By unlocking the power of big data and predictive analytics, the cargo transport industry is experiencing a new era of digital disruption and transformation.
What has been historically considered a very conservative sector, the cargo transport industry is now facing disruption from a data analytics company that is accelerating that digital transformation.
Transmetrics, the startup that launched this revolution, combines the might of big data with the foresight of predictive analytics to logistics companies in order to accurately forecast shipping demands and optimize their transport networks.
Before Transmetrics came along in 2013, main transport players for air, land, and sea found themselves far behind when it came to digitalization and accurate forecasting by using antiquated methods.
Now, with extensive experience in demand forecasting and predictive optimization for the cargo transport industry, Transmetrics announced on Thursday that it was selected by NileDutch — a major shipping company, focusing on links between West Africa and the rest of the world — as the best company to implement its logistics software.
“The project with NileDutch is strategically important for us. The container still is a symbol of globalization and it revolutionized the way how transportation works worldwide. With machine learning and intelligent algorithms we now want to revolutionize how empty container handling and relocation is organized. Our envisioned goal for the project with NileDutch is to reduce the cost of empty container logistics by 10-15 percent,” said Anna Shaposhnikova, CCO and co-founder of Transmetrics.
The Sociable spoke with the Transmetrics CCO and co-founder to find out more about how the cargo transport industry is being shaken-up.
When it comes to working with big data, pulling the best information forward is one of the biggest challenges any company faces using this cutting-edge technology.
“Collecting data is not that difficult,” said Shaposhnikova. “The problem is making sense out of it.”
“We use the available and reliable tools for data collection, and it happens automatically. But usually data is inconsistent, full of gaps, kept in different systems, or is otherwise incomplete. That’s where Transmetrics is disrupting the industry.”
Traditional record-keeping among cargo transport players could be very unorganized in the past. Manually entering details into the data systems could bring about big inconsistencies, especially when measuresments were recorded inaccurately due to human error, which would later compound.
One of the methods Transmetrics uses to bring transparency to the industry while reducing inconsistencies is something called Data Cleansing.
“In order to do anything with this data, you have to enrich it and cleanse it. If transport companies could do this in-house, they would do it, but it requires hiring data analysts and data scientists,” says Shaposhnikova.
Data cleansing in-house would be too expensive for transport companies, which is one of the many problems Transmetrics looks to resolve.
And now the company’s products help companies make better, data-driven decisions and continuously improve their operational performance — the latest of which, NileDutch, is now implementing in order to streamline its empty container flows and increase efficiency.
“We had been looking for a partner who could help us streamline our empty container flows for a long time. There are different IT solutions on the market, but we chose Transmetrics over other providers because they proved their technical expertise and also demonstrated a deep knowledge of the logistics industry and container shipping in particular, which is very difficult to find,” said Carlo Zaalberg, Global Director Logistics at NileDutch.
With the start of the cooperation, Transmetrics also will launch its new product AssetMetrics.
AssetMetrics uses historical data of the shipping company and improves those data sets to the extent that the intelligent algorithm can produce reliable predictions. In earlier test projects, Transmetrics was able to increase the data quality by around 75 percent during this enrichment process. Afterwards, the system will create a demand forecast model, which is then optimized by the algorithm.
With its technology, Transmetrics was achieving from 25% to 50% higher forecast accuracy than the in-house teams of their customers.