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randomness. In this context, the application of
multiagent model, with other forecasting methods,
markedly reduces the Bullwhip Effect generated.
To develop the tool, we have considered only
simple forecasting methods, such as moving
averages and exponential smoothing, so that each
level of the chain uses the best one that suits the
demand it should deal with. With them, it is possible
to achieve great results in reducing Bullwhip Effect.
Even so, we have also shown that the inclusion of
more advanced forecasting methods (ARIMA
models) allows an even better system performance.
Lastly, we have analyzed the effect of
negotiation and collaboration among different levels
of the supply chain, verifying that it is an adequate
solution in reducing the Bullwhip Effect.
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