A competição global e a necessidade de otimização dos sistemas de produção e logística têm levado à necessidade de sistemas de manutenção mais eficientes. A habilidade de prever a ocorrência de falhas, através de Sistemas de Manutenção Inteligentes (Intelligent Maintenance Systems - IMS), pode evitar paradas não programadas e constituir uma grande vantagem competitiva. Além disso, um planejamento de demanda mais preciso em Cadeias de Suprimento de Peças de Reposição (Spare Parts Supply Chain - SPSC) resulta na disponibilidade de peças e serviços quando eles são necessários no chão de fábrica, evitando interrupções na produção. Uma integração adequada de ambos domínios é de grande importância neste contexto. Alguns dos desafios relativos a esta integração vêm das diferenças semânticas entre as áreas com diferentes vocabulários e conceitos. Este trabalho pretende propor uma modelagem semântica dos domínios através da criação de uma ontologia, provendo um vocabulário comum e uma integração semântica apropriada entre as áreas, como base para a construção futura de sistemas de informação para integrar IMS e SPSC. / Global competition and the need for logistic and production systems optimization has been leading to the need of more eficiente maintenance systems. Ability to forecast failures, through Intelligent Maintenance Systems (IMS), can avoid breakdowns and be a competitive advantage. Moreover, a more precise demand planning in Spare Parts Supply Chains (SPSC) results in the availability of parts and services when they are needed at shop floor, avoiding production interruptions. A proper integration of both domains is of utmost importance in this context. Some of the challenges related to this integration come from the semantic differences between areas with such a diversity of vocabulary and concepts. This work intends to propose a semantic modelling through the creation of an ontology, providing a common vocabulary and proper semantic integration, as basis for a future implementation of an integration information system between IMS and SPSC.

A competição global e a necessidade de otimização dos sistemas de produção e logística têm levado à necessidade de sistemas de manutenção mais eficientes. A habilidade de prever a ocorrência de falhas, através de Sistemas de Manutenção Inteligentes (Intelligent Maintenance Systems - IMS), pode evitar paradas não programadas e constituir uma grande vantagem competitiva. Além disso, um planejamento de demanda mais preciso em Cadeias de Suprimento de Peças de Reposição (Spare Parts Supply Chain - SPSC) resulta na disponibilidade de peças e serviços quando eles são necessários no chão de fábrica, evitando interrupções na produção. Uma integração adequada de ambos domínios é de grande importância neste contexto. Alguns dos desafios relativos a esta integração vêm das diferenças semânticas entre as áreas com diferentes vocabulários e conceitos. Este trabalho pretende propor uma modelagem semântica dos domínios através da criação de uma ontologia, provendo um vocabulário comum e uma integração semântica apropriada entre as áreas, como base para a construção futura de sistemas de informação para integrar IMS e SPSC. / Global competition and the need for logistic and production systems optimization has been leading to the need of more eficiente maintenance systems. Ability to forecast failures, through Intelligent Maintenance Systems (IMS), can avoid breakdowns and be a competitive advantage. Moreover, a more precise demand planning in Spare Parts Supply Chains (SPSC) results in the availability of parts and services when they are needed at shop floor, avoiding production interruptions. A proper integration of both domains is of utmost importance in this context. Some of the challenges related to this integration come from the semantic differences between areas with such a diversity of vocabulary and concepts. This work intends to propose a semantic modelling through the creation of an ontology, providing a common vocabulary and proper semantic integration, as basis for a future implementation of an integration information system between IMS and SPSC.

A competição global e a necessidade de otimização dos sistemas de produção e logística têm levado à necessidade de sistemas de manutenção mais eficientes. A habilidade de prever a ocorrência de falhas, através de Sistemas de Manutenção Inteligentes (Intelligent Maintenance Systems - IMS), pode evitar paradas não programadas e constituir uma grande vantagem competitiva. Além disso, um planejamento de demanda mais preciso em Cadeias de Suprimento de Peças de Reposição (Spare Parts Supply Chain - SPSC) resulta na disponibilidade de peças e serviços quando eles são necessários no chão de fábrica, evitando interrupções na produção. Uma integração adequada de ambos domínios é de grande importância neste contexto. Alguns dos desafios relativos a esta integração vêm das diferenças semânticas entre as áreas com diferentes vocabulários e conceitos. Este trabalho pretende propor uma modelagem semântica dos domínios através da criação de uma ontologia, provendo um vocabulário comum e uma integração semântica apropriada entre as áreas, como base para a construção futura de sistemas de informação para integrar IMS e SPSC. / Global competition and the need for logistic and production systems optimization has been leading to the need of more eficiente maintenance systems. Ability to forecast failures, through Intelligent Maintenance Systems (IMS), can avoid breakdowns and be a competitive advantage. Moreover, a more precise demand planning in Spare Parts Supply Chains (SPSC) results in the availability of parts and services when they are needed at shop floor, avoiding production interruptions. A proper integration of both domains is of utmost importance in this context. Some of the challenges related to this integration come from the semantic differences between areas with such a diversity of vocabulary and concepts. This work intends to propose a semantic modelling through the creation of an ontology, providing a common vocabulary and proper semantic integration, as basis for a future implementation of an integration information system between IMS and SPSC.

Le cadre général des travaux de cette thèse est la chaîne logistique des pièces de rechange et particulièrement l’amélioration de sa performance en prévision et en gestion des stocks à plusieurs niveaux de service. La spécificité d’une demande faible et erratique ne permet pas l’utilisation des approches classiques de prévision. Par ailleurs, les mesures de performance associées reposant sur des indicateurs purement statistiques ne reflètent pas nécessairement les objectifs de gestion d’une chaîne logistique qui cherche à s’aligner avec différents niveaux d’exigence des contrats de maintenance. Après une description des processus et des outils en prévision et en gestion de stocks utilisés dans ce contexte, ces travaux proposent de nouvelles approches de sélection des méthodes de prévisions qui intègrent prévision et gestion de stocks, qui permettent la priorisation en service ou en inventaire et qui utilisent une segmentation par profil de la demande et par niveau de maturité des pièces. Par la suite, ces travaux considèrent le cas de la différenciation client dans le modèle de gestion de stocks basé sur des prévisions, en comparant la performance de la politique à niveau critique avec les politiques classiques à stock commun ou séparé. Chaque processus étudié a été accompagné d’une analyse comparative entre différentes méthodes de prévisions et politiques de gestion de stocks sur des données réelles du cas d’application industrielle de la chaine logistique de GE Healthcare. Ce qui a permis de construire un ensemble de recommandations en fonction de la segmentation de la pièce et des priorités des décideurs. / The main scope of these works is the spare parts supply chain management, particularly the improvement of forecasting and inventory management performance. The specificity of low and erratic demand does not allow the use of conventional approaches of forecasting. Moreover, the associated performance measurements, based on purely statistical indicators, are not adapted to this context. Indeed, it should align with different levels of expectations of the maintenance contracts. After a presentation of the processes and tools used by the dedicated literature. Here we propose new performance analysis approaches seeking to combine the statistical performance of forecasting methods and inventory management performance while considering decision makers priorities and using demand pattern as well as parts age segmentations. These works also address the use of customer differentiation in the inventory model based on forecasting, by comparing the performance of the critical level policy with the common stock and separated stock policies. Each studied process was associated with a comparative analysis of different forecasting methods and inventory management models based on real data of the spare parts supply chain of GE Healthcare, which allowed us to define a set of methods and parameters recommendations according to both part segmentations and supply chain priorities.

Additive manufacturing is described in literature as a disruptive technology for spare parts supply chains and shows the potential to impact business models for spare parts production through various advantages over conventional manufacturing. Understanding changes in business models is important for companies to adopt any technology and explore business opportunities around it. This research aims to fill the gap in literature on how business models change when additive manufacturing is used for spare parts production in the automotive industry. To investigate those changes a qualitative research design with semi-guided interviews with experts in automotive companies was chosen. The findings showed that the adoption of additive manufacturing in the automotive industry is generally slow and only a few spare parts are specifically developed for additive manufacturing. Especially for niche low-volume and high-value applications AM is already used. At the same time identifying new business cases is needed for broader adoption. Contrasting opinions in literature that additive manufacturing is a disruptive technology, the adopters referred to it rather as a tool. Moreover, the findings showed that the potential of additive manufacturing described in literature is still there but cannot be utilized yet due to various identified barriers at the current stage. The main identified barriers are lack of knowledge, suitable manufacturing data, and resources to explore additive manufacturing.

Additive manufacturing (AM), commonly known as 3D printing, has been attracting attention from practitioners as well as academicians with its continuous evolution from being used primarily for prototyping to now end-product production. Despite this technology's current and future potential, few studies indicate that AM has not been extensively used across all industries. This dissertation addresses knowledge gaps by providing theoretical and empirical support for adopting AM through three essays that study the role of AM in restructuring supply chains. Essay 1 provides systematic support for AM implementation by developing a typology derived from technology-specific potentials and challenges to adopting AM. This study uses an exploratory research approach to collect and analyze data from semi-structured interviews of practitioners with deep knowledge of AM and supply chains from diverse industries. In Essay 2, our results show that AM adoption positively influences supply chain responsiveness and, in turn, reshoring decisions. Essay 3 compares different supply chain configurations based on traditional and AM. We developed a hybrid simulation model combining discrete event simulation and agent-based simulation and compared the performance in terms of wait time and costs. Our results indicate that under certain conditions, the centralized configuration delivers spare parts faster than the distributed configuration, contrary to previous literature findings. However, a hybrid configuration (a combination of centralized and decentralized) provides a better response (reduced wait time) than the traditional, centralized, and decentralized configurations. Collectively, the three essays provide academicians and practitioners with a more comprehensive understanding of how AM creates value for supply chains.