Qualitative Risk Criticality Matrix (QRCM) applied in a biodegradable packaging production line
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Abstract
The criticality analysis techniques that consider the Risk factor are tools that, first, allow, to identify the importance of assets in an industrial facility, and second, enable the efficient allocation of resources: human, financial, and technological. In the following technical article, are explained, the basic theoretical aspects of a Qualitative Risk Analysis model used to determine the criticality of equipment. This process involves the evaluation of two key factors: reliability (failure frequency) and consequences (impact that failures have on safety, the environment, and production). Additionally, a case study is presented, which includes the development of a model based on a Qualitative Risk Criticality Matrix (QRCM), applied in a biodegradable packaging manufacturing plant in the agro-industrial sector of Panamá.
The following describes some of the most important keys to be developed in the article:
- The correct application of Qualitative Criticality Analysis methods will help management and technical levels make decisions with a lower level of uncertainty in processes related to the operation and maintenance of industrial assets. Specifically, regarding maintenance management, the results of a Risk-based criticality analysis process will allow the development of maintenance strategies with a Cost-Risk-Benefit optimization approach.
- It is important that the maintenance management understands that the criticality models to be designed must align with the business objectives and not make the mistake of developing criticality tools where only specific factors of the maintenance process are included. In this regard, using risk-based criticality models creates a favorable scenario for evaluating the impact of factors inherent to the maintenance process and also facilitates the process of adding factors such as production, quality, production loss costs, safety, environment, etc., which are related to a comprehensive asset management process.
Finally, the correct application and implementation of recommendations obtained from the analysis of the results of the QRCM (Qualitative Risk Criticality Matrix), will optimize the technical processes of maintenance management and improve the profitability of manufactured products at the Biodegradable Packaging Plant MOLPASA throughout its entire industrial life cycle.
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References
Parra, C., Tino, G., Parra, J. A., Viveros, P., & Kristjanpoller, F. A. (2022). Criticality analysis techniques applied to optimize maintenance management processes: Tools based on the qualitative and quantitative risk model. In V. González-Prida, C. Márquez, & A. Márquez (Eds.), Cases on optimizing the asset management process (pp. 180–207). IGI Global. https://doi.org/10.4018/978-1-7998-7943-5.ch008
Parra, C., & Crespo, A. (2015). Ingeniería de mantenimiento y fiabilidad aplicada en la gestión de activos (2nd ed.). INGEMAN. https://doi.org/10.13140/RG.2.2.29363.66083
Crespo Márquez, A. (2007). The maintenance management framework: Models and methods for complex systems maintenance. Springer Verlag. https://doi.org/10.13140/RG.2.2.16765.38884
Woodhouse, J. (1994). Criticality analysis revisited. The Woodhouse Partnership Limited.
Crespo, A., Moreu de León, P., Gómez, J., Parra, C., & López, M. (2009). The maintenance management framework. Journal of Quality in Maintenance Engineering, 15(2), 167–178. https://doi.org/10.1108/13552510910961110
Parra, C., Morán, C., Pizarro, F., Duque, P., Aránguiz, A., González-Prida, V., & Parra, J. (2024). Implementation of the asset management, operational reliability and maintenance survey in recycled beverage container manufacturing lines. Information, 15(12), 784. https://doi.org/10.3390/info15120784
Parra, C., & Crespo, A. (2020). Whitepaper V: Criticality analysis methods based on the risk assessment process. INGEMAN, Escuela Superior de Ingenieros Industriales.
Mitchell, J. (1998). Physical asset management handbook. Clarion Technical Publishers.
Viveros, P., Parra, C., Kristjanpoller, F., Gonzalez-Prida, V., & Crespo, A. (2020). Life cycle cost techniques for decision making in maintenance optimization: Case study in oil and gas industry. DYNA Management, 8(1), 20 pages. https://doi.org/10.6036/MN9825
Parra, C., Viveros, P., Kristjanpoller, F., Crespo, A., & Gonzalez-Prida, V. (2021). Audit and diagnosis in asset management and maintenance applied in the electrical industry. DYNA, 96(3), 238. https://doi.org/10.6036/10037
Kristjanpoller, F., Crespo, A., Barberá, L., & Viveros, P. (2017). Biomethanation plant assessment based on reliability impact on operational effectiveness. Renewable Energy, 101, 301–310.
Jones, R. (1995). Risk-based management: A reliability-centered approach (1st ed.). Gulf Publishing Company.
El-Thalji, I. (2025). Emerging practices in risk-based maintenance management driven by industrial transitions: Multi-case studies and reflections. Applied Sciences, 15(3), 1159. https://doi.org/10.3390/app15031159
Liao, R., He, Y., Feng, T., Yang, X., Dai, W., & Zhang, W. (2023). Mission reliability-driven risk-based predictive maintenance approach of multistate manufacturing system. Reliability Engineering & System Safety, 236, Article 109273.
Fernández, P. M. G., López, A. J. G., Márquez, A. C., Fernández, J. F. G., & Marcos, J. A. (2022). Dynamic risk assessment for CBM-based adaptation of maintenance planning. Reliability Engineering & System Safety, 223, Article 108359.
Abbassi, R., Arzaghi, E., Yazdi, M., Aryai, V., Garaniya, V., & Rahnamayiezekavat, P. (2022). Risk-based and predictive maintenance planning of engineering infrastructure: Existing quantitative techniques and future directions. Process Safety and Environmental Protection, 165, 776–790.
Kim, D., Lee, Y., & Jae Lee, M. (2018). Development of risk-based bridge maintenance prioritization methodology. KSCE Journal of Civil Engineering, 22, 3718–3725.
International Organization for Standardization. (2016). ISO 14224: Petroleum, petrochemical and natural gas industries—Collection and exchange of reliability and maintenance data for equipment. https://www.iso.org/standard/61010.html
Standard Norge. (2017). NORSOK Z-008: Risk based maintenance and consequence classification. https://online.standard.no/en/norsok-z-008-2017
Saaty, T. L. (1990). How to make a decision: The analytic prioritization process. European Journal of Operational Research, 48(1), 9–26.
ENAP SIPETROL. (2015). Matriz de evaluación y gestión de riesgos para la confiabilidad operacional, diseñada para los Yacimientos Pampa del Castillo — La Guitarra (ENAP INF-10-2015-CONF1). Santiago de Chile, Chile.
Parra, C. (2002). Aplicación de la técnica de proceso de análisis jerárquico (AHP) en los sistemas de refinación y producción de la industria petrolera (Doctoral dissertation, Universidad de Sevilla).
Saaty, T. L. (1990). How to make a decision: The analytic prioritization process. European Journal of Operational Research, 48(1), 9–26.
Parra, C., González-Prida, V., Candón, E., De la Fuente, A., Martínez-Galán, P., & Crespo, A. (2020). Integration of asset management standard ISO 55000 with a maintenance management model. In A. Crespo Márquez, D. Komljenovic, & J. Amadi-Echendu (Eds.), Proceedings of the 14th World Congress on Engineering Asset Management (pp. 189–200). Springer. https://doi.org/10.1007/978-3-030-64228-0_17
Parra, C., Tino, G., Parra, J., Crespo, A., Viveros, P., Kristjanpoller, F., & González-Prida, V. (2021). Metodología básica de análisis de riesgo para evaluar la criticidad de activos industriales: Caso de estudio línea de manufactura de envases biodegradables. INGEMAN, Escuela Superior de Ingenieros Industriales. https://doi.org/10.13140/RG.2.2.10422.52802/3
Parra, C., & Crespo, A. (2015). Review of the basic processes of a maintenance and reliability management model. In Project: Design and construction of the third set of locks in the ACP (Autoridad del Canal de Panamá). INGECON—INGEMAN—MWH GLOBAL.