The ability to be flexible will be of great importance to stay competitive as a manufacturing company, and the human operator is a key resource to rapidly respond to changes within manufacturing. Creating a cross-trained workforce where the operators have the ability to rotate between tasks is a strategy to increase the labor flexibility, which in turn can increase the manufacturing flexibility. Equipping the operators with supporting technology has been seen beneficial to become more flexible. Using AR (Augmented Reality) for training has great potential, but few tests have been made in a real industrial environment. Furthermore, there is a lack of involvement of the operators in previous research. This study aims to investigate how AR can be implemented practically at an automotive manufacturing company and, by involving the operator, examine how the AR solution can contribute to increased labor flexibility. To fulfill the aim of the study, the following research question was formulated: • What factors are important to consider when implementing AR technology for increased labor flexibility in a manufacturing company? The research process of this study has included a literature review and a single case study conducted at a production line in an automotive manufacturing company. The design thinking process has been followed during the case study, to be able to always include the operators in the process. In the final phase of the case study, two prototypes were created to evaluate, from the operator’s perspective, how AR can be used at the shop floor. Data was gathered using tools from the design thinking toolbox, including explorative interviews and observations. The study results shows that the preferred AR method for experienced operators is using a hand-held device. Head mounted devices still has barriers to overcome, and more tests are needed to evaluate the user acceptance and risks involved with using head mounted devices in an industrial environment. The thesis has contributed to highlight what factors needs to be considered when implementing AR, by conducting real-life tests at the case company. Implementing AR in an industrial environment is not only buying the right hardware and software. There is a need for a strategy in how the technology should be adapted to the organization. This thesis proposes a start of the implementation journey in using AR to increase labor flexibility by formulating solution targets for further developing of AR technology.

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Face à la limitation de la modélisation paramétrique, nous avons proposé dans cette thèse une procédure standard pour combiner les données reçues a partir de Réseaux de capteurs sans fils (WSN) pour modéliser a l'aide de Réseaux de Neurones Artificiels (ANN). Des expériences sur la modélisation thermique ont permis de démontrer que la combinaison de WSN et d'ANN est capable de produire des modèles thermiques précis. Une nouvelle méthode de formation "Multi-Pattern Cross Training" (MPCT) a également été introduite dans ce travail. Cette méthode permet de fusionner les informations provenant de différentes sources de données d'entraînements indépendants (patterns) en un seul modèle ANN. D'autres expériences ont montré que les modèles formés par la méthode MPCT fournissent une meilleure performance de généralisation et que les erreurs de prévision sont réduites. De plus, le modèle de réseau neuronal basé sur la méthode MPCT a montré des avantages importants dans le multi-variable Model Prédictive Control (MPC). Les simulations numériques indiquent que le MPC basé sur le MPCT a surpassé le MPC multi-modèles au niveau de l'efficacité du contrôle. / A Wireless Sensor Network (WSN) consisting of autonomous sensor nodes can provide a rich stream of sensor data representing physical measurements. A well built Artificial Neural Network (ANN) model needs sufficient training data sources. Facing the limitation of traditional parametric modeling, this paper proposes a standard procedure of combining ANN and WSN sensor data in modeling. Experiments on indoor thermal modeling demonstrated that WSN together with ANN can lead to accurate fine grained indoor thermal models. A new training method "Multi-Pattern Cross Training" (MPCT) is also introduced in this work. This training method makes it possible to merge knowledge from different independent training data sources (patterns) into a single ANN model. Further experiments demonstrated that models trained by MPCT method shew better generalization performance and lower prediction errors in tests using different data sets. Also the MPCT based Neural Network Model has shown advantages in multi-variable Neural Network based Model Predictive Control (NNMPC). Software simulation and application results indicate that MPCT implemented NNMPC outperformed Multiple models based NNMPC in online control efficiency.

In the present fierce global competition, poor responsiveness, low flexibility to meet the uncertainty of demand, and the low efficiency of traditional assembly lines are adequate motives to persuade manufacturers to adopt highly flexible production tools such as cross-trained workers who move along the assembly line while carrying out their planned jobs at different stations [1]. Cross-trained workers can be applied in various models in assembly lines. A novel model which taken into consideration in many industries nowadays is called the linear walking worker assembly line and employs workers who travel along the line and fully assemble the product from beginning to end [2]. However, these flexible assembly lines consistently endure imbalance in their stations which causes a significant loss in the efficiency of the lines. The operational time variability is one of the main sources of this imbalance [3] and is the focus of this study which investigated the possibility of decreasing the mentioned loss by arranging workers with different variability in a special order in walking worker assembly lines. The problem motivation comes from the literature of unbalanced lines which is focused on bowl phenomenon. Hillier and Boling [4] indicated that unbalancing a line in a bowl shape could reach the optimal production rate and called it bowl phenomenon.  This study chose a conceptual design proposed by a local automotive company as a case study and a discrete event simulation study as the research method to inspect the questions and hypotheses of this research.  The results showed an improvement of about 2.4% in the throughput due to arranging workers in a specific order, which is significant compared to the fixed line one which had 1 to 2 percent improvement. In addition, analysis of the results concluded that having the most improvement requires grouping all low skill workers together. However, the pattern of imbalance is significantly effective in this improvement concerning validity and magnitude.