Newell Brands faces high working capital intensity due to a diverse consumer product portfolio and seasonal demand variability that creates high levels of on-hand inventory. To control costs, the company needs to optimize cycle stock parameters, specifically lot size and ordering frequency for raw materials, both of which are currently set by localized planner intuition. This project applies a simulation-based optimization framework to historical demand data for raw materials, using Monte Carlo methods to test various lot-sizing policies. The optimization algorithm identifies specific lot sizes that reduce total annualized inventory costs while maintaining required service levels, outperforming existing manual policies. We recommend that the supply planning organization adopt this as a decision support tool for regular inventory reviews to standardize raw material procurement decisions and leverage the identified cost trade-offs in future supplier negotiations.
