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.

This paper introduces a novel methodology for business process simulation, focusing on the incremental transformation of Business Process Modeling and Notation (BPMN) models enriched with Business Process Simulation Interchange Standard (BPSIM) elements into the Discrete Event System Specification (DEVS) formalism. The proposed method enhances the precision and consistency of simulations by systematically converting BPMN components and BPSIM characteristics into DEVS representations, using adaptable rules and templates. A major contribution of this work is the introduction of the Interaction Intermediate Model (I2M), a model that provides a visually lucid representation with significant semantics, effectively encapsulating BPMN and BPSIM simulation aspects. The resulting DEVS model ensures accurate, reliable, and interoperable simulations. We provide a thorough analysis of this methodology, emphasize its advantages, and validate its efficiency through a case study. This method, applicable across various sectors, equips stakeholders with the ability to make informed decisions based on accurate simulation insights, effectively bridging the gap between conceptual modeling and simulation methodologies.