Monte Carlo Simulation for Assessing Operational Efficiency and Environmental Impact in the Green Manufacturing Industry - Aini, Nurul; Nugroho, Dwi (Universitas Sains dan Teknologi Komputer, 2026)
The shift to green manufacturing has become a strategic imperative due to increasing regulatory pressure and market demand for energy-efficient, low-emission products. Nevertheless, in Indonesia's manufacturing sector, data limitations and process uncertainties still prevent effective operational decision-making. The purpose of the present research is to develop a Monte Carlo Simulation (MCS) model that can evaluate both operational and environmental impacts simultaneously, thereby enabling the formulation of adaptive green production strategies. The research uses a quantitative approach based on stochastic simulation. It gathers data from a sample of 15 green manufacturing companies over 12 months, covering variables such as energy consumption, machine efficiency, production output, and CO₂ emissions. The results suggest that green automation application leads to a 14% increase in operational efficiency and a 28% decrease in carbon emissions compared with baseline conditions. Sensitivity analysis indicates that machine efficiency and energy consumption are the most significant factors affecting sustainable performance. The originality of this research lies in the unique combination of operational and ecological dimensions within a probabilistic framework that remains useful even with limited data. In practice, the model is a decision-making tool based on evidence for managers and policymakers in the industry, helping them implement low-carbon manufacturing strategies more quickly.