Supply Chain Simulation: A Survey

This paper provides a survey of simulation in supply chain management.It reviews four types of simulation, namely spreadsheet simulation, system dynamics, discreteevent simulation, and business games.Which simulation type should be applied, depends on the type of managerial question to be answered by the model.Moreover, this paper summarizes novel sensitivity and robustness analyses.This sensitivity analysis yields a shortlist of the truly important factors in large simulation models with (say) a hundred factors.The robustness analysis optimises the important factors controllable by management, while accounting for the noise created by the important non-controllable, environmental factors.Both analyses are illustrated by a case study involving the simulation of a supply chain in the mobile communications industry in Sweden.In general, simulation is important because it may support the quantification of the benefits resulting from supply chain management.simulation;logistics;performance measurement;risk analysis;uncertainty;bifurcation;supply chain management

PROCESS SIMULATION IN SUPPLY CHAIN USING LOGWARE SOFTWARE

The authors present basis of simulation usage in managerial decisionsupport focusing on the supply chain processes. In the beginning the need for simulationis presented, then advantages and disadvantages of simulation experiments and thesimulation tools juxtaposition. Finally the chances of supply chain process simulationusing Logware software are presented.simulation, supply chain

Modelling very large complex systems using distributed simulation: A pilot study in a healthcare setting

Modern manufacturing supply chains are hugely complex and like all stochastic systems, can benefit from simulation. Unfortunately supply chain systems often result in massively large and complicated models, which even today’s powerful computers cannot run efficiently. This paper presents one possible solution - distributed simulation. This pilot study is implemented in a healthcare setting, the supply chain of blood from donor to recipient

Distributed supply chain simulation in GRIDS

Amongst the majority of work done in supply chain simulation, papers have emerged that examine the area of model distribution. The executions of simulations on distributed hosts as a coupled model require both coordination and facilitating infrastructure. A distributed environment, the Generic Runtime Infrastructure for Distributed Simulation (GRIDS) is suggested to provide the bonding requirements for such a model. The advantages of transparently connecting the distributed components of a supply chain simulation allow the construction of a conceptual simulation while releasing the modeler from the complexities of the underlying network. The infrastructure presented demonstrates scalability without losing flexibility for future extensions based on open industry standard

Distributed simulation with COTS simulation packages: A case study in health care supply chain simulation

The UK National Blood Service (NBS) is a public funded body that is responsible for distributing blood and asso-ciated products. A discrete-event simulation of the NBS supply chain in the Southampton area has been built using the commercial off-the-shelf simulation package (CSP) Simul8. This models the relationship in the health care supply chain between the NBS Processing, Testing and Is-suing (PTI) facility and its associated hospitals. However, as the number of hospitals increase simulation run time be-comes inconveniently large. Using distributed simulation to try to solve this problem, researchers have used techniques informed by SISO’s CSPI PDG to create a version of Simul8 compatible with the High Level Architecture (HLA). The NBS supply chain model was subsequently divided into several sub-models, each running in its own copy of Simul8. Experimentation shows that this distri-buted version performs better than its standalone, conven-tional counterpart as the number of hospitals increases

System Dynamics Simulation to Test Operational Policies in the Milk-Cheese Supply Chain Case study: Piar Municipality, Bolivar State, Venezuela.

With the purpose of detecting the impact that variations of demand cause in the milk-cheese supply chain, and determining how the operational policies of capacity, inventories or labor force can mitigate this impact, a system dynamics simulation model has been designed based on a survey conducted on a sample of cheese manufacturers and their links with milk farms, transportation companies and cheese distributors. This supply chain will be consolidated when a milk center that will collect the raw milk is completed. From this center, and after adequate treatment, milk will be distributed to the different cheese manufacturers in the supply chain. Managing adequately the milk-cheese supply chain represents an important challenge due to the short life of these products. Although this study was done in a region in Latin America, its results can be applicable to food supply chains by introducing some modifications. The milk-cheese supply chain in this case study contemplates three milk producers, one milk center, five cheese producers and several distributing agents. These companies operate individually under normal conditions, but they have understood that their integration in a supply chain improves the competitiveness of all its members. That is to say, the sum is greater than the parts. For its initial design a simulation software model is used in which the resources of the supply chain are optimized. Later the product of this optimization facilitates some initial values to be used in the system dynamics model in which causeeffect or influence relationships have been previously established considering the most representative variables. Finally, changes in operational policies that can reduce the level of pending orders in the supply chain are tested using other simulation software. The main contribution of this research is that it can serve as support or contribute to reduce the uncertainty in the decision making process of the supply chain management due to the speed with which individual or combined policies can be analyzed. In response to a variation of demand the most adequate policy may be selected and that can be done before the policy is implemented

Comparing conventional and distributed approaches to simulation in complex supply-chain health systems

Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete-event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today's powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models. To investigate this claim, this paper presents experiences in implementing a simulation model with a 'conventional' approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period. However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations

Evolution of Supply Chain Collaboration: Implications for the Role of Knowledge

Increasingly, research across many disciplines has recognized the shortcomings of the traditional “integration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes