Work

Advanced Stochastic Modeling for Series Production Processes: A Markov Chains and Queuing Theory Approach to Optimizing Manufacturing Efficiency

Year: 2025

Type: article

Abstract: The study presents an integrated stochastic modeling framework that combines Markov chains and queuing theory to optimize production efficiency in a metallurgical machining process. The model captures the dynamic behavior of key manufacturing stages, milling, drilling, reaming, and packing, by representing rework, inspection, and waste as distinct probabilistic states. Effective arrival rates and service rates are computed to evaluate machine utilization, total processing time, and throughput. The proposed approach was applied to an industrial case study, where the results showed that reprocessing activities increased the total cost per conforming unit by approximately 0.3% and affected overall system performance. By adjusting machine allocation and minimizing rework probabilities, the model demonstrated measurable improvements in cycle time and cost efficiency. This integrated methodology provides a practical decision-support tool for optimizing production flow, balancing resource utilization, and reducing the financial impact of nonconformity in continuous manufacturing environments.

Source: Processes

DOI: 10.3390/pr13113468

Authors: Nestor E. Caicedo-Solano, Darwin Peña-González, Diego Vergara, Izabel F. Machado, Edwan Anderson Ariza-Echeverri

Institutions: University of Magdalena, Catholic University of Ávila, Universidade de São Paulo

Language: English

Topic: Advanced Queuing Theory Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896