Interaction of optimization models and information sharing in a two echelon supply chain
Uncertainty in the manufacturing industry has been a research interest for many years. Deterministic and stochastic optimization methods have been proposed in the past. The objective of this thesis is to study the interaction of these models in a supply chain with a varying error in demand forecast. All the possible combinations of the optimization strategies in a two-echelon supply chain have been considered. Results indicate that the performance of the supply chain is driven by the choice of strategy of the supplier. Stochastic optimization is very efficient in lowering the operational costs and bull-whip effect in most cases. However, in cases where the trend in demand variation is smooth, use of deterministic strategy by both stakeholders is beneficial and it helps in lowering operational cost. Information sharing results in cost saving in most of the cases. It increases with increase in root mean squared error in demand forecast when the supplier uses deterministic strategy.