APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments
This study presents a methodology for integrating Asset Performance Management (APM) and Asset Investment Planning (AIP) platforms for joint optimization of productivity and reliability using simulation experiments. This research combines data from an APM, which provides information on equipment rel...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/6/476 |
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| author | Jorge Pinilla Orlando Durán Christian Salas |
| author_facet | Jorge Pinilla Orlando Durán Christian Salas |
| author_sort | Jorge Pinilla |
| collection | DOAJ |
| description | This study presents a methodology for integrating Asset Performance Management (APM) and Asset Investment Planning (AIP) platforms for joint optimization of productivity and reliability using simulation experiments. This research combines data from an APM, which provides information on equipment reliability, and a simulation module of AIP software that offers detailed technical data of a set of alternative equipment in a sort of catalog. Criteria such as availability, reliability, criticality, and utilization levels, based on historic stored data, are used to evaluate different equipment configurations. Such data are provided by the APM platform, while the productivity and efficiency of existing and candidate equipment are captured from the configuration module on the AIP platform. Key aspects of this work point to the possibility of applying it in two main stages of a system’s life cycle: the design stage, where the project is in its conceptual design phase, and the operation or exploitation stage, where newer configurations are considered to prioritize operational adjustments and optimizations due to the inherent constraints of reliability and maintainability aspects. Through the development of a specifically developed model, which is applied to ensure optimal selection of equipment and configurations, it is possible to obtain new equipment configurations that ensure operational continuity and efficient production performance without exceeding budgetary restrictions and energy consumption limits or compromising productivity. |
| format | Article |
| id | doaj-art-baf3dc6fb2434a8d9894b2dfbaa657db |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-baf3dc6fb2434a8d9894b2dfbaa657db2025-08-20T03:27:36ZengMDPI AGSystems2079-89542025-06-0113647610.3390/systems13060476APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation ExperimentsJorge Pinilla0Orlando Durán1Christian Salas2Mechanical Engineering School, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileMechanical Engineering School, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileMechanical Engineering School, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileThis study presents a methodology for integrating Asset Performance Management (APM) and Asset Investment Planning (AIP) platforms for joint optimization of productivity and reliability using simulation experiments. This research combines data from an APM, which provides information on equipment reliability, and a simulation module of AIP software that offers detailed technical data of a set of alternative equipment in a sort of catalog. Criteria such as availability, reliability, criticality, and utilization levels, based on historic stored data, are used to evaluate different equipment configurations. Such data are provided by the APM platform, while the productivity and efficiency of existing and candidate equipment are captured from the configuration module on the AIP platform. Key aspects of this work point to the possibility of applying it in two main stages of a system’s life cycle: the design stage, where the project is in its conceptual design phase, and the operation or exploitation stage, where newer configurations are considered to prioritize operational adjustments and optimizations due to the inherent constraints of reliability and maintainability aspects. Through the development of a specifically developed model, which is applied to ensure optimal selection of equipment and configurations, it is possible to obtain new equipment configurations that ensure operational continuity and efficient production performance without exceeding budgetary restrictions and energy consumption limits or compromising productivity.https://www.mdpi.com/2079-8954/13/6/476asset performance managementasset investment planningequipment selectionproductivityenergy consumptionprocess simulation software |
| spellingShingle | Jorge Pinilla Orlando Durán Christian Salas APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments Systems asset performance management asset investment planning equipment selection productivity energy consumption process simulation software |
| title | APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments |
| title_full | APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments |
| title_fullStr | APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments |
| title_full_unstemmed | APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments |
| title_short | APM and AIP Integration for Joint Optimization of Productivity and Reliability Using Simulation Experiments |
| title_sort | apm and aip integration for joint optimization of productivity and reliability using simulation experiments |
| topic | asset performance management asset investment planning equipment selection productivity energy consumption process simulation software |
| url | https://www.mdpi.com/2079-8954/13/6/476 |
| work_keys_str_mv | AT jorgepinilla apmandaipintegrationforjointoptimizationofproductivityandreliabilityusingsimulationexperiments AT orlandoduran apmandaipintegrationforjointoptimizationofproductivityandreliabilityusingsimulationexperiments AT christiansalas apmandaipintegrationforjointoptimizationofproductivityandreliabilityusingsimulationexperiments |