The Impact of Demand Information Sharing on the Supply Chain Stability

Supply chain management (SCM) is defined as a set of approaches utilized to efficiently integrate suppliers, manufacturers, distributors and retailers to make sure that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system-wide costs while satisfying customer requirements. As a long-term cooperation strategy among enterprises, supply chain requires stable operations as necessary conditions for optimization and long-term competitive advantage of the whole supply chain. Stability, therefore, becomes an important indicator in supply chain management. Research on supply chain stability began in early 1960s. Based on system dynamics method, Forresterう1961え proves the presence and the cause of the bullwhip effect. He indicates that bullwhip effect could be weakened by improving decision-making behavior. From then on, the bullwhip effect and supply chain stability have been studied by many researchers. Most of these studies employed an ordering policy known as the APIOBPCS model (John et al., 1994; Disney and Towill, 2003). APIOBPCS policy is optimal in that, the policy minimizes the variance of the inventory levels with a sequence of forecast errors of demand over the lead time given (Vassian, 1954). In discrete time domain, pure time delays are readily handled by the z-transform and many results are known about its stability (Disney and Towill, 2002), variance amplification properties (Disney and Towill, 2003; Disney et al., 2004), and dynamic performance (Dejonckheere et al., 2003). Disney et al. (2006) investigate the stability of a generalized OUT (Order Up To) policy for the step response in both discrete and continuous time. Disney et al. (2007) prove that discrete and continuous Bullwhip Effect expressions have similar structures and show that the two domains are managerially equivalent and each domain can be used to study a supply chain in practice. The pure time delay causes complications in the time domain differential/difference equations. In the frequency domain, such equations generate an infinite number of zeros in the transfer function. Certain progress has been made on the stability of such systems by recasting the policy as a Smith Predictor (Warburton et al., 2004; Riddalls and Bennett, 2002). However, little is known about variance amplification issues or other aspects of the model’s dynamic performance. The use of Lambert ω function to solve such problems has recently gained some popularity. Several authors have studied the production and inventory control problem using continuous mathematics; and in the solution process the Lambert ω function is used (Warburton, 2004a, b; Warburton et al., 2004).