Commentary

By Lars Jensen

It is clearly easier to try to sell a new visibility solution by focusing on the darkness .

The buzzwords “ supply chain visibility ” have been around the container shipping sector for a couple of decades now . Over these decades , there are plenty of companies that have developed a market for themselves focused partly or wholly on visibility . Some are profitable , some are not .

Every year , new entrants come into the industry , often with the promise of increased visibility in either specific pockets of the supply chain or across the wider supply chain .

When new technologies emerge , providers often attach them to the “ visibility ” moniker ; a few years ago , we saw this with blockchain , and more recently , we ’ ve seen it with AI .

This is not to denigrate the many providers of visibility software and data , as there are clearly companies and tools that demonstrably create more visibility than we had a few decades ago . But the more interesting question is where value is being created when it comes to visibility .

Entering this millennium , end-to-end visibility was quite poor when it came to something as simple as knowing where your cargo was . And hence the first wave of visibility was born , simply trying to make the information about the cargo ’ s whereabouts available . The second wave was to make it real-time . The third wave was to be able to present the information in a compelling manner , making it much easier to relate to the increasing barrage of data .

The development has been driven by various incumbents in the industry and newcomers from outside the industry . But with a couple of decades of this development , why does “ visibility ” continue to be used as a buzzword ? As something to strive for that — apparently — has not yet been achieved .

There are three fundamentally different reasons for this .

One is that despite the massive amounts of data available today , we still have pockets of data that are not easily interlinked . Increasingly , these pockets of darkness are the exception and not the norm , but it is clearly easier to try to sell a new visibility solution by focusing on the darkness .

The second reason is that just because the data is available in principle , it does not mean that all supply chain stakeholders have bought into these solutions . There can be many reasons for this , but at the end of the day it boils down to just one single reason : They do not see a compelling business case for buying into it .

This leads to the third and most important reason : the value of visibility . There are many ways to calculate this , most often linked to supply chain optimization . By knowing the flows in your supply chain , you can reduce wasteful processes and costs .

These are usually good calculations ; I have even been doing some myself . But in these cases , there is a limit on how much granularity you need to plan your supply chain . Real-time data isn ’ t always necessary , and knowing that a truck has moved one mile closer to the port doesn ’ t really add any value .

‘ Exception management ’

The core of the value lies elsewhere . What decisions can be changed for the better if you have good supply chain visibility ? Here we enter the territory of exception management and forecasting . Optimizing the supply chain is about planning and not about tactical supply chain visibility . But making decisions when something has gone wrong — or when you have a need outside the planned supply chain — requires a much better tactical overview .

Many visibility tools tend to focus on providing visibility centered around the planned supply chain and treating the exceptions as just that — exceptions . Perhaps what the industry needs is a shift in mindset regarding visibility : the perspective that the exceptions are the most important aspect , and creating real-time visibility singularly focused on supporting the decisions that must be taken in those circumstances .

What about AI , you might ask ? Isn ’ t that well-suited for an AI application ? In fact , the exact opposite might be argued .

Looking at recent history , we are in an environment riddled with big and small “ black swan ” events , from the sudden closure of the Suez routing to the collapse of the Key Bridge in Baltimore , Iran hijacking a mega-sized container vessel and completely unexpected congestion in key ports in Asia .

AI ’ s strength is learning from vast arrays of data , or past behavior . But the real value in the supply chain is the ability to adapt to entirely new problems in an instant . For that , AI can help get an overview , but the true competitive advantage is for those who can combine highly skilled people with the visibility into the parameters which truly matter once you have an exception on your hands .

email : lars . jensen @ vespucci-maritime . com

60 Journal of Commerce | September 9 , 2024 www . joc . com