Optimization of Tin Granular Inventory Management Through Modelling Monte Carlo Simulation
DOI:
https://doi.org/10.30656/jika.v6i1.11893Keywords:
Forecasting, Inventory, Monte Carlo Simulation, Python, Tin GranularAbstract
Problems: In the manufacturing industry, uncertainty in raw material demand is a critical challenge that can lead to stockouts and overstocking. The tinplate production line faces an operational scheduling asymmetry: the internal production unit for granular tin raw materials operates only five days a week, while the main production process runs continuously seven days a week.
Purpose: This study aims to design an adaptive and accurate granular tin inventory planning system using a stochastic simulation approach.
Methodology: This study used a Monte Carlo simulation in Python to minimize the margin of error and account for the probabilistic nature of demand uncertainty.
Results/Findings: The Python-based Monte Carlo simulation results achieved an accuracy level of 93.34% with a Mean Absolute Percentage Error (MAPE) of only 7%. These results fundamentally outperform classical forecasting methods such as Moving Average (MAPE 57%), Linear Regression (MAPE 50%), and Holt's Exponential Smoothing (MAPE 53%). This approach has proven effective in minimizing the risk of raw material shortages and optimizing the company's inventory costs. The integration of this simulation enables a transition from static forecasting to scenario-based planning, significantly improving cost efficiency and supply chain resilience amid extreme operational fluctuations.
Paper Type: Quantitative analytical research
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