While blockchain technology may be grabbing the headlines, it’s big data that will have a bigger and more immediate impact on the logistics and FMCG sector.
That’s the view of Anton Eccles, solutions evangelist at Global Trade Solution (GTS), who told FTW that there was a great deal of potential for the use of big data in logistics.
“I know that any freight and logistics company generates a lot of data, especially if they’re more open to IT. They do hundreds of movements every day,” he said. “GTS processes hundreds of invoices, customs declarations and product libraries daily so there is a wealth of information available to use.”
According to North American transportation company Cerasis, big data refers to the large-scale analysis of data to reduce inconsistencies and produce reports for quick decision-making and is among the most important automated logistics technologies to affect logistics in 2018.
Eccles said that the most important thing about big data was machine learning, where a computer was given the ability to “learn” with data without being explicitly programmed.
“Big data on its own means nothing. How and what you learn from the data is the important thing.”
He said that one of the most beneficial uses of big data in the logistics sector would be the use of information for predictive applications such as risk analysis and proactive risk management actions.
He pointed to the increase in random stops being experienced at South Africa’s borders as a potential factor that could be reduced or almost eliminated by the use of such predictive analyses.
“Where Sars had previously gotten quite good a few years ago by enforcing electronic customs stop processes, that seems to have fallen by the wayside and we’re getting more and more direct stops and interventions at the border posts,” said Eccles. “However, by looking at both your own data of previous stops by customs and the possible reasons for customs to stop your cargo – and then applying the information into the use of big data technologies – you could potentially identify any potential risks that would cause these stops before they even happen.”
He said that this was only one example of where big data could assist in the proactive management of trade risk.
In another example Eccles pointed out that if logistics companies utilised the big data approach, they would be able to use the result of the analytics to optimise routes by looking at traffic patterns, container requirements and shipment intervals.
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The big data approach can help to optimise routes by looking at traffic patterns and shipment intervals. – Anton Eccles
