In the last steps of the simulation development process, OptQuest is used to optimize the model and enhance the performance of the simulated supply chain.