Eliminating bullwhip effect in supply chain stock systems using smart controllers

Abstract

Several alternative approaches have been proposed for supply chain modeling majority of which steady-state models. These models cannot adequately deal with dynamic characteristics of supply chain system affected by lead time, demand fluctuation, sale prediction and so forth. Static models in particular cannot describe, analyze and provide solutions for a key issue in supply chains called bullwhip effect. The bullwhip effect is information deviation from one end of the supply chain to the other which intensifies fluctuation and change in demand from downstream to upstream. This issue leads to major deficiencies. One of the approaches used to cope with dynamic issues is control systems approach. In present study, a predicting model controller was developed to minimize the bullwhip effect in supply chain. In addition, a prediction methodology is integrated into predicting model control framework to predict uncertainty in distorting demand behavior. Integration of a prediction methodology in predicting model control framework improved the controlling system's performance. The main feature of demand signal used in model design is its fluctuation and distortion. One of the main factors behind bullwhip effect is demand signals processing and in fact, the predicting model used.