If you only have average and maximum demand and lead time figures, the basic max-avg formula is usually the best place to start. It's practical, easy to explain, and common in smaller operations. If you track demand volatility, the demand variability method gives a more statistically grounded buffer. If both demand and lead time fluctuate and you have standard deviations for each, the combined variability method is the strongest choice because it captures both sources of uncertainty.
" }, "name" : "Which safety stock method should I use?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "The service level sets the z-score used in the variability-based formulas. A higher service level means you want more protection against stockouts, so the tool recommends more safety stock. For example, 95% service generally produces less buffer than 99%. It's a simple way to connect your inventory policy to your tolerance for running out during replenishment.
" }, "name" : "What does the service level selector actually do?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "That can happen when your entered maximum values don't exceed your average demand over lead time enough to justify extra buffer, especially with the basic formula. In that case, the tool returns zero instead of a negative number because negative safety stock doesn't make practical sense. It simply means your current assumptions do not indicate a need for additional buffer inventory beyond expected demand during lead time.
" }, "name" : "Why would safety stock ever come out as zero?" } ] } ```