Krunal Patel,
Nirmal Kumar Balaraman,
Priyanka Das,
- Technical Program Manager, Department of Mechanical Engineering, Semiconductor Products Group, California, USA
- PLC Applications Engineer, Department of Mechanical Engineering, Inframark LLC, Cumming, USA
- Manufacturing Controls Engineer, Department of Mechanical Engineering, Ohio, USA
Abstract
Manufacturing and industrial automation projects face high uncertainty and risk arising from factors such as complex supply chains, equipment variability, and fluctuating production demands. If not properly managed, these uncertainties can lead to costly delays, unplanned downtime, and budget overruns that jeopardize project success. Given the shortcomings of deterministic planning in such volatile environments. If not properly managed, it can lead to costly delays and failures if not properly managed. Monte Carlo Simulation (MCS) serves as a potent instrument for decision analytics in addressing uncertainty within industrial automation and manufacturing contexts. It offers a proactive approach to address this challenge by modeling uncertainties and generating a range of possible outcomes. Unlike traditional single-point estimations, MCS runs thousands of simulations to produce a probability distribution of project outcomes, enabling more robust risk assessment and forecasting.
This paper proposes decision analytics framework that integrates MCS into project management for manufacturing and industrial automation. MCS to enhance production planning, mitigate operational risks, and reduce costs, utilizing semiconductor manufacturing as a representative case study. The research illustrates a methodological application of MCS for robust decision-making by assessing multiple production scenarios and deriving probabilistic inferences. The proposed approach encompasses comprehensive data collection, simulation model development, sensitivity analysis, and customized risk mitigation strategies. The findings indicate that MCS enhances decision accuracy despite uncertainty-induced downtime and results in substantial cost reductions. These outcomes further highlight the utility of MCS in industrial automation, where its stochastic insights can facilitate adaptive control systems to promptly adjust operations in response to uncertainty.
Keywords: Monte Carlo Simulation, decision analytics, semiconductor industry, project management, risk management, uncertainty analysis, probabilistic forecasting, supply chain optimization, operational efficiency, sensitivity analysis
[This article belongs to Journal of Production Research & Management ]
Krunal Patel, Nirmal Kumar Balaraman, Priyanka Das. A Monte Carlo Simulation Approach to Decision Analytics in Manufacturing and Industrial Automation Project Management. Journal of Production Research & Management. 2025; 15(02):1-12.
Krunal Patel, Nirmal Kumar Balaraman, Priyanka Das. A Monte Carlo Simulation Approach to Decision Analytics in Manufacturing and Industrial Automation Project Management. Journal of Production Research & Management. 2025; 15(02):1-12. Available from: https://journals.stmjournals.com/joprm/article=2025/view=223898
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Journal of Production Research & Management
| Volume | 15 |
| Issue | 02 |
| Received | 29/05/2025 |
| Accepted | 31/07/2025 |
| Published | 07/08/2025 |
| Publication Time | 70 Days |
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