You can’t expect to meet SLAs and KPIs if you simply react to problems as they occur. You must predict the problems and take a proactive approach to ensure they never happen. This requires capturing data from your fleet of robots and sensors, enhancing it with human intelligence and machine learning, and then using those insights to identify predictive trends, behavioral anomalies, and process improvements. Analyzing the data collected and utilizing the insights to construct automated and proactive workflows will significantly improve your fleet’s performance.