Capacity optimization in ocean freight industry : An Analytics Solution Opportunity

Shipping Lines getting prudent with capacity planning

Container capacity, like Trucking capacity, has always been a challenge for companies to manage. Excessive capacity has been generally handled by shipping companies by reducing rates, which leads to significant rate reductions, which obviously impacts profit margins. Often, the rates war lead to ocean freight rates so low that it hardly leaves any revenues to cover even operating costs.

However, this time around, shipping companies had a different strategy.

As demand collapsed in March and April, companies decided to decrease capacity- they started canceling sailings and sidelining ships. This created a balance in Supply and Demand, which combined with fuel savings due to decreasing oil prices, led to a complete reversal of expectations of at least $5 billion in losses, when volumes collapsed in March and April from the virus-imposed shutdowns. The Wall Street Journal now expect that on average, the world’s top dozen carriers will collectively make a profit of around $11 billion this year.

As you can see in the graph below, as the demand rises, the number of idle verssels is now decreasing again, signaling an increase in demand.

Source: Alphaliner

Building capacity optimization as a permanent capability

So this was a great example of what prident capacity optimization can do. Now I am not sure what methodology line companies leveraged to determine how much capacity they need to reduce. And Covid19 was an unprecedented event. But if you think about the normal cycles of ups and downs, shipping companies will now probably want to leverage the same methodology in order to hold the rates steady. And this opens an opportunity for an analytics product in my opinion !

A predictive analytics platform that helps liners define their capacity strategy in the short term. While this may not always be 100% accurate, it will still be a huge improvement on the current methodologies most of these companies use. The inputs and architecture is not difficult to imagine (because I can do it so any one else can😁). I think the key here is designing a heuristics that effectively leverages those inputs to suggest capacity levels.

But here is the most attractive $$$ part. Since the external input parameters into this model will be external influences that all major liners face, there is a great opportunity to develop this solution as an off the shelf product.


Vies expressed are my own.

Inspiration from the article comes from WSJ article: Shipping Lines Learn to Make Money By Balancing Supply and Demand

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