Tom Bray, Sr. Industry Business Advisor
September 16, 2022
The basic premise of risk reduction is that you need to be able to use the data to see trends. This process tells you what is predictable when it comes to future risks. Once you know what is predictable, you can then take steps to avoid the risk.
One key is seeing and acting on the data that matters. The problem is there is so much data available. Therefore, to begin the process of risk reduction you must locate, organize, and prioritize the data.
Locating involves looking for the data you need. It can be found in your:
In-house information, such as hard brake data, dash camera footage, internal log auditing results, mock audit results, DQ compliance rates, accident data, maintenance records, driver MVRs, etc.; and
Federal Motor Carrier Safety Administration (FMCSA) roadside inspection and past audit data.
Organizing and prioritizing involves putting the data into buckets in an organized manner, and then prioritizing the buckets and what is in each bucket. An example is having buckets for driver and vehicle data. Inside these buckets, the data would be organized in a useful manner (such as cross referenced by driver, vehicle, event, and date).
The priority inside each bucket would then be safety and compliance data. Operational and general performance data should also be in these buckets, but they should be a lower priority. Once this structure is established, the highest priority should be safety and compliance issues in the driver bucket. This is because a vehicle is normally not a risk until you put a driver into it.
The final step is interpreting the data in such a way that meaningful action can be taken to reduce your risks. As an example, if you see in the driver safety data that several drivers are having the same issues, this is indicating that you need to consider systemic changes (such as updating policies, procedures, training, driver management and supervision, etc.) once the drivers are dealt with (corrected, counseled, disciplined, etc.). On the other hand, if you see one driver is having issues, you know to deal with this one driver.
A common tool that is used in assisting in the interpretation phase is scorecards. Scorecards take the data and prioritize it by assigning values to events. They then present the data in a streamlined view. Scorecards are especially useful when using data to track driver performance, due to the many sources of data involved. They can be developed internally or by an outside vendor. The key is making sure the correct data is being used to generate the score(s) on the scorecard.
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