Data without context is just numbers, and many transportation companies are “drowning in data, but starving for insights.”
Those were a couple revelations from Adrian Gonzalez, president of Adelante SCM, when speaking during Trimble’s virtual in.Sights conference June 22. A reason fleets struggle to harness and utilize meaningful data is so much is generated, and from so many different sources, that it is difficult to make sense of.
Everything from rates, miles driven, stops per route, dwell time per stop, pickup and delivery times, average speed, temperature, fuel and maintenance costs, and empty miles can be tracked.
“The bottom line is, there is a large amount of data being generated, shared and stored across a multitude of different systems out there,” Gonzalez said, adding “more data is not necessarily better or desirable.”
The goal is to convert data into actionable insights that deliver business benefits. “It’s important to recognize that not everything that can be measured is important, and not everything that is important can be measured,” he said.
Another challenge facing transportation companies is that managers are inherently problem solvers, not data scientists. Gonzalez said fleets may have to invest in retraining their seasoned professionals in order to meet future needs using data.
Experienced managers may dismiss data because it doesn’t support what their own experience tells them is true. “What was true 20 years ago may not be true today,” said Gonzalez.
But those companies that do effectively use data can see monumental benefits. Gonzalez said one fleet saved more than $1 million in six months by rerouting trucks to reduce empty miles.
There are four key areas Gonzalez said can be improved by effectively collecting and analyzing data:
Procurement: Carriers can use data to help shippers anticipate future capacity, and carriers to better align freight demand with capacity.
Performance management: Shippers measure carrier performance using scorecards, and with accurate data carriers can do the same of their shippers. Dwell time can be measured by customer, facility, even day of the week. “Dig down into data to uncover root causes,” Gonzalez advised. Maybe a certain facility is understaffed on a certain day of the week, something that would’ve gone unnoticed without data.
Network design: Fleets must stay on top of how their networks are evolving, especially with increased e-commerce and changes to where products are shipped to and from. Data enables fleets to identify these trends and adjust their design networks as necessary.
Benchmarking: Data also allows fleets to see how they measure up against other players in the market, when it comes to rates, lanes, and service performance.
Customer experience: There has been an increased focus on the customer experience, Gonzalez said. Leading transport companies are selling their services less on rates, and more on providing a better experience. To this end, data allows them to provide real-time insights into estimated time of arrival and allows them to get ahead of potential problems before they become problems. “Competing solely on cost is no longer enough.”
Asked how carriers can get started on better leveraging data, Gonzalez said they need to first set clearly defined goals they wish to achieve. They must also appoint someone to oversee data quality management, to ensure the data collected is useful.
“The responsibility for data quality management is not defined in most companies,” he said.
Carriers should assign a value to data, just like they would other assets such as equipment or real estate. Always ensure the company is collecting the right data and be prepared to change the data collected based on evolving needs or goals.
They must also use modern tools to collect and analyze data. “If you’re still working with spreadsheets, that’s the first order of business,” said Gonzalez. “That’s step one – looking around and saying ‘Where are we still relying on manual processing and spreadsheets?’”
Soon, machine learning will help fleets with data analysis.
“There is more data today than humans can analyze by themselves,” he said. “Algorithms are going to be able to analyze on an ongoing basis huge amounts of data and in some cases automate specific processes. Let the machines automate those aspects of it that can be automated. Machine learning can detect trends and changes up and down, tweak and make adjustments.”