On the Test Particle Monte-Carlo method to solve the steady state Boltzmann equation, the congruity of its results with experiments and its potential for shared memory parallelism
“…The Monte Carlo simulation, as an approach for managing uncertainty and quantifying risk, has been used in several fields of knowledge, including risk quantification in costing systems [48]. Among the advantages of the Monte Carlo simulation are the ability to include probability distributions of variables, the ability to include correlations of variables, and the ability to obtain solutions in reasonable computational times.…”
In capital-intensive organizations, decisions regarding capital costs play an important role due to the significant amount of investment required and the expected return on investment. Spare parts management is crucial to those ends, as spare parts management can constitute a significant portion of OPEX. Companies must implement a trade-off analysis between stock levels and assets’ availability. Decision-making supports mechanisms such as the Level of Repair Analysis (LORA), Integrated Logistics Systems (ILS), and life-cycle costing (LCC) models have been developed to aid in equipment selection, implementation, and decommissioning. Nowadays, these mechanisms appear to be integrated with risk-management models and standards. This paper proposes a long-term costing model that integrates a capacity analysis, reliability functions, and risk considerations for the cost management of logistics activities, particularly in MRO structures. The model is built upon Time-Driven Activity-Based Costing (TD-ABC) and incorporates the volume of activities generated by MRO needs. It also addresses uncertainty through the integration of a cost-at-risk model. By integrating spare parts, activity-based cost models, and risk measurement through Monte Carlo simulation, this study offers powerful insights into optimizing spare parts logistics activities. The proposed model is a novel approach to include the risk of cost in spare parts management, and its matrix-activity-based structure makes possible the development of sophisticated mathematical models for costing and optimization purposes in different domains.
“…The Monte Carlo simulation, as an approach for managing uncertainty and quantifying risk, has been used in several fields of knowledge, including risk quantification in costing systems [48]. Among the advantages of the Monte Carlo simulation are the ability to include probability distributions of variables, the ability to include correlations of variables, and the ability to obtain solutions in reasonable computational times.…”
In capital-intensive organizations, decisions regarding capital costs play an important role due to the significant amount of investment required and the expected return on investment. Spare parts management is crucial to those ends, as spare parts management can constitute a significant portion of OPEX. Companies must implement a trade-off analysis between stock levels and assets’ availability. Decision-making supports mechanisms such as the Level of Repair Analysis (LORA), Integrated Logistics Systems (ILS), and life-cycle costing (LCC) models have been developed to aid in equipment selection, implementation, and decommissioning. Nowadays, these mechanisms appear to be integrated with risk-management models and standards. This paper proposes a long-term costing model that integrates a capacity analysis, reliability functions, and risk considerations for the cost management of logistics activities, particularly in MRO structures. The model is built upon Time-Driven Activity-Based Costing (TD-ABC) and incorporates the volume of activities generated by MRO needs. It also addresses uncertainty through the integration of a cost-at-risk model. By integrating spare parts, activity-based cost models, and risk measurement through Monte Carlo simulation, this study offers powerful insights into optimizing spare parts logistics activities. The proposed model is a novel approach to include the risk of cost in spare parts management, and its matrix-activity-based structure makes possible the development of sophisticated mathematical models for costing and optimization purposes in different domains.
“…Estos métodos han sido utilizados en el análisis de la incertidumbre en sistemas de costeo (11-13). Un enfoque bien conocido en este contexto es el método de Monte Carlo el cual utiliza la lógica de la probabilidad y el volumen, este método puede incluirse en la rama experimental de las matemáticas que basada en probabilidades estadísticas apoya experimentos con números aleatorios (14) En los procesos de producción, el riesgo se relaciona con la variación en los factores necesarios para la elaboración de un bien o servicio, dicha variación puede generar efectos adversos para las finanzas de una organización, pues los costos pueden ser superiores a los ingresos lo que afectaría la rentabilidad de una organización. Por lo anterior, el riesgo debe ser identificado y medido para generar medidas de mitigación que permitan su eliminación, reducción o control (15).…”
Aunque las compañías estandarizan sus procesos para obtener productos estandarizados con costos estandarizados, es necesario establecer en qué momento una variación en el costo con respecto al estándar se considera tolerable y cuando deben ser emprendidas acciones correctivas, preventivas o de mejora. El considerar valores tolerables con respecto a un estándar genera la idea que, en vez de ser un valor definido, un estándar puede ser considerado como un intervalo del valor esperado del costo, por lo que valores de costo que pertenecen al intervalo se consideran toreables mientras que valores fuera de dicho intervalo deben generar medidas de intervención por parte de los tomadores de decisiones. Este artículo presenta una metodología que permite establecer el intervalo de tolerancia del costo para un producto. La metodología utiliza un modelo matricial basado en la lógica del costeo basado en actividades que incorpora la simulación Monte Carlo para representar la variabilidad inherente en cada uno de los procesos. La metodología fue aplicada en un caso de estudio en el sector transporte obteniendo como resultado el rango de valores estándar el cual sirve como base para la toma de decisiones tácticas y operativas dentro de la organización.
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