Abstract: A computing system may define a predictive model that is configured to receive, for a given set of data variables, data values that are related to operation of an asset during an observation period, and based on the received data values, output a prediction of fuel consumption of the asset during the observation period, where the given set of data variables may include data variables from different categories of data variables. The computing system may then obtain a dataset that includes, for the given set of data variables, data values that are related to operation of a given asset during a given observation period, apply the predictive model to the obtained dataset, and perform an evaluation of whether any data variable in the given set of data variables presented a fuel savings opportunity for the given asset during the given observation period.

Abstract: The example systems, methods, and devices disclosed herein generally relate to handling operating data from non-communicative assets. In some instances, a data-analytics platform receives operating data points from a given asset of a plurality of assets. Based on that data, the data-analytics platform detects a communication abnormality at the given asset, in accordance with one or more techniques disclosed herein. In response to detecting the communication abnormality, the data-analytics platform designates the given asset as being non-communicative. The data-analytics platform handles operating data points received from the given asset in accordance with the non-communicative designation.