Multistage safety stock planning with item demands correlated across products and through time

Determining safety stocks in multistage manufacturing systems with serial or divergent structures, where end-item demands are allowed to be correlated both between products as well as in time, is my focus. I show that these types of correlation have contrary effects on the distribution of safety stocks over the manufacturing stages and that neglecting the correlation of demand can lead to significant deviation from the optimal buffer policy. Using base-stock control and assuming total reliability for internal supplies, I present a procedure for integrated multilevel safety stock optimization that can be applied to arbitrary serial and divergent systems even when demand is jointly cross-product and cross-time correlated. As I demonstrate in an example for autocorrelated demands of a moving average type, there are specific solution properties that drastically reduce the computational effort for safety stock planning. Safety stocks determined in that way can be used as an appropriate protection against demand uncertainties in material requirements planning systems.