Safety Stock Determination of Uncertain Demand and Mutually Dependent Variables https://ideas.repec.org/a/lrc/larijb/v8y2018i3p1-11.html Sharfuddin Lisan contributed a model that considers both direct and indirect costs related to inventory to calculate safety stock. This model works with ordering cost, holding cost, product price, economic order quantity, regression, lead time, mean lead time, and forecasting error calculation to estimate reorder points and order quantities while reducing human judgment error in the calculation. Order-up-to-level Policy Update Procedure for a Supply Chain Subject to Market Demand Uncertainty https://www.sciencedirect.com/science/article/abs/pii/S0360835217304242 Eduardo et el. proposes a new reorder point update procedure for order-up-to-level (OUTL) policies in continuous review systems. The new procedure modifies the classical OUTL policy by introducing absorption inventory, a concept that changes reorder points and lot sizes according to demand variations. Results obtained through a discrete event simulation based on real-world data provided by a Brazilian company show that the proposed order policy provides better performance, particularly in terms of bullwhip effect reduction and improved service level. Simulation based comparison of safety-stock calculation methods https://www.sciencedirect.com/science/article/abs/pii/S000785061200056X Matthias et el. developed mathematical methods for calculating safety stock while ensuring a designated service level with low inventory. The performance of the methods is then mapped in regard to the resulting service and safety-stock level against the background of different articles with variant logistical behavior. Exploring the Bullwhip Effect and Inventory Stability in a Seasonal Supply Chain https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=237290 Francesco et el. described a simulation approach to study the effect of demand seasonality on bullwhip effect and inventory stability in a four‐echelon supply chain that adopts an order‐up‐to ordering policy. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio. This investigation also indicates that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level.
