Made available in DSpace on 2016-12-23T14:33:39Z (GMT). No. of bitstreams: 1 Dissertacao_CRAVO_G_L_2009.pdf: 1278828 bytes, checksum: ebab7f313edc64bb51241b5c7d587d33 (MD5) Previous issue date: 2009-06-25 / This master s thesis presents an implementation of the GRASP meta-heuristic for solving the Multi-mode Resource constrained Problem of Scheduling Project (MRCPSP). The MRCPSP belongs to the class NP-Hard and therefore has received attention of many researchers. In this thesis, a case study problem of Scheduling Industrial Maintenance is viewed as a MRCPSP. The GRASP was tested with a set of benchmark tests obtained from PSPLIB (Project Scheduling Library). The results showed that the GRASP is a good strategy for solving MRCPSP instances. / Esse trabalho apresenta uma implementação da meta-heurística GRASP para a resolução do Problema de Escalonamento de Projetos com Restrições de Recursos e Múltiplos Modos de Processamento (MRCPSP). O MRCPSP é um problema da classe NP Difícil e por isso vem recebendo atenção dos pesquisadores. Nessa dissertação, também é apresentado um estudo de caso cujo problema de Programação de Manutenção Industrial é visto como um problema de escalonamento de projeto. O GRASP foi testado com o conjunto de instâncias do MRCPSP disponíveis na PSPLIB (Project Scheduling Problem Library). Os resultados obtidos mostraram que o GRASP proposto se configura como uma boa estratégia de solução para o MRCPSP.

Le principal objectif de cette thèse est de contrôler des objets à deux dimensions dans des scènes distantes, en utilisant des flux multimédia interactifs et contextualisés. Pour le cas d’usage de ces travaux, ces objets sont des instruments de mesures de physique. Une caméra IP est placée devant l’instrument à contrôler. Le flux généré est décodé. L’instrument est reconnu par des méthodes de traitement d’images basées sur les points caractéristiques locaux. Un flux multimédia interactif basé sur le standard « MPEG-4 binary format » construit et envoyé aux utilisateurs distants. Pour supporter la collaboration, nous proposons un mécanisme de notifications ainsi que des outils de communication pour reproduire la « sensation de groupe ». Cela permet aux utilisateurs d’appréhender les interactions entre eux. Cette approche nous a conduit à enrichir le standard MPEG-BIFS avec le protocole websocket. L’utilisation du web sémantique nous permet d’encoder des séquences d’interaction prévues avec l’instrument. Le système permet également de proposer l’adaptation à la volée du flux multimédia. L’implémentation d’un prototype est illustré dans la contribution à deux projets open source : Ocelot (http://ocelot.ow2.org) et GPAC (http://gpac.wp.institut-telecom.fr) / The main objective of this thesis is to propose the control of 2D object in a remote scene, using a contextualized, collaborative and interactive multimedia. The 2D objects are mainly electronic devices. For the use case in this work, an IP camera is placed in front of the device to control. The generated stream is decoded. The front of the device is identified using image-processing techniques based on natural feature points. Then, an interactive multimedia based on the MPEG-4 binary format for scenes standard is built and broadcasted to the remote users. In order to handle collaboration, we propose a notification mechanism and communication tools to reproduce the “group awareness”, which allows users to apprehend interactions between them. This approach leads us to enrich the MPEG-4 BIFS with the websocket protocol. The use of the semantic web allows us to encode expected sequences of use of the interactive multimedia. Eventually the system provides on the fly interaction adaptations to the end-user. The implementation of a proof of concept is illustrated in contributions to two open source projects : Ocelot (http://ocelot.ow2.org) and GPAC (http://gpac.wp.institut-telecom.fr)

Les travaux de recherche présentés dans ce mémoire s’inscrivent dans le cadre de la valorisation des connaissances issues des expériences passées afin d’améliorer les performances des processus industriels. La connaissance est considérée aujourd'hui comme une ressource stratégique importante pouvant apporter un avantage concurrentiel décisif aux organisations. La gestion des connaissances (et en particulier le retour d’expérience) permet de préserver et de valoriser des informations liées aux activités d’une entreprise afin d’aider la prise de décision et de créer de nouvelles connaissances à partir du patrimoine immatériel de l’organisation. Dans ce contexte, les progrès des technologies de l’information et de la communication jouent un rôle essentiel dans la collecte et la gestion des connaissances. L’implémentation généralisée des systèmes d’information industriels, tels que les ERP (Enterprise Resource Planning), rend en effet disponible un grand volume d’informations issues des événements ou des faits passés, dont la réutilisation devient un enjeu majeur. Toutefois, ces fragments de connaissances (les expériences passées) sont très contextualisés et nécessitent des méthodologies bien précises pour être généralisés. Etant donné le potentiel des informations recueillies dans les entreprises en tant que source de nouvelles connaissances, nous proposons dans ce travail une démarche originale permettant de générer de nouvelles connaissances tirées de l’analyse des expériences passées, en nous appuyant sur la complémentarité de deux courants scientifiques : la démarche de Retour d’Expérience (REx) et les techniques d’Extraction de Connaissances à partir de Données (ECD). Le couplage REx-ECD proposé porte principalement sur : i) la modélisation des expériences recueillies à l’aide d’un formalisme de représentation de connaissances afin de faciliter leur future exploitation, et ii) l’application de techniques relatives à la fouille de données (ou data mining) afin d’extraire des expériences de nouvelles connaissances sous la forme de règles. Ces règles doivent nécessairement être évaluées et validées par les experts du domaine avant leur réutilisation et/ou leur intégration dans le système industriel. Tout au long de cette démarche, nous avons donné une place privilégiée aux Graphes Conceptuels (GCs), formalisme de représentation des connaissances choisi pour faciliter le stockage, le traitement et la compréhension des connaissances extraites par l’utilisateur, en vue d’une exploitation future. Ce mémoire s’articule en quatre chapitres. Le premier constitue un état de l’art abordant les généralités des deux courants scientifiques qui contribuent à notre proposition : le REx et les techniques d’ECD. Le second chapitre présente la démarche REx-ECD proposée, ainsi que les outils mis en œuvre pour la génération de nouvelles connaissances afin de valoriser les informations disponibles décrivant les expériences passées. Le troisième chapitre présente une méthodologie structurée pour interpréter et évaluer l’intérêt des connaissances extraites lors de la phase de post-traitement du processus d’ECD. Finalement, le dernier chapitre expose des cas réels d’application de la démarche proposée à des interventions de maintenance industrielle. / The research work presented in this thesis relates to knowledge extraction from past experiences in order to improve the performance of industrial process. Knowledge is nowadays considered as an important strategic resource providing a decisive competitive advantage to organizations. Knowledge management (especially the experience feedback) is used to preserve and enhance the information related to a company’s activities in order to support decision-making and create new knowledge from the intangible heritage of the organization. In that context, advances in information and communication technologies play an essential role for gathering and processing knowledge. The generalised implementation of industrial information systems such as ERPs (Enterprise Resource Planning) make available a large amount of data related to past events or historical facts, which reuse is becoming a major issue. However, these fragments of knowledge (past experiences) are highly contextualized and require specific methodologies for being generalized. Taking into account the great potential of the information collected in companies as a source of new knowledge, we suggest in this work an original approach to generate new knowledge based on the analysis of past experiences, taking into account the complementarity of two scientific threads: Experience Feedback (EF) and Knowledge Discovery techniques from Databases (KDD). The suggested EF-KDD combination focuses mainly on: i) modelling the experiences collected using a knowledge representation formalism in order to facilitate their future exploitation, and ii) applying techniques related to data mining in order to extract new knowledge in the form of rules. These rules must necessarily be evaluated and validated by experts of the industrial domain before their reuse and/or integration into the industrial system. Throughout this approach, we have given a privileged position to Conceptual Graphs (CGs), knowledge representation formalism chosen in order to facilitate the storage, processing and understanding of the extracted knowledge by the user for future exploitation. This thesis is divided into four chapters. The first chapter is a state of the art addressing the generalities of the two scientific threads that contribute to our proposal: EF and KDD. The second chapter presents the EF-KDD suggested approach and the tools used for the generation of new knowledge, in order to exploit the available information describing past experiences. The third chapter suggests a structured methodology for interpreting and evaluating the usefulness of the extracted knowledge during the post-processing phase in the KDD process. Finally, the last chapter discusses real case studies dealing with the industrial maintenance domain, on which the proposed approach has been applied.

This master thesis scrutinizes maintenance of production line made of two grinding machines used in bearing production. In the first section of the work, general principles of industrial maintenance are summarized. In the following practical section, maintenance of the production line is analysed. A calculation for determining production line downtime costs caused by machine failure is designed enabling an analysis of the effectiveness of investment into the diagnostic system of the machine. Lastly, possible measures for optimization of the production line’s maintenance are proposed.

Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application / Datengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage. Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application

Industriellt underhåll har upplevt en utveckling från det ursprungliga akuta avhjälpande underhållet till dagens möjligheter till underhåll baserat på data, så kallat tillståndsbaserat underhåll (CBM). För CBM genomförs endast underhåll vid behov och detta bestäms av aktuell data från den studerade utrustningen. Onödigt underhåll minimeras och antalet plötsliga haverier minskar. Utvecklingen mot Internet of Things (IoT) ger upphov till en stor mängd data som potentiellt kan användas vid CBM-underhåll. En utmaning uppstår dock i att identifiera sådan data och hur denna data kan användas. Denna studie har syftat till att undersöka hur sådan data kan identifieras och hur den kan tänkas användas vid CBM-underhåll. Studien har utförts tillsammans med Quant i Karlskrona där Quant genomför alla underhållsrelaterade aktiviteter åt ABB High Voltage Cables, ett industriföretag som tillverkar högspänningskablar. Arbetet har utgått från tre frågeställningar som har syftat till att: Identifiera datavariabler som kan tänkas ha relevans för CBM-underhåll. Tolka de identifierade datavariablerna för att bedöma hur de kan användas i CBM. Bedöma lämpligheten av en potentiell CBM-implementation baserat på identifierad data jämfört med existerande underhållsmetod. Arbetet har avgränsats genom att fokusera på ett enskilt företag och en enskild fabrik. Utöver detta har en avgränsning gjorts där fokus lagts på några få enskilda maskiner och komponenter. Sekretess har även behövt beaktas vid hantering av känslig information. Studien har huvudsakligen utförts kvalitativt, genom att på djupet fokusera på ett fåtal maskiner och komponenter. Arbetet har genomförts i nära samarbete med några av Quants anställda. Processdata har samlats in direkt från maskinerna och analyserats genom att identifiera och studera avvikelser i data. Intervjuer av olika slag, kompletterade med dokument, har varit en viktig metod för att inhämta information från anställda på Quant, både kring hur data kan tolkas men även kring hur olika processer fungerar. Analytic hierarchy process (AHP) genomfördes i fokusgrupp med anställda för att bedöma lämplig underhållsstrategi. Ett potentiellt tillvägagångssätt har identifierats som tillåter användning av processdata för CBM hos en särskild komponenttyp på företaget. Metoden behöver implementeras och testas men potential finns att minska underhållskostnaderna. Intressanta avvikelser i processdata har identifierats hos en annan komponent som bör studeras vidare för att förstå om processdata och avvikelserna kan användas i en CBM-kontext eller inte. Förbättringsområden hos företaget har identifierats i tillämpningen av vibrationsmätning, vilket är en metod med god potential att användas för CBM-underhåll och därmed minska underhållskostnaderna. Oljeanalys tillämpas redan men en intressant fundering är hur företagets oljefiltrering påverkar möjligheterna att implementera ett prediktivt underhåll i framtiden. Detta är ett område som framtida studier behöver titta på och bedöma hur det ska tacklas. AHP har även bekräftats vara en användbar metod för att bedöma lämpligaste underhållspolicyn. / Industrial maintenance has experienced an evolution from the initial corrective maintenance to the possibility of using data based maintenance techniques, so called condition-based maintenance (CBM). Maintenance is only performed when needed under CBM and this is decided based on the data retrieved from the studied equipment. Unnecessary maintenance is minimized and the number of sudden breakdowns decreases. The trend towards Internet of Things (IoT) gives rise to a large amount of data that can potentially be used in CBM maintenance. 'A challenge arises in identifying and using such data. This study has aimed to investigate how such data can be identified and how it might be used in CBM maintenance. This study has been carried out together with Quant in Karlskrona, Sweden, where Quant performs all maintenance related activities for ABB High Voltage Cables, an industrial manufacturing company. The study has been based on three questions that have aimed to: Identify data variables that might be relevant for CBM maintenance. Interpret the identified data variables to assess how they can be used in CBM. Assess the suitability of a potential CBM implementation based on the identified data compared to the existing maintenance method. The study has been delimited by focusing on a single company and a single factory. In addition, a delimitation has been made to focus on a few individual machines and components. A nondisclosure agreement also had to be considered when dealing with sensitive information. This study has mainly been conducted qualitatively, by focusing in-depth on a few machines and components. The work has been done in close collaboration with Quant’s employees. Process data has been collected from the machines and analyzed by identifying and studying data anomalies. Interviews, complemented with documents, has been an important method in obtaining information from Quant employees, both regarding how data can be interpreted but also on how the various processes work. Analytic hierarchy process (AHP) was conducted in a focus group with employees to determine the most appropriate maintenance strategy. One potential approach has been identified that allows the use of process data for CBM on a particular type of component at the company. The method needs to be implemented and tested but the potential exists to reduce maintenance costs. Interesting anomalies in the process data have been identified in another component which should be studied further to understand if the process data and the anomalies can be used in a CBM context or not. Areas for improvement at the company have been identified in the application of vibration measurements, which is a method with good potential to be used in CBM maintenance, thereby reducing maintenance costs. Oil analysis is already used but an interesting question is how the company’s oil filtration affects its ability to implement a predictive maintenance scheme in the future. This is an area that future studies need to look at and assess how it should be tackled. AHP has also been confirmed to be a useful method to determine the most appropriate maintenance policy.

The construction equipment industry has been slow to increase its level of servitization, compared to other related sectors such as the car and flight industries. The fundamental problem is the endless variants of machines and business settings that their customers operate in. Hence, scaling up standardized solutions has been a struggle in the industry. Moreover, the manufacturers are often disconnected from the end customers, where value-added resellersoften act as the middlemen selling machines and offering the services connected to them. They generally have better customer relationships than manufacturers, which implies a better situation for servitization. Companies in the construction equipment industry are pushed more toward data-driven decision-making. One such case is utilizing log data from the machines to predict the remaining useful life. A technology referred to as predictive maintenance. Henceforth, the value-added resellers could potentially increase their level of servitization with service offerings that derive from this technology. This thesis explores the role predictive maintenance has in advancing the level of servitization in the construction equipment industry. This thesis was conducted through a single-case study of a value-added reseller in the construction equipment industry, with a practical model-building part inspired by the methodology of action research. Findings indicate that predictive maintenance implies benefits for servitization purposes by gaining insights about how and when construction equipment fails. However, there are barriers to implementing a predictive maintenance model; both technical and organizational. This thesis suggests systematically evaluating capabilities related to the identified barriers and investing accordingly. Predictive maintenance is an essential tool for providing advanced service agreements in the form of Productivity-as-a-Service. In the end, it comes down to what risk the value-added resellers are willing to take; (1) the risk of incumbency, allowing competitors to implement predictive maintenance and gain market shares or (2) the risk of investing excessively and failing to overcome the challenges related to the implementation of predictive maintenance. This thesis contributes to findings regarding the intersection between servitization and predictive maintenance in the construction equipment industry. More specifically, through the lens of a value-added reseller. From a sustainability perspective, implications may affect companies to reduce waste related to construction equipment maintenance and strengthening economic sustainability through recurring revenues. / Branschen för anläggningsmaskiner har varit långsam med att öka sin nivå av tjänstefiering, jämfört med andra relaterade sektorer som bil- och flygindustrin. Det grundläggande problemet är de många varianterna av maskiner och arbetsmiljöer som deras kunder verkar i. Därför har det varit en utmaning inom branschen att skala upp standardiserade lösningar. Dessutom är tillverkarna ofta bortkopplade från slutkunderna, där värdeskapande återförsäljare agerar rollen som mellanhand för att sälja maskiner och erbjuda relaterade tjänster. De har generellt sett bättre kundrelationer än tillverkarna, vilket innebär andra förutsättningar för tjänstefiering. Företag inom branschen för anläggningsmaskiner drivs alltmer mot datadrivna beslut. Ett sådant fall är att använda data från anläggningsmaskinerna för att förutspå den återstående livslängden. En teknologi som kallas prediktivt underhåll. Till följd av detta kan värdeskapande återförsäljare potentiellt öka sin nivå av tjänstefiering med tjänsteerbjudanden som härrör från denna teknik. Detta examensarbete undersöker vilken roll prediktivt underhåll har för att förbättra nivån av tjänstefiering inom branschen för anläggningsmaskiner. Studien genomfördes som en fallstudie av en värdeskapande återförsäljare inom branschen för anläggningsmaskiner, samt en del inspirerad av aktionsforskning innehållandes att utveckla en modell för prediktivt underhåll. Resultaten tyder på att prediktivt underhåll har tydliga fördelar för tjänstefiering genom att bidra med insikter om hur och när anläggningsmaskiner går sönder. Däremot finns hinder för att implementera en modell för prediktivt underhåll; varav många relaterar till organisatorisk tröghet. Detta examensarbete föreslår att man systematiskt utvärderar förmågor relaterade till de identifierade hindren och investerar därefter. Prediktivt underhåll är ett viktigt verktyg för att tillhandahålla avancerade serviceavtal i form av ”Productivity-as-a-Service”. I slutändan handlar det om vilken risk värdeskapande återförsäljare är villiga att ta; (1) risken att inte investera i tekniken och bli irrelevant när nya aktörer eller konkurrenter avancerar eller (2) risken att investera mycket, men att ändå inte lyckas komma över utmaningarna med att implementera prediktivt underhåll. Denna uppsats bidrar till insikter om skärningspunkten mellan tjänstefiering och prediktivt underhåll inom branschen för anläggningsmaskiner. Mer specifikt genom värdeskapande återförsäljares lins. Ur ett hållbarhetsperspektiv kan dessa implikationer påverka företag att minska avfall relaterat till underhåll av anläggningsmaskiner samt stärka dess ekonomiska hållbarhet genom återkommande intäkter.