In our on-demand webinar, the third installment in our safety stock series, we delved into a key concern often raised within the realm of safety stocks: the reliance on the normal distribution assumption. While the prevailing formulae and their iterations commonly lean on this assumption, it’s crucial to explore alternative perspectives.

During this session, Sujit Singh talked about it from two distinct points of view. One is where the product volumes are small and sporadic. When this is the case, the assumption of normality is particularly problematic. The calculation of safety stocks in these cases is based on a Poisson distribution. He also talked about the bootstrapping method which does not assume any distribution and can be applied to any type of data.

By sidestepping the need for distribution assumptions entirely, practitioners gain a versatile tool for calculating safety stocks. Attendees will depart equipped with a comprehensive understanding of how to navigate safety stock calculations without tethering themselves to the constraints of a normal distribution assumption.


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About the presenter: Sujit Singh


As COO of Arkieva, Sujit manages the day-to-day operations at Arkieva such as software implementations and customer relationships. He is a recognized subject matter expert in forecasting, S&OP, and inventory optimization. Sujit received a Bachelor of Technology degree in Civil Engineering from the Indian Institute of Technology, Kanpur, and an M.S. in Transportation Engineering from the University of Massachusetts. Throughout the day don’t be surprised if you find him practicing his cricket technique before a meeting.