Reducing the bullwhip effect by means of intelligent, soft computing methods

Considers a series of companies in a supply chain, each of which orders from its immediate upstream collaborators. Usually, the retailer´s orders do not coincide with the actual retail sales. The bullwhip effect refers to the phenomenon where orders to the supplier tend to have larger variance than sales to the buyer (i.e. demand distortion), and the distortion propagates upstream in an amplified form (i.e. variance amplification). We show that, if the members of the supply chain share information with intelligent support technology and agree on better and better fuzzy estimates (as time advances) on future sales for the upcoming period, then the bullwhip effect can be significantly reduced.