A new approach to calculate safety stock in the semiconductor Solid-State Drive (SSD) supply-chain. First, I analyze errors that have historically been made in the demand forecasting process. Next, I proposes a new approach to optimize the inventory safety stock using these learnings.

Most conventional models for demand forecasting typically assume that market demand is normally distributed. This assumption is often baked into commercial software for supply planning and cannot be changed. But, since demand does not follow a normal distribution in Solidigm’s SSD supply chain, I propose an alternative algorithm that leverages non-parametric kernel density estimations and overlapping continuous time intervals.

I presented a research poster at the Institute for Operations Research and Management Science (INFORMS) Annual Meeting in 2022.