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| Introduction to tools Failure Modes, Effects and Criticality Analysis Reliability Centred Maintenance Life Cycle Cost analysis Discrete Event Simulation Simulation versus real life experimentation  Simulation versus other mathematical modelling techniques
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Simulation versus Other Mathematical Modelling TechniquesInteraction of Random EventsSome other mathematical tools can mange to effectively model a steady state scenario but only simulation lets you build in random occurrences like a machine breaking down and see the effects of this further down the line. The more complex the scenario is, the less alternative solutions are able to deal with them and simulation is the only answer.Non Standard DistributionsMany mathematical techniques force the model builder to describe a situation as an approximation, it takes and average of 5 minutes to serve each customer; in real life this isn't the case: it takes 3 minutes to serve the customer if they have 4 items, it takes 7 minutes if they have 20 items. Approximating means results such as resource utilization time, customer waiting time are all inaccurate. Only simulation gives you the flexibility to describe events and timings as they actually are in real life. In many cases using averaged values gives misleading results as the system or operation never sees the extremes and is therefore never challenged: many operations can cope well with the 'average activity' but fail completely when significant, but credible, deviations occur.Generating IdeasSimulation provides a vehicle for a discussion about all aspects of a process. The generation of rules and data collection processes force you to consider why elements work in a certain way and if they could work better. It also brings to the surface inconsistencies and inefficiencies, especially between different sections of a process that work independently.:: Simulation versus Real Life Experimentation |
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