Reducing the Bullwhip effect in a supply chain with fuzzy logic approach

Abstract

Demand variability increases when it moves downstream to upstream in a supply chain, this is called the 'Bullwhip effect'. This effect causes unnecessary inventory build up along the nodes of the supply chain and hence reductions of this play a vital role. In this paper an effort is made to measure the Bullwhip effect by applying conventional P-controller in the transfer function dynamics model. It is shown that the Bullwhip effect can be reduced by replacing the P-controller with a Fuzzy Logic Controller (FLC). This FLC controls the errors and change in errors associated with forecasted demand between the nodes of a supply chain and allows a smooth information flow by reducing the vagueness in the chain. Tuning of the FLC has been performed using Adaptive Neuro-Fuzzy Inference System (ANFIS) and this leads to a further reduction in its error among the nodes. The introduction of fuzzy set theory addresses the real situation of human judgment with fuzziness in application, which helps the managers to forecast the demand with less distortion and to improve the supply chain effectiveness.