This study analyzes dynamic lot sizing models to optimize inventory management within a Mexican manufacturing company specializing in paper-based air fresheners. Building on established lot size techniques, the research integrates demand analysis with an extract, transform and load (ETL) process to derive accurate and actionable inventory insights. Two dynamic methods, the Silver-Meal heuristic and the Wagner-Whitin algorithm, were applied to historical demand data for various paper raw materials. The results reveal that the Wagner-Whitin method typically leads to fewer purchase orders and lower total inventory costs compared to the Silver-Meal method. These findings provide practical guidance for improving production planning and cost efficiency in environments with high demand variability.
Martinez-Luna et al. (Thu,) studied this question.