Criticality Analysis for Network Utilities Asset Management

The proposed work describes the main part of asset criticality analysis for Distribution Network Services Providers (DNSP), also known as Network Utilities, the severity-value factors definition. The methodology is based on the risk-based evaluation of assets, considering potential impacts of their failures on network value. Thus, it provides the capability to take maintenance management decision in terms of value and risk, considering the whole network under unique and homogeneous criteria. A hierarchy of assets ranked according to with value and risk will come out of this process, which represents a fundamental result serving as input of the subsequent steps of the asset management process. Specific attention is paid to network utilities issues, characterizing assets in these companies, and the services that they provide. In addition to this, high requirements established by the Service Level Agreements (SLA), that are characteristics of network services contracts, make this methodology especially suitable in this application. In order to illustrate method applicability, an example extracted from a real electrical network use case is included.Unión Europea 64573

Multi-criteria decision tool applied to a system reliability for the priorization of spare parts

This paper proposes a method for spare part priorization based on the system reliability behavior. The method considers the values taken by the reliability distribution parameters, as the result of a multi-criteria decision process. The range of values is divided into possible alternatives, which depend on the importance of different criteria. The presented exercise provides a quick view about how different spare part policies can be selected by the effect, not only of the design, installation quality or performed maintenance, but also due to factors that sometimes come from subjective assessments. Hence, the Analytic Hierarchy Process (AHP) includes both qualitative and quantitative criteria in the priorization scheme. The presented method is intended to be a starting point for the analysis of external factors that make an important influence on the decision–making of complex industrial assets, with high amounts of data, system configurations, and maintenance inputs, which will be analyzed in future researches with the support of a tailored software application

The state of the art development of AHP (1979-2017): A literature review with a social network analysis

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979?1990, 1991?2001 and 2002?2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions

The state of the art development of AHP (1979-2017): a literature review with a social network analysis

Although many papers describe the evolution of the analytic hierarchy process (AHP), most adopt a subjective approach. This paper examines the pattern of development of the AHP research field using social network analysis and scientometrics, and identifies its intellectual structure. The objectives are: (i) to trace the pattern of development of AHP research; (ii) to identify the patterns of collaboration among authors; (iii) to identify the most important papers underpinning the development of AHP; and (iv) to discover recent areas of interest. We analyse two types of networks: social networks, that is, co-authorship networks, and cognitive mapping or the network of disciplines affected by AHP. Our analyses are based on 8441 papers published between 1979 and 2017, retrieved from the ISI Web of Science database. To provide a longitudinal perspective on the pattern of evolution of AHP, we analyse these two types of networks during the three periods 1979–1990, 1991–2001 and 2002–2017. We provide some basic statistics on AHP journals and researchers, review the main topics and applications of integrated AHPs and provide direction for future research by highlighting some open questions

Applications of discrete-event simulation to reliability and availability assesment in civil engineering structures

This paper discusses the convenience of predicting, quantitatively, time-dependent reliability and availability levels asso-ciated with most building or civil engineering structures. Then, the paper reviews different approaches to these problems and proposes the use of discrete-event simulation as the most realistic way to deal with them, specially during the design stage. The paper also reviews previous work on the use of both Monte Carlo simulation and discrete-event simulation in this area and shows how discrete-event simulation, in particular, could be employed to solve uncertainty in time-dependent structural reliability problems. Finally, a case study is developed to illustrate some of the concepts previously covered in the paper.Postprint (published version

Análisis FODA y matriz GUT para la gestión empresarial y la resolución de problemas: una aplicación en un caso de estudio brasileño

[EN] The present research aims to use SWOT analysis to identify strategic problems of small business companies (SBCs) from the automotive selling parts of a brazilian city and use the GUT matrix for identifying a Priority Level (PL) result of each problem listed. This case-study is expected to bring improvements to the automotive pole in territory, as a way to quantify and prioritize the actions focused on the weaknesses and threats identified though the SWOT analysis (an acronym for strengths, weaknesses, opportunities, and threats and is a structured method that evaluates those four elements on a strategic way), the GUT matrix (technique for priorization and decision making) will be used according its aspects of severity, urgency and tendency. It finds that solving problems of “Low cooperation between the companies” and increase the level of interaction between the companies will increase generation of information. Solve “infrastructure deficiency and accessibility to the Pole” problems will increase the Association’s recognition and representativeness, bringing closer and enabling the gradual implementation of a more adequate governance.[ES] La presente investigación tiene como objetivo utilizar el análisis FODA para identificar problemas estratégicos de pequeñas empresas (SBC) relativos a la venta de automóviles en una ciudad brasileña, y utilizar la matriz GUT para identificar un resultado sobre el Nivel de Prioridad (PL) de cada problema enumerado. Se espera que este caso de estudio traiga mejoras al polo automotriz en el territorio, de tal manera que se cuantifiquen y prioricen las acciones centradas en las debilidades y amenazas identificadas a través del análisis FODA (acrónimo de fortalezas, debilidades, oportunidades y amenazas, el cuál es un método estructurado que evalúa esos cuatro elementos de manera estratégica), y se utilice la matriz GUT (técnica de priorización y toma de decisiones), según aspectos de severidad, urgencia y tendencia. Se considera que resolver problemas de «Baja cooperación entre las empresas» y aumentar el nivel de interacción entre las empresas aumentará la generación de información. Por otro lado, resolver los problemas de «deficiencia de infraestructura y accesibilidad al Polo» aumentará el reconocimiento y la representatividad de la Asociación, acercando y posibilitando la implementación paulatina de una gobernanza más adecuada

Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). Clasificación ABC Multicriterio: Tipos de Criterios y efectos en la asignación de pesos. ITECKNE, 8(2). doi:10.15332/iteckne.v8i2.35Morash, E. A., & Clinton, S. R. (1998). Supply Chain Integration: Customer Value through Collaborative Closeness versus Operational Excellence. Journal of Marketing Theory and Practice, 6(4), 104-120. doi:10.1080/10696679.1998.11501814Fabbe-Costes, N. (2015). Évaluer la création de valeurdu Supply Chain Management. Logistique & Management, 23(4), 41-50. doi:10.1080/12507970.2015.11758621Flores, B. E., & Clay Whybark, D. (1986). Multiple Criteria ABC Analysis. International Journal of Operations & Production Management, 6(3), 38-46. doi:10.1108/eb054765Partovi, F. Y., & Burton, J. (1993). Using the Analytic Hierarchy Process for ABC Analysis. International Journal of Operations & Production Management, 13(9), 29-44. doi:10.1108/01443579310043619Balaji, K., & Kumar, V. S. S. (2014). Multicriteria Inventory ABC Classification in an Automobile Rubber Components Manufacturing Industry. Procedia CIRP, 17, 463-468. doi:10.1016/j.procir.2014.02.044Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700. doi:10.1016/j.cor.2004.07.014Van Kampen, T. J., Akkerman, R., & Pieter van Donk, D. (2012). SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32(7), 850-876. doi:10.1108/01443571211250112Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer Modelling, 16(12), 71-82. doi:10.1016/0895-7177(92)90021-cGajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process. International Journal of Production Economics, 35(1-3), 293-297. doi:10.1016/0925-5273(94)90095-7Scala, N. M., Rajgopal, J., & Needy, K. L. (2014). Managing Nuclear Spare Parts Inventories: A Data Driven Methodology. IEEE Transactions on Engineering Management, 61(1), 28-37. doi:10.1109/tem.2013.2283170Hadad, Y., & Keren, B. (2013). ABC inventory classification via linear discriminant analysis and ranking methods. International Journal of Logistics Systems and Management, 14(4), 387. doi:10.1504/ijlsm.2013.052744Altay Guvenir, H., & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research, 105(1), 29-37. doi:10.1016/s0377-2217(97)00039-8Rezaei, J., & Dowlatshahi, S. (2010). A rule-based multi-criteria approach to inventory classification. International Journal of Production Research, 48(23), 7107-7126. doi:10.1080/00207540903348361Hatefi, S. M., Torabi, S. A., & Bagheri, P. (2013). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776-786. doi:10.1080/00207543.2013.838328Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. doi:10.1080/00207543.2012.657966Yu, M.-C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421. doi:10.1016/j.eswa.2010.08.127Tsai, C.-Y., & Yeh, S.-W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656-666. doi:10.1016/j.ijpe.2008.02.017Aydin Keskin, G., & Ozkan, C. (2013). Multiple Criteria ABC Analysis with FCM Clustering. Journal of Industrial Engineering, 2013, 1-7. doi:10.1155/2013/827274Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74. doi:10.1016/j.ijpe.2014.05.015Raja, A. M. L., Ai, T. J., & Astanti, R. D. (2016). A Clustering Classification of Spare Parts for Improving Inventory Policies. IOP Conference Series: Materials Science and Engineering, 114, 012075. doi:10.1088/1757-899x/114/1/012075Zowid, F. M., Babai, M. Z., Douissa, M. R., & Ducq, Y. (2019). Multi-criteria inventory ABC classification using Gaussian Mixture Model. IFAC-PapersOnLine, 52(13), 1925-1930. doi:10.1016/j.ifacol.2019.11.484Babai, M. Z., Ladhari, T., & Lajili, I. (2014). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290. doi:10.1080/00207543.2014.952791Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/ijssci.2008.017590Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34. doi:10.1016/j.omega.2015.07.004Douissa, M. R., & Jabeur, K. (2016). A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method. Procedia Computer Science, 96, 550-559. doi:10.1016/j.procs.2016.08.233Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2018). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Bruno, G., Esposito, E., Genovese, A., & Simpson, M. (2016). Applying supplier selection methodologies in a multi-stakeholder environment: A case study and a critical assessment. Expert Systems with Applications, 43, 271-285. doi:10.1016/j.eswa.2015.07.016Poza, C. (2020). A Conceptual Model to Measure Football Player’s Market Value. A Proposal by means of an Analytic Hierarchy Process. [Un modelo conceptual para medir el valor de mercado de los futbolistas. Una propuesta a través de un proceso analítico jerárquico]. RICYDE. Revista internacional de ciencias del deporte, 16(59), 24-42. doi:10.5232/ricyde2020.05903Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. 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A Multi-Criteria Classification Framework for Spare Parts Management: A case study

The offshore petroleum industry can be described as a capital-intensive industry. Capital intensive refers to a heavy and high-value asset structure with long lifetimes that demands considerable effort to maintain. Large investments are required to produce goods and services, and the consequences of downtime, shortage and production loss are extensive. Efficient and reliable maintenance operations are essential to secure safe, productive and reliable production, creating a great incentive to stock up on all kinds of spare parts to reduce the consequences of the above-mentioned. However, there are great costs and inefficiencies related to spare parts inventories. Holding costs are high, turnover ratios are low, and inconsistent demand patterns make demand difficult to predict. Therefore, the trade-off between availability and efficiency is a fundamental principle in inventory management of spare parts. The industry puts a lot of effort into optimising spare parts inventories and spends resources on developing efficient and reliable spare parts operations. Among these efforts is spare parts classification. This is the process of classifying spare parts into distinct groups and is crucial to control the enormous number of parts with different characteristics. The decisions on which characteristics to use in classification practices is not straightforward and has been subject to research and debate for many decades. In current classification practices, most spare parts of an equipment are assigned the same criticality rank as the equipment itself, which is not necessarily the case. Therefore, Moreld Apply AS are interested in developing a method for spare parts classification that further evaluates criticality and consequence analysis on a spare parts level. This study presents a way to classify spare parts using a multi-criteria framework to establish precise criticality classes for each part. The findings in this thesis have ultimately led to the conclusion that multi-criteria approaches have great potential in the classification practices in the industry. We also see that the framework is already implementable for single case scenarios, such as the one analysed in this thesis, and provide reliable results. The results indicate that, in almost all instances, the criticality level of spares is reduced compared to the main equipment. The main contributions of this thesis is a framework with several steps guiding the user through the process of setting up the evaluation, preparing the analysis, as well as doing the analysis. Important aspects will be the selection of the most appropriate classification criteria, data collection processes and preparation activities. These topics form the main body of research.The offshore petroleum industry can be described as a capital-intensive industry. Capital intensive refers to a heavy and high-value asset structure with long lifetimes that demands considerable effort to maintain. Large investments are required to produce goods and services, and the consequences of downtime, shortage and production loss are extensive. Efficient and reliable maintenance operations are essential to secure safe, productive and reliable production, creating a great incentive to stock up on all kinds of spare parts to reduce the consequences of the above-mentioned. However, there are great costs and inefficiencies related to spare parts inventories. Holding costs are high, turnover ratios are low, and inconsistent demand patterns make demand difficult to predict. Therefore, the trade-off between availability and efficiency is a fundamental principle in inventory management of spare parts. The industry puts a lot of effort into optimising spare parts inventories and spends resources on developing efficient and reliable spare parts operations. Among these efforts is spare parts classification. This is the process of classifying spare parts into distinct groups and is crucial to control the enormous number of parts with different characteristics. The decisions on which characteristics to use in classification practices is not straightforward and has been subject to research and debate for many decades. In current classification practices, most spare parts of an equipment are assigned the same criticality rank as the equipment itself, which is not necessarily the case. Therefore, Moreld Apply AS are interested in developing a method for spare parts classification that further evaluates criticality and consequence analysis on a spare parts level. This study presents a way to classify spare parts using a multi-criteria framework to establish precise criticality classes for each part. The findings in this thesis have ultimately led to the conclusion that multi-criteria approaches have great potential in the classification practices in the industry. We also see that the framework is already implementable for single case scenarios, such as the one analysed in this thesis, and provide reliable results. The results indicate that, in almost all instances, the criticality level of spares is reduced compared to the main equipment. The main contributions of this thesis is a framework with several steps guiding the user through the process of setting up the evaluation, preparing the analysis, as well as doing the analysis. Important aspects will be the selection of the most appropriate classification criteria, data collection processes and preparation activities. These topics form the main body of research