Fleets can use predictive analytics to anticipate — and eliminate — errors before they happen.
As the amount of data available to fleet managers and executives has grown, many have incorporated exception-based tools to view events or metrics – such as speeds, fuel economy, mileages and detention times – that depart from the norm or the acceptable. For some, however, that’s not good enough. They want to know how to prevent exceptions in the first place.
“The cost of operations today – fuel, personnel, etc. – are such that the errors can be quite costly,” says Kathi Laughman, vice president of business systems and services for SCG, a 600-truck carrier based in Houston. “By the time you see an exception, the damage has already been done.”
Preventing problems before they actually occur sounds like a tall order, but in many situations it’s possible to analyze existing data in ways that will predict future outcomes. And with intervention, fleet managers can stop unfavorable results before they happen.
Unlocking your data
Fleet managers typically have numerous reporting tools that summarize past events and transactions, allowing them to take corrective measures. Less common, however, are tools that identify real-time or future trends.
Consider cargo claims. Most trucking companies use a cargo claims ratio (expense to revenue) as an important measure of risk. But this is very much a trailing indicator. At less-than-truckload carrier Saia, for example, the average lag time for customers to file cargo claims is between 30 and 60 days after the incident occurs. By law, customers have up to nine months to file a claim unless otherwise provided by contract.
“If you are waiting for a customer to have filed a claim before you look at issues, it is too late,” says Rick O’Dell, chief executive officer of the Duluth, Ga.-based company. To predict the risk of future cargo claims, Saia uses a measure called “exception-free delivery.” The measure shows the number of deliveries made free of notations for damaged freight.
