Top view of Container port and container ship transportation
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Better visibility and more responsive supply chains are the only way for shippers and their forwarders to respond to problems related to slow-steaming and containership schedule unreliability.

At last week’s TOC Americas Container Supply Chain event in Cartagena, Bill Rooney, vice president of strategic development at Kuehne + Nagel, told delegates: “Visibility is the solution to slow and unreliable assets.”

He explained that only 75%-80% of containerships arrived at their destination ports on time and it now takes longer to move a box through a North American terminal – which he estimated was, on average, four to five days.

In addition, hinterland container supply chains were also slowing down, he said.

“Truck wait times at ramps and marine terminals are longer, chassis issues around ports are causing further delays and train speeds are also getting slower – intermodal services are around 12% slower than in 2016.

“We cannot run an effective supply chain if we rely on assets on schedule less than 80% of the time, while at the same time customer expectations are for shorter and shorter delivery times.

“Supply chain visibility is the only way we can bridge the gap between asset performance and supply chain expectation – to put it simply, speed will be substituted by visibility because of the predictability this will allow,” he said.

He said that although data quality today was largely “not where it should be”, the possibilities that machine learning and predictive analytics offered the logistics industry gave supply chain operators grounds for optimism.

“The system needs to be able to change the asset performance in the future, so that when you see an issue coming in your supply chain you are able to change the chain’s physical processes for optimal performance,” he said.

He said freight forwarders in particular needed to invest in the right talent to be able to take advantage of these opportunities.

“At Kuehne + Nagel we now employ two people who used to be particle physicists, but are now working as data scientists,” he said.

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