A new approach to calculate safety stock in the semiconductor Solid-State Drive (SSD) supply-chain. First, we analyze errors that have historically been made in the demand forecasting process, and second, we proposes a new approach to optimize the inventory safety stock using these learnings.
The existing 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, there is an opportunity to build models for inventory optimization that do not make any assumptions about the nature of demand. We propose an algorithm that leverages non-parametric kernel density estimations and overlapping continuous time intervals to solve this problem.
Selected to present a poster on my research at the INFORMS (Institute for Operations Research and Management Science) Annual Meeting in 2022 as one of the few industry presentations. It received positive feedback from several professors and practitioners.
Contact aparna.komarla@gmail.com with any questions