The internet of things (IoT) and Industry 4.0 technologies are becoming widely used in the field of apiculture to enhance honey production and reduce colony losses using connected scales combined with additional data, such as relative humidity and internal temperature. This paper exploits beehive weight measurements and builds appropriate business rules using two instruments. The first is an IoT fixed scale installed on one hive, taking rich continuous measurements, and used as a reference. The second is a portable nomad scale communicating with a smartphone and used for the remaining hives. A key contribution will be the run and triggering of a business process model based on apicultural business rules learned from experience and system observed events. Later, the evolution of the weight of each individual hive, obtained by either measurement or inference, will be associated with a graphical workflow diagram expressed with the business process model and notation (BPMN) language, and will trigger events that inform beekeepers to initiate relevant action. Finally, the BPMN processes will be transformed into executable models for model driven decision support. This contribution improves amateur and professional user-experience for honeybee keeping and opens the door for interoperability between the suggested model and other available simulations (weather, humidity, bee colony behavior, etc.).

Business processes (BPs) have become extremely complex with thousands of tasks to be completed. These processes should be modelled to manage their complexity, but deviations from these models can occur during execution. To account for the potential outcomes, models are often over-specified, but in fact, it is impossible to anticipate every scenario. Therefore, the issue of how to model the response to these problems arises. The approach put forward in this study entails the creation of a primary model with the aid of domain experts. If a deviation occurs during execution, the system searches for compatible patterns in a collaborative library of workflows, rather than relying on dedicated solutions for specific problems. Nevertheless, the main challenges lie in selecting the most appropriate set of candidates from a vast number of patterns, as well as identifying the optimal injection points on the primary model to correct the deviation. One main objective is to rapidly eliminate incompatible patterns by employing simple mathematical techniques to narrow down the pool of candidate solutions. This serves to minimize the number of patterns that should be considered where more sophisticated methods can then be utilized to rank and select the best solution from this smaller set.

Industrialization of the construction sector is a real challenge, due to the strong compartmentalization still existing in the production process (between stakeholders and between trades involved) and the lack of visibility on the potential return on investment. The digital and ecological transitions are now having an impact on the sector, given the advances made in other industrial sectors. The industrial model in this sector must, for example, consider the need to reduce lead times, costs and the carbon footprint and energy impact of its activities to remain credible and be more competitive. The SmartFabrik project aims to transform construction from traditional processes to a more efficient model, by working upstream from the very design of the building, producing industrial components off-site and assembling on site. By combining the principles of a systems vision with the principles and processes of systems engineering, and by highlighting the importance and benefits of modelling, this article proposes an original methodological contribution, the SmartFabrik method, which addresses this design phase but has an impact on the entire life of the building in question.

Gestion de la Continuité et de l’Intégrité des Processus Métiers à l’aide de Corrections Basées sur des Patterns: Cette thèse présente une approche pour la gestion des déviations dans les flux de travail utilisant le Business Process Model and Notation (BPMN). La recherche répond au besoin de gestion efficace des déviations en intégrant un cadre complet comprenant la correction des déviations basée sur des modèles et un mécanisme enrichi de State Token. L’approche est testée par une étude de cas dans le domaine de l’apiculture, démontrant l’applicabilité pratique et l’efficacité de la méthode proposée. Les contributions clés incluent le développement d’une bibliothèque de modèles, la caractérisation des éléments BPMN et un mécanisme pour aider à la prise de décision dans la gestion des déviations. Les résultats montrent que l’approche peut corriger efficacement les déviations, assurant la continuité et l’intégrité du flux de travail.