Supply chain risk management is drawing the attention of practitioners and academics. A source of risk is demand uncertainty. To deal with it demand forecasting and safety stocks are employed. Most of the work has focused on point demand forecasting, assuming that forecast errors follow the typical normal i.i.d. assumption. The variability of the forecast errors is used to compute the safety stock, in order to reduce the risk of stockouts with a reasonable inventory investment. Nevertheless, real products’ demand is very hard to forecast and that means that at minimum the normally i.i.d. assumption should be questioned. This work analyses the effects of possible deviations from these assumptions and it proposes empirical methods based on Kernel density estimators (non-parametric) and GARCH models (parametric) in order to compute the safety stock.
Currently, I am teaching a subject about operations management and I have to introduce to my students the importance of safety stocks and the different ways to determine it. At this point, I was analyzing how this issue is explained in operations management books, and I realized that some of them compute the safety stock on the basis of the lead time demand distribution (Heizer and Render, 2008), whereas books more specialized in inventory management (Silver et al, 1998) and (Nahmias, 2004), they suggest to use the lead time forecast demand distribution. To be more precise, if we compute the safety stock for a certain service level, the safety stock (SS=k*standard deviation of lead time demand), where k can be obtained given the service level. The problem relies on the standard deviation, shall I use the standard deviation of the lead time demand distribution or shall I use the standard deviation of the lead time forecast demand error?