Reaping efficiency and productivity benefits
“At the same time that a larger volume of data on trucking operations is becoming more readily available, less expensive processing power is also coming on the scene,” says Steve Bryan, chief executive officer of Vigillo. “Those two factors have collided to make big data accessible and affordable. The value to fleets, today, is access to data and a system that enables predictive analytics.
“The challenge of bringing data together is also being addressed effectively by collaboration among various source providers,” Bryan continues. “Companies across the industry are breaking down silos of information that hampered or prevented effective analysis. It’s now possible to extract what you need to know when you need to know it because seemingly disconnected information can be more readily combined into a universe of knowledge.”
Vigillo’s Athena big data Business Intelligence platform capitalizes on the availability of safety, efficiency and profitability data that is critical to trucking. Athena taps into data from a wide variety of integrated channels, brings them together in a cohesive environment and presents information that helps analyze big dollar challenges for fleets.
The benefits of collecting large amounts of data for effective analysis fall into five categories, relates Jack Jones, vice president of truckload product development at Transportation Costing Group (TCG). TCG applies that data in activity-based costing and profitability management tools for truckload and less-than-truckload motor carriers.
“One benefit is process verification,” Jones says, “which helps ensure that workflow processes are appropriate to the operations of a company. Once a process has been established and verified, effective staff training can happen. Additionally, work flow discipline is improved by assuring adherence to established processes or by adjusting training. Peripheral systems and processes that are critical to the ongoing success of a trucking business also benefit from this activity as each of those requires and generates data. Finally, future needs can be addressed more effectively because while no company knows what it will need due to changes in the industry, economy, technology, or even in their own operations, the data collected may be essential to some unforeseen process.”
Data structure is also an important characteristic of any system designed to produce effective analyses. “Systems designed to produce data for effective analysis,” Jones says, “should be both flexible enough to adapt to each carrier’s business model and operations, and rigid enough to produce consistent data. A high level of data integrity, consistent processes and effective integrations and interfaces are the qualification characteristics for data that can be used for actionable, effective analysis.”
