ForeSimBI: A predictive analytics decision support tool for capacity planning Abstract   This paper proposes a decision support tool for maintenance capacity planning of complex product systems. The tool xe2x80x93 ForeSimBI xe2x80x93 addresses the problem faced by maintenance organizations in forecasting the workload of future maintenance interventions and in planning an adequate capacity to face that expected workload. Developed and implemented from a predictive analytics perspective in the particular context of a Portuguese aircraft maintenance organization, the tool integrates four main modules: (1) a forecasting module used to predict future and unprecedented maintenance workloads from historical data; (2) a Bayesian inference module used to transform prior workload forecasts, resulting from the forecasting module, into predictive forecasts after observations on the maintenance interventions being predicted become available; (3) a simulation module used to characterize the forecasted total workloads through sets of random variables, including maintenance work types, maintenance work phases, and maintenance work skills; and (4) a Bayesian network module used to combine the simulated workloads with historical data through probabilistic inference. A linear programming model is also developed to improve the efficiency of the decisionmaking process supported by Bayesian networks. The tool uses real industrial data, comprising 171 aircraft maintenance projects collected at the host organization, and is validated by comparing its results with real observations of a given maintenance intervention to which predictions were made and with a model simulating current forecasting practices employed in industry. Significantly more accurate forecasts have been obtained with the proposed tool, resulting in an important cost saving potential for maintenance organizations.

BFM: a scalable and resourceaware method for adaptive mission planning of UAVs UAVs must continuously adapt their mission to face unexpected internal or external hazards. This paper proposes a new BFM model (Bayesian Networks built from FMEA tables for MDP). This scalable model offers a modular and comprehensive method to incorporate different types of diagnosis modules based on BN (Bayesian Networks) and FMEA table (Failure Mode and Effects Analysis) to mission specifications expressed as an MDP (Markov Decision Processes). The BFM model implements the complete decisionmaking process that covers both the application configurations at the embedded system level and the mission planning at the UAV level. These decisions are based on the QoS (Quality of Service) of applications, the resource use and the system and sensors health. We demonstrate on a case study for a target tracking mission that the BFM model can interface hazards and applications specifications and can improve the success and quality of the mission. To the best of our knowledge, this is the first proposal of a systematic method that integrates diagnosis modules to MDP model in order to take care of the implementation of embedded applications during a mission.