The globalized world is acknowledged by increased competition, and since competition today is between supply chains there is an importance of controlling the supply chain. The globalization is one of the reasons for the increasing need for organizational change, and the process of implementing strategic change is affected by skills for effective leadership; conceptual, interpersonal and technical skills. The purpose of this study was to develop an understanding of the middle manager’s skillset and how it differs through the phases of organizational change in supply chain. By focusing on middle managers, we put emphasis on the individuals involved in putting the plan for organizational change into practice. A model is developed to explain the utilization of skills in the phases of transition. For this study we used a qualitative research method with a single case study design, by interviewing seven middle managers in an international organization. The empirical data were based on semistructured interviews with respondents in middle managerial positions in supply chain. An abductive approach was applied through the process of writing the thesis. Environmental factors as a reason for organizational changes in supply chain were initially described in the theoretical framework. Change and the phases in change processes as well as the middle manager's role in the change process were further explained. Required skills for effective leadership when presenting, implementing and embedding organizational change initiatives were further described. This thesis shows that a middle manager who faces an organizational change in supply chain are required to focus on specific skills at different points of time, when operating in the different phases of transition.

La sécurité des installations industrielles implique une gestion intégrée de tous les facteurs pouvant causer des incidents. La gestion d’alarmes est une condition qui peut être formulée comme un problème de reconnaissance de motifs pour lequel les motifs temporels sont utilisés pour caractériser différentes situations typiques, en particulier liées au phases de démarrage et d'arrêt. Dans cette thèse, nous proposons une nouvelle approche de gestion des alarmes basée sur un processus de diagnostic. En considérant les alarmes et les actions des procédures d'exploitation standard comme des événements discrets, le diagnostic repose sur la reconnaissance de situation pour fournir aux opérateurs des informations pertinentes sur les défauts induisant les flux d'alarmes. La reconnaissance de situation est basée sur des chroniques qui sont apprises pour chaque situation. Nous proposons d'utiliser un modèle causal hybride du système et des simulations pour générer les séquences d'événements représentatives à partir desquelles les chroniques sont apprises automatiquement en utilisant l'algorithme « Heuristic Chronicle Discovery Algorithm Modified » (HCDAM). Une extension de cet algorithme est présentée dans cette thèse où les connaissances d'experts sont prises en compte comme des restrictions temporelles qui constituent une nouvelle entrée pour HCDAM. Deux cas d’étude illustratifs dans le domaine des procédés pétrochimiques sont présentés. / Industrial plant safety involves integrated management of all the factors that may cause incidents. Process alarm management is a requisite that can be formulated as a pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this thesis, we propose a new approach of alarm management based on a diagnosis process. Assuming the alarms and the actions of the standard operating procedures as discrete events, diagnosis relies on situation recognition to provide the operators with relevant information about the faults inducing the alarm flows. Situation recognition is based on chronicles that are learned for every situation. We propose to use the hybrid causal model of the system and simulations to generate the representative event sequences from which the chronicles are learned using the Heuristic Chronicle Discovery Algorithm Modified (HCDAM). An extension of this algorithm is presented in this thesis where expert knowledge is included as temporal restrictions which are a new input to HCDAM. Two illustrative case studies in the field of petrochemical plants are presented.