Esta tese de doutorado tem como objetivo a definição dos fatores determinantes para o sucesso na Gestão da Manutenção, buscando identificar e explorar seus elementos estratégicos, bem como estabelecer uma estrutura lógica de implementação desses fatores e de elaboração de planos diretores de manutenção na indústria. Para atingir os objetivos, foi empregada a técnica de grupos focados e realizadas as seguintes etapas: i) identificação dos fatores determinantes do sucesso da gestão da manutenção; ii) definição da estrutura para elaboração do plano diretor da manutenção; iii) definição do conjunto de indicadores chaves para o controle do desempenho das atividades de manutenção e iv) aplicação da estrutura proposta na definição do plano diretor de manutenção da planta industrial de Carajás da Empresa Vale para o triênio 2014 – 2016. Como resultados, o estudo determinou 13 fatores de sucesso para a Gestão da Manutenção, entre eles estão: Sistemas Computadorizados de Administração da Manutenção, Sistema de Planejamento e Controle da Manutenção e Sistema de Custeio da Manutenção. Baseado nesses fatores, foi determinada uma estrutura para a construção e revisão anual de planos diretores de manutenção. A utilização desse novo modelo na planta industrial de Carajás possibilitou estimar os seguintes ganhos: i) redução em 20% da taxa de acidentes pessoais; ii) elevação das disponibilidades físicas das plantas com potencial de faturamento líquido de R$ 299,1 milhões; e iii) redução no orçamento da manutenção em 13,3%, o que representa uma redução de custos na ordem R$ 18,63 milhões. / This thesis aims to define the factors for success in Maintenance Management by seeking to identify and explore its strategic elements, as well as establish a logical structure to implement these factors and determine directive maintenance plans in the industry. To achieve the objectives, focus groups were utilized and performed the following steps: i) identification of the determinant factors of successful maintenance management, ii) definition of a framework for preparing the directive maintenance plan, iii) definition of key indicators to track the performance of maintenance activities and iv) application of the proposed structure in the industrial plant Carajás, of Vale Company, for the period of 2014-2016. As a result, the study found 13 success factors for Maintenance Management, among them are: Computerized Maintenance Management System, System of Maintenance Planning and Control and Maintenance Costing System. Based on these factors, a framework was developed for the construction and annual review of directive maintenance plans. Using this new model in the industrial plant Carajás resulted in the following estimated gains: i) a 20% reduction in the rate of injury, ii) an increase in the availability of physical plants with potential net revenues of R$ 299,1 million, and iii) a reduction in the maintenance budget by 13,3% which represents a decrease of U.S. $ 18,63 million in costs.

Une des conséquences les plus marquantes de l'évolution actuelle de l’industrie ferroviaire est l'augmentation des contraintes exercées aussi bien sur les voies que sur les matériels roulants ; tant en termes de sollicitations, de charges, de fréquences, qu'en termes d'exigences de disponibilité et de sécurité. De ce fait, la recherche de politiques de maintenance optimales répondant aux objectifs de disponibilité, de coûts, de sécurité est devenue un sujet particulièrement d'actualité. Pour répondre à cette demande d’ajustement des stratégies de maintenance, le formalisme des réseaux bayésiens est une approche de plus en plus utilisée pour développer des outils d'aide à la décision. Afin de s’affranchir de l’hypothèse markovienne restrictive imposée par l’utilisation « standard » des réseaux bayésiens, une structure originale a été proposée pour modéliser finement un processus de dégradation dans le cadre discret à partir de distributions de temps de séjour quelconques. Cette approche, dénommée Modèles Graphiques de Durée, autorise une finesse de modélisation du processus de dégradation qui permet de reproduire le comportement de systèmes multi-composants et multi-états, tout en tenant compte de variables exogènes. Cette modélisation semi-markovienne de la dégradation a, jusqu'à présent, été utilisée surtout pour évaluer ou comparer des stratégies de maintenance pouvant mêler des approches correctives, systématiques ou conditionnelles. Cette thèse vise à étendre les travaux précédents aux actions de maintenance prévisionnelle. Cette approche, qualifiée également de pronostic, offre en effet l’avantage d’une prédiction de l’instant optimal d’intervention maximisant la durée de fonctionnement du système avant intervention, tout en satisfaisant les contraintes d’exploitation et d’entretien. Les systèmes considérés sont à espaces d’états discrets et finis, périodiquement observables, situation fréquente pour de nombreuses applications industrielles, notamment dans le domaine des transports. Ces travaux de thèse proposent, à partir du formalisme des réseaux bayésiens dynamiques et des modèles graphiques de durée, des outils de pronostic dans le but de permettre la modélisation de politiques de maintenance préventives prévisionnelle. Pour répondre à cet objectif, un algorithme de pronostic basé sur des distributions de temps de séjour a tout d’abord été introduit, dans le but de calculer une estimation de la durée de vie résiduelle (RUL) d'un système et de la mettre à jour à chaque fois qu’un nouveau diagnostic est disponible. Pour améliorer la précision des calculs de pronostic, un nouveau modèle de dégradation a ensuite été proposé pour tenir compte de l'existence éventuelle de plusieurs dynamiques de dégradation coexistantes. Son principe consiste à identifier à chaque instant un mode de dégradation actif, puis à répercuter cette information sur les temps de séjour considérés dans les états suivants par l'utilisation de lois de temps de séjour conditionnelles. Enfin, des solutions pour diminuer la complexité des calculs d'inférence exacte sont proposées / One of the most important consequences due to current developments in the rail industry is the increase of stresses on tracks and rolling stock; in terms of loads, frequencies, and both in terms of availability and security requirements. Therefore, looking for optimal maintenance policies to meet the availability, cost and security objectives has become a particularly topical subject. To address this need of maintenance strategy adjustment, approaches using bayesian networks have increasingly been used for the development of decision support tools. To overcome the restrictive Markovian assumption induced by the use of standard bayesian networks, a specific structure has been proposed to accurately model a degradation process in discrete case using any kind of sojourn time distributions. This approach called "Graphical duration model" make possible to describe multicomponent and multi state system behaviours by taking into account many exogenous variables. This semi-markovian modelling of the degradation has mainly been used to evaluate and compare different maintenance strategies based on corrective, systematic and conditional approaches. This PhD thesis aims to extend previous works to predictive maintenance policies. This approach, based on prognosis computations, has the advantage to predict the optimal intervention time maximizing the remaining useful life of the system and both satisfying operating and maintaining constraints. Considered systems have finite discrete state spaces and are periodically observable as many existing ones in the industry and particularly in the field of transport systems. The presented works, based on the dynamic bayesian network formalism and the graphical duration model, propose prognostic tools in order to model the set of predictive maintenance policies. A prognosis algorithm is first introduced to compute the remaining useful life (RUL) of the system and update this estimation each time a new diagnosis is available. To improve the prognosis estimation accuracy, a new degradation model is proposed to take into account the possible existence of many coexisting degradation modes. The principle is to identify at each time the active degradation mode and then to use this information to choose sojourn times considered in next states using conditional sojourn times distributions. At last, some solutions to reduce the complexity of inference computations are proposed

Esta tese de doutorado tem como objetivo a definição dos fatores determinantes para o sucesso na Gestão da Manutenção, buscando identificar e explorar seus elementos estratégicos, bem como estabelecer uma estrutura lógica de implementação desses fatores e de elaboração de planos diretores de manutenção na indústria. Para atingir os objetivos, foi empregada a técnica de grupos focados e realizadas as seguintes etapas: i) identificação dos fatores determinantes do sucesso da gestão da manutenção; ii) definição da estrutura para elaboração do plano diretor da manutenção; iii) definição do conjunto de indicadores chaves para o controle do desempenho das atividades de manutenção e iv) aplicação da estrutura proposta na definição do plano diretor de manutenção da planta industrial de Carajás da Empresa Vale para o triênio 2014 – 2016. Como resultados, o estudo determinou 13 fatores de sucesso para a Gestão da Manutenção, entre eles estão: Sistemas Computadorizados de Administração da Manutenção, Sistema de Planejamento e Controle da Manutenção e Sistema de Custeio da Manutenção. Baseado nesses fatores, foi determinada uma estrutura para a construção e revisão anual de planos diretores de manutenção. A utilização desse novo modelo na planta industrial de Carajás possibilitou estimar os seguintes ganhos: i) redução em 20% da taxa de acidentes pessoais; ii) elevação das disponibilidades físicas das plantas com potencial de faturamento líquido de R$ 299,1 milhões; e iii) redução no orçamento da manutenção em 13,3%, o que representa uma redução de custos na ordem R$ 18,63 milhões. / This thesis aims to define the factors for success in Maintenance Management by seeking to identify and explore its strategic elements, as well as establish a logical structure to implement these factors and determine directive maintenance plans in the industry. To achieve the objectives, focus groups were utilized and performed the following steps: i) identification of the determinant factors of successful maintenance management, ii) definition of a framework for preparing the directive maintenance plan, iii) definition of key indicators to track the performance of maintenance activities and iv) application of the proposed structure in the industrial plant Carajás, of Vale Company, for the period of 2014-2016. As a result, the study found 13 success factors for Maintenance Management, among them are: Computerized Maintenance Management System, System of Maintenance Planning and Control and Maintenance Costing System. Based on these factors, a framework was developed for the construction and annual review of directive maintenance plans. Using this new model in the industrial plant Carajás resulted in the following estimated gains: i) a 20% reduction in the rate of injury, ii) an increase in the availability of physical plants with potential net revenues of R$ 299,1 million, and iii) a reduction in the maintenance budget by 13,3% which represents a decrease of U.S. $ 18,63 million in costs.

This research was concerned with investigating “the absence of Total Productive Maintenance (TPM)” at a plastic painting plant. TPM is a combination of operations and maintenance activities and is performed by operators under their own authority and is called “autonomous maintenances”. The author has tested the degree of implementation of TPM by using a sevenstep TPM implementation model this highlighted an absence of TPM in the organisation investigated. TPM is a relatively new concept in the South African automotive industry and has not been fully accepted by management as a solution to the maintenance problems in the organisation. Due to a long implementation period and the difficulty in measuring the benefits of the TPM program for the organisation TPM tends not to be implemented by companies. The research studies several maintenance philosophies each with its own advantages and disadvantages. The benefits that arise from the implementation of an effective TPM philosophy were explored. The development of an effective improved model for the implementation of TPM was investigated. Maintenance engineers and managers in any organisation can use this model to implement an effective TPM program. The seven-step TPM implementation model formed the basis for the compilation of a questionnaire. A survey was conducted to determine if an absence of TPM existed in a plastic painting plant. The results from the survey were drawn up and recommendations to further develop a model for the implementation of TPM was put forward.

Esta tese de doutorado tem como objetivo a definição dos fatores determinantes para o sucesso na Gestão da Manutenção, buscando identificar e explorar seus elementos estratégicos, bem como estabelecer uma estrutura lógica de implementação desses fatores e de elaboração de planos diretores de manutenção na indústria. Para atingir os objetivos, foi empregada a técnica de grupos focados e realizadas as seguintes etapas: i) identificação dos fatores determinantes do sucesso da gestão da manutenção; ii) definição da estrutura para elaboração do plano diretor da manutenção; iii) definição do conjunto de indicadores chaves para o controle do desempenho das atividades de manutenção e iv) aplicação da estrutura proposta na definição do plano diretor de manutenção da planta industrial de Carajás da Empresa Vale para o triênio 2014 – 2016. Como resultados, o estudo determinou 13 fatores de sucesso para a Gestão da Manutenção, entre eles estão: Sistemas Computadorizados de Administração da Manutenção, Sistema de Planejamento e Controle da Manutenção e Sistema de Custeio da Manutenção. Baseado nesses fatores, foi determinada uma estrutura para a construção e revisão anual de planos diretores de manutenção. A utilização desse novo modelo na planta industrial de Carajás possibilitou estimar os seguintes ganhos: i) redução em 20% da taxa de acidentes pessoais; ii) elevação das disponibilidades físicas das plantas com potencial de faturamento líquido de R$ 299,1 milhões; e iii) redução no orçamento da manutenção em 13,3%, o que representa uma redução de custos na ordem R$ 18,63 milhões. / This thesis aims to define the factors for success in Maintenance Management by seeking to identify and explore its strategic elements, as well as establish a logical structure to implement these factors and determine directive maintenance plans in the industry. To achieve the objectives, focus groups were utilized and performed the following steps: i) identification of the determinant factors of successful maintenance management, ii) definition of a framework for preparing the directive maintenance plan, iii) definition of key indicators to track the performance of maintenance activities and iv) application of the proposed structure in the industrial plant Carajás, of Vale Company, for the period of 2014-2016. As a result, the study found 13 success factors for Maintenance Management, among them are: Computerized Maintenance Management System, System of Maintenance Planning and Control and Maintenance Costing System. Based on these factors, a framework was developed for the construction and annual review of directive maintenance plans. Using this new model in the industrial plant Carajás resulted in the following estimated gains: i) a 20% reduction in the rate of injury, ii) an increase in the availability of physical plants with potential net revenues of R$ 299,1 million, and iii) a reduction in the maintenance budget by 13,3% which represents a decrease of U.S. $ 18,63 million in costs.

En el presente trabajo se describen los resultados alcanzados de incorporar las herramientas del Mantenimiento Productivo Total para mejorar el nivel de calidad, reducir el desperdicio y Producto No Conforme en el proceso de Flexografía en la empresa Amcor. En primer lugar, se hizo el análisis de la situación actual, en la cual se identificó que la empresa tiene problemas de baja calidad en el proceso de fabricación del envase flexible proveniente del proceso de Flexografía, lo cual se debe al alto índice de desperdicio y producto no conforme, causados por la falta de procedimientos estandarizados. En segundo lugar, se utilizó la metodología del Mantenimiento Productivo Total aplicando los pilares del Mantenimiento Autónomo y Planificado a fin de solucionar los problemas sistémicos del operador. A raíz de ello, se estandarizó los procedimientos de limpieza de limpieza y trabajo. También, se insertó tareas básicas de mantenimiento al operador empleando el mantenimiento autónomo, logrando reducir los problemas sistémicos en el proceso. Por último, con la implementación del TPM aplicando los pilares del Mantenimiento Autónomo y Planificado se logró aumentar la tasa de índice de calidad de 92% a 95%, reducir el desperdicio por empalme y defectos en un 13% y 17% respectivamente, y el Producto No Conforme en un 17%, logrando un ahorro anual de USD 320,512. En resumen, la implementación de Mantenimiento Productivo Total en el proceso de Flexografía con los resultados obtenidos puede ser útil a los investigadores que necesiten mejorar los procesos de producción en una organización. / This work describes the results achieved by incorporating the tools of Total Productive Maintenance to improve the level of quality, reduce waste and Non-Conforming Product in the Flexography process in the Amcor Company dedicated to the manufacture of flexible envelopes. In the first place, the analysis of the current situation was made, in which it was identified that the company has low quality problems in the manufacturing process of the flexible packaging from the Flexography process, this is due to the high rate of waste and non-conforming product, caused by the lack of standardized procedures. Secondly, the methodology of Total Productive Maintenance was used applying the pillars of Autonomous and Planned Maintenance in order to solve the systemic problems of the operator. As a result, cleaning and work cleaning procedures were standardized. Also, basic maintenance tasks were inserted to the operator using autonomous maintenance, managing to reduce systemic problems in the process. Finally, with the implementation of the TPM applying the pillars of the Autonomous and Planned Maintenance, it was possible to increase the rate of quality index from 92% to 95%, reduce waste by splicing and defects by 13% and 17% respectively, and the Product Not Conform to 17%, achieving annual savings of USD 320,512. In summary, the implementation of Total Productive Maintenance in the Flexography process with the results obtained, may be useful to researchers who need to improve production processes in an organization. / Mantenimiento Autónomo / Tesis

The purpose of this study was to determine the effects of a simulation with workedexamples on the creation of episodic memories and the troubleshooting ability of maintenance technician students. Previous research shows that domain knowledge, conceptual knowledge, strategic knowledge, and episodic memories are all required to successfully troubleshoot. While domain, conceptual, and strategic knowledge can all be taught using traditional instruction, episodic memories require students to experience the actual troubleshooting of a fault. Simulations and worked examples are two instructional methods that have proven effective at teaching troubleshooting. This research specifically examined how a simulation combined with worked examples would affect 1) immediate troubleshooting abilities, 2) the creation of episodic memories, and 3) delayed troubleshooting abilities. This study was conducted in two stages and administered via a learning management system due to COVID-19 restrictions. The first stage included a pre-test, a training session using the simulation with worked examples, and an immediate post-test for near and far transfer of troubleshooting abilities. The second stage occurred one week later and included the final posttest for near and far transfer of troubleshooting abilities and creation of episodic memories. Answers to four troubleshooting questions on each of the pre-test and immediate and delayed post-tests were collected to determine any differences in the immediate and retained troubleshooting abilities. Answers to the solution mapping questions were collected to determine the creation of episodic memories. A repeated measure analysis of variance was conducted in SPSS to analyze the results of the troubleshooting pre- and post-tests. A correlational coefficient was used to determine any interaction between episodic memories and delayed troubleshooting abilities. Previous experience levels and participants’ major of study were also examined to determine their effect on the results. The findings show the simulation with worked examples had a statistically significant effect on delayed troubleshooting abilities and the created episodic memories had a positive correlation with the delayed troubleshooting, both with a medium effect size. However, the simulation with worked examples had no statistically significant effect on immediate troubleshooting abilities. Levels of previous experience and participants’ major of study had little effect on the results.

Purpose The purpose is to identify responsibility of owners and operators for maintenance of assets, fixtures and fittings, to identify maintenance management strategies adopted for the efficient operation of hotels and to find out difficulties faced by operators in implementing these strategies. Methodology Four interviews provided supplementary data for the main data collection phase involving administration of 64 questionnaires to hotel operators and maintenance managers. Findings The hotel owner and operator have individual and joint responsibility for asset maintenance. Outsourcing is a maintenance strategy used to minimise operating costs, an important factor that guides this decision is time constraint required to deliver work. In house maintenance it is also influenced by the strategic value of the service and the availability of the resource in the market. In maintenance practice an important challenge is maintaining energy efficiency because of the increasing electronic equipment and high volume of lights.

L'augmentation des données disponibles dans presque tous les domaines soulève la nécessité d'utiliser des algorithmes pour l'analyse automatisée des données. Cette nécessité est mise en évidence dans la maintenance prédictive, où l'objectif est de prédire les pannes des systèmes en observant continuellement leur état, afin de planifier les actions de maintenance à l'avance. Ces observations sont générées par des systèmes de surveillance habituellement sous la forme de séries temporelles et de journaux d'événements et couvrent la durée de vie des composants correspondants. Le principal défi de la maintenance prédictive est l'analyse de l'historique d'observation afin de développer des modèles prédictifs.Dans ce sens, l'apprentissage automatique est devenu omniprésent puisqu'il fournit les moyens d'extraire les connaissances d'une grande variété de sources de données avec une intervention humaine minimale. L'objectif de cette thèse est d'étudier et de résoudre les problèmes dans l'aviation liés à la prévision des pannes de composants à bord. La quantité de données liées à l'exploitation des avions est énorme et, par conséquent, l'évolutivité est une condition essentielle dans chaque approche proposée.Cette thèse est divisée en trois parties qui correspondent aux différentes sources de données que nous avons rencontrées au cours de notre travail. Dans la première partie, nous avons ciblé le problème de la prédiction des pannes des systèmes, compte tenu de l'historique des Post Flight Reports. Nous avons proposé une approche statistique basée sur la régression précédée d'une formulation méticuleuse et d'un prétraitement / transformation de données. Notre méthode estime le risque d'échec avec une solution évolutive, déployée dans un environnement de cluster en apprentissage et en déploiement. À notre connaissance, il n'y a pas de méthode disponible pour résoudre ce problème jusqu'au moment où cette thèse a été écrite.La deuxième partie consiste à analyser les données du livre de bord, qui consistent en un texte décrivant les problèmes d'avions et les actions de maintenance correspondantes. Le livre de bord contient des informations qui ne sont pas présentes dans les Post Flight Reports bien qu'elles soient essentielles dans plusieurs applications, comme la prédiction de l'échec. Cependant, le journal de bord contient du texte écrit par des humains, il contient beaucoup de bruit qui doit être supprimé afin d'extraire les informations utiles. Nous avons abordé ce problème en proposant une approche basée sur des représentations vectorielles de mots. Notre approche exploite des similitudes sémantiques, apprises par des neural networks qui ont généré les représentations vectorielles, afin d'identifier et de corriger les fautes d'orthographe et les abréviations. Enfin, des mots-clés importants sont extraits à l'aide du Part of Speech Tagging.Dans la troisième partie, nous avons abordé le problème de l'évaluation de l'état des composants à bord en utilisant les mesures des capteurs. Dans les cas considérés, l'état du composant est évalué par l'ampleur de la fluctuation du capteur et une tendance à l'augmentation monotone. Dans notre approche, nous avons formulé un problème de décomposition des séries temporelles afin de séparer les fluctuations de la tendance en résolvant un problème convexe. Pour quantifier l'état du composant, nous calculons à l'aide de Gaussian Mixture Models une fonction de risque qui mesure l'écart du capteur par rapport à son comportement normal. / The increase of available data in almost every domain raises the necessity of employing algorithms for automated data analysis. This necessity is highlighted in predictive maintenance, where the ultimate objective is to predict failures of hardware components by continuously observing their status, in order to plan maintenance actions well in advance. These observations are generated by monitoring systems usually in the form of time series and event logs and cover the lifespan of the corresponding components. Analyzing this history of observation in order to develop predictive models is the main challenge of data driven predictive maintenance.Towards this direction, Machine Learning has become ubiquitous since it provides the means of extracting knowledge from a variety of data sources with the minimum human intervention. The goal of this dissertation is to study and address challenging problems in aviation related to predicting failures of components on-board. The amount of data related to the operation of aircraft is enormous and therefore, scalability is a key requirement in every proposed approach.This dissertation is divided in three main parts that correspond to the different data sources that we encountered during our work. In the first part, we targeted the problem of predicting system failures, given the history of Post Flight Reports. We proposed a regression-based approach preceded by a meticulous formulation and data pre-processing/transformation. Our method approximates the risk of failure with a scalable solution, deployed in a cluster environment both in training and testing. To our knowledge, there is no available method for tackling this problem until the time this thesis was written.The second part consists analyzing logbook data, which consist of text describing aircraft issues and the corresponding maintenance actions and it is written by maintenance engineers. The logbook contains information that is not reflected in the post-flight reports and it is very essential in several applications, including failure prediction. However, since the logbook contains text written by humans, it contains a lot of noise that needs to be removed in order to extract useful information. We tackled this problem by proposing an approach based on vector representations of words (or word embeddings). Our approach exploits semantic similarities of words, learned by neural networks that generated the vector representations, in order to identify and correct spelling mistakes and abbreviations. Finally, important keywords are extracted using Part of Speech Tagging.In the third part, we tackled the problem of assessing the health of components on-board using sensor measurements. In the cases under consideration, the condition of the component is assessed by the magnitude of the sensor's fluctuation and a monotonically increasing trend. In our approach, we formulated a time series decomposition problem in order to separate the fluctuation from the trend by solving a convex program. To quantify the condition of the component, we compute a risk function which measures the sensor's deviation from it's normal behavior, which is learned using Gaussian Mixture Models.

I takt med att tekniken utvecklas gör även underhållsstrategierna det. Trots detta är det många företag som fortfarande använder samma underhållsstrategier som man alltid gjort. Olika underhållsstrategier ser behovet av underhåll på olika sätt, allt från att vänta tills felet uppstår till att förebygga fel genom avancerad dataanalys och det kan därför vara svårt för underhållsorganisationer att välja rätt strategi. Detta arbete undersöker hur ett byte av underhållsstrategi kan påverka effektiviteten av underhållet i en större organisation, med fokus på kostnader, personalresurser och tillgänglighet på utrustning. PostNord har valts som fallstudie. De strategier som jämförs och utvärderas är; avhjälpande underhåll, förebyggande underhåll, tillståndsbaserat underhåll samt prediktivt underhåll. Resultatet påvisar ökad effektivitet för tre av fyra strategier, som också ser till företagets behov. Den efterföljande diskussionen av resultatet visar att det framtida behovet är oklart där främst två faktorer som påverkar; årliga volymminskningar samt morgondagens utdelningsmodell. Slutsatsen blir att det finns två olika rekommendationer beroende på hur det framtida behovet ter sig. Om det är ett fortsatt behov av hög tillgänglighet på maskinerna så rekommenderas en kombination av prediktivt underhåll och förebyggande underhåll på drifttid. Om det framtida behovet däremot påvisar att tillgängligheten inte kommer att påverka beslutet i samma grad rekommenderas en kombination av förebyggande underhåll på drifttid och tillståndsbaserat underhåll där tillståndsmätningar görs mot den data som samlas in idag. I båda rekommendationerna räknas fortfarande avhjälpande underhåll in som strategi för hantering av de akuta stopp som kan uppstå. Att titta på andra maskintyper där det framtida behovet framgår tydligare föreslås som fortsatt arbete. / As technology evolves, so do maintenance strategies. Despite this, many companies tend not to change maintenance strategy. Maintenance strategies differ significantly in the way they address the need for maintenance, for some the strategy is to provide maintenance after a fault has occurred while others focus on preventing faults through advanced data analysis. With this diversity in approach, it may be difficult for maintenance organizations to choose the right strategy. This thesis examines how a change of maintenance strategy can affect the efficiency of maintenance in a larger organization, focusing on costs, personal resources and availability of the equipment. As a case study, PostNord has been the selected company for the study. The study compares and evaluates four maintenance strategies; corrective maintenance; preventive maintenance; condition-based maintenance; and predictive maintenance. The results show increased efficiency for three out of four strategies, which also meet the needs of the company. In the discussing the results in the context of PostNord, annual volume reductions and future distribution model, are identified as the two main factors that will affect the future validity and application of results. Subsequently, the conclusion consists of two recommendations depending on distribution model and annual volumes. If there is a continued need for high availability in the machines, then the combination of predictive maintenance and preventative maintenance on operating time is recommended. If the future need does impact on the need of availability, then a combination of preventive maintenance on operating time and condition-based maintenance where measurements are made against the data that are accessible today is recommended. Both recommendations still include corrective maintenance as a strategy for managing the emergency stops that may occur. To focus on machines with a clearer future need are recommended for continued work.