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.

Robust Control of Bullwhip Effect for Supply Chain System With Time-Varying Delay on Basis of Discrete-Time Approach

“…Furthermore, Information sharing is an important issue of supply chain where members have different level of knowledge or information, which complicates interactions among them. To deal with the information asymmetry, scholars proposed several scenarios and models to ensure coordination among the supply chain actors [19]. The methodology is based on control systems engineering and allows to gain valuable insights into the dynamic behavior of supply chain replenishment rules [20].…”