In the information age, data collection and analytics have affected best practices in every industry. From business decisions at the C-suite to basketball plays drawn up on a white board (or tablet), analytics have changed the way decision makers assess their landscapes. For risk management managers of trucking companies, data collection can be especially important to pre-emptively avoid common roadblocks for their companies.
Smaller and newer trucking companies may doubt the benefits of analytics, believing they do not have the fleet size or the historical data to justify the costs of implementing data collection methods. However, small trucking companies, should also be taking advantage of data analysis to limit accidents, stay compliant to safety standards and gain a competitive advantage.
What data should I be collecting?
One of the biggest issues regarding launching data collection is setting up parameters. With new technology, the amount of collectable data can be overwhelming. One data scientist, preaches the importance of focusing on data quality as opposed to quantity.
Having a lot of data doesn’t do you much good if it doesn’t pertain to the key issues that you want to be measuring. For instance, if you wanted to determine when inclement weather is most likely to affect your business, having a lot of data on tire pressure and axle stress wouldn’t help you.
The data you should collect depends on what questions you want answered. For smaller companies with less man power and resources, narrowing your data view to the crucial areas of your business will be especially helpful for getting a ROI on your analytics programs.
How predictive analysis can inform fleet safety
One of the biggest liability concerns for trucking companies are accidents that happen from driver negligence. Predictive analysis can help prevent accidents, by providing key information about when accidents tend to happen. By utilizing data around previous accidents, you can help your fleet avoid potential risks.
The parameters around this data can include driver behavior, such as driving too closely to a car in front, as well as macro concerns, like particularly risky routes that see a lot of vehicle accidents during a specific time of day. These insights can inform how you design trucking schedules to minimize the potential for vehicle accidents, and help you address issues like driver fatigue.
Using data analysis to schedule truck maintenance
Truck maintenance is a huge part of making sure a trucking business stays efficient. According to the American Transportation Research Institute, maintenance accounts for nearly 10% of the operational cost per mile for companies. An untimely breakdown can cause huge inefficiencies from lost productivity time, to bigger repair costs and late deliveries, not to mention the potential for liability claims from accidents involving vehicle failure.
By utilizing data collected on variables such as engine temperature and oil pressure, you may be able to find optimal operating levels for your trucks, as well as automatically get notifications when a truck might be due for maintenance with machine learning programs. These practices can help minimize both your risk of vehicle failure and your maintenance spending.
Small companies and big data
While smaller trucking companies may believe that they do not have as large of a sample size to track data from, big data practices can have immediate benefits on risk management with the right implementation.
Setting up data collection can also give you a clear picture of operational norms, which will be especially helpful for making effective decisions about scaling your business in the future.
