SIMULATION BASED ANALYSIS OF BULLWHIP EFFECT IN REVERSE SUPPLY CHAIN

Abstract

In this thesis, at first, mathematical models are developed for the calculation of bullwhip ratio by using order up to policy with Moving average and EWMA forecasting methods. In these models three stage reverse supply chain of waste paper recycling has been taken, in which manufacturer(paper industry), supplier of segregated waste paper, godown owner and waste paper merchant are considered as 0th.,Ist,2nd td stages. Thcn though sc:.siti v ity analysis percentage change in the bullwhip ratio is calculated for the change in different factors. Least increment and highest decrement in bullwhip ratio is obtained with centralized information sharing as compared with decentralized information sharing. In the simulation based analysis by using C++ programming, the simulation work has been performed for the calculation of the bullwhip ratio under different inventory control policies i.e. min-max -inventory control policy and stock to demand inventory control policy with centralized and decentralized information sharing strategies and finally the best solution has been obtained by comparison. i of tine simulation work approximate data has been taken which is related to cost and demand and these data are used in the C++ programs to make the simulation run. In the simulation work moving average forecasting method is used, each time monthly demand is generated from the random number assuming that demand of waste paper comes from the manufacturer with normal distribution (mean =1000 ton and standard deviation= 300 ton). Each time last ten period demands have been taken for the calculation of next period forecasted demand. This forecasted demand passes to the next stage; similarly the order quantity of last stage passes to next stage and finally the standard deviation of demand has been calculated for each stage in each replication. Simulation is done for 10 replications and the length of each replication is taken as 1000 months. The results of this simulation study shows that min-max inventory control policy with centralized information sharing shows least standard deviation of demand for each stage. This is mainly due to frequent reordering and thus less batching of orders.