Gestão de Estoque de Produtos Químicos em uma Indústria de Proteína de soja

Abstract

This study presents the development and application of a Production Planning and Control (PPC) model aimed at managing the critical chemical inputs used in soy protein processing. The research was motivated by the recurring delays in truck unloading observed between 2023 and 2024, caused by the absence of defined parameters for safety stock and reorder point, which resulted in significant annual costs due to truck detention fees. Based on historical data of consumption, variability, lead time, and tank capacity, appropriate safety stock (SS) and reorder point (RP) levels were calculated for Hydrochloric Acid, Membrane-Grade Caustic Soda, and Ethanol. Simulations were then carried out considering scenarios of average consumption, maximum consumption, and delayed deliveries. The results showed that in all simulated scenarios, stock levels remained above critical limits, and the RP triggered replenishment at the appropriate time, ensuring operational autonomy and eliminating the risk of stockouts. The implementation of the model in 2025 resulted in the complete elimination of truck detention costs, generating an annual savings of approximately R$ 208,000. The study demonstrates that basic PPC tools, when supported by accurate data and disciplined operational application, can effectively solve critical industrial problems, reduce costs, and enhance process reliability. The proposed model is robust, sustainable, and replicable across other areas of the plant.