When the Data-driven decision-making strategy is adopted in an organization managing their fleet, significant improvements can be experienced. For an organization to have an edge in the competitive market one need to have an insight of operational activities in your fleet. This insight can be achieved by making a connection between your asset and integration with your business systems.
What is Data-driven Decision Making (DDDM)?
Data-driven decision making is a strategy used to assess, test, and improve your operational activity, organization’s strategy and performance. Data-driven decision making moves an organization toward an evidence-based culture that is focused on the future by promoting decisions based on data, experimentation, and evidence rather than opinions or intuition. It comprises of four stages i.e. identifying the target, analyzing the data, communicating the results with the decision-makers, and strengthening the policy by filling the gaps assessed by the data previously.
Considering the importance of Data-driven decision making, Eagle-I introduced the driver scorecard to provide you up-to-date insight into the performance of your driver when they are behind the wheels. Driver scorecard is not only a tool to prevent you from relying on lagging indicators such as accidents or profit but it can also act as a leading indicator of success.
Eagle-I driver scorecard utilizes the data captured by the system of Eagle-I vehicle tracking and fleet management solution which includes the over-speeding violations, harsh braking, harsh acceleration, harsh cornering, hours of service, idling, seatbelt violation.
Driver scorecard can be used beyond the identification of risky drivers. It can also be used to determine the best corrective action using prescriptive analytics. For instance, if the drivers have a certain number of harsh braking incidents, an alert can be issued automatically to the fleet manager for arranging a training module for the specific driver on defensive driving.
Once the risks are identified by using driver scorecard, safety metrics can be defined and associated with each outcome. The selection of appropriate metrics is one of the most important aspects of DDDM, as the usefulness of data for decision making largely depends on the validity of the data and the extent to which they accurately reflect the outputs and outcomes they are meant to represent.
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