A New Kind of Logistics Operation

By Ken Lyon

We have been highlighting the impact generative artificial intelligence has been having on multiple sectors and that this revolution is only getting started. This week, the technology commentator Jonah McIntire wrote a lengthy essay about the likely transformation Gen AI will have on the business of creating software and what this means for SaaS vendors. You can read the full text here…  

The reason I found this of particular interest is that it suggests a profound change in the way companies acquire the necessary technology to support their business goals, but it also suggests that new kinds of company will emerge.

The logistics and supply chain management sector has primarily been comprised of companies whose sole purpose is the collection, qualification and dissemination of data and information to their suppliers, partners and customers. It is only those organisations that manufacture physical products or provide physical assets for the transport and storage of products, that are less likely to be threatened. At least for the foreseeable future. This assumes that they are well managed and operate sensible business models, maintaining profitability and resiliency to unexpected events.

All other companies in the above scenario that are primarily information brokers cannot guarantee that they will survive beyond the next 5-10 years. I accept, this could be construed as sensationalising and overly dramatic. But consider this, the Generative AI tools now becoming available to collect, qualify and direct information flows are starting to be deployed across large organisations at very low cost. These efforts will impact a lot of the people employed to perform these roles.

What is also happening is that as companies understand how effective these tools are, the wave of AI ‘Agents’ being deployed to automate existing processes, are starting to reform those processes into more efficient new ones or removing them altogether. This results in operations that are nimble in response to change, operate at very low cost and can scale very rapidly depending on available compute capacity and power generation.

Seismic shifts like this have happened before, but over a longer time period. Large corporations and banks had huge numbers of accounting staff managing their financial activities. People involved in accounts payables, receivables, bookkeeping, treasury management, etc. Then electronic calculators and computers arrived on the scene, replacing the people and improving the accuracy and efficiency of those organisations. It took maybe a decade to see the real impact of this in the major economies. Global trade went through a similar transformation due to improved communications, the opening up of government computer systems for direct Customs entries and automated tax and duty payments and email for the transfer of shipping documentation. Before the large-scale adoption of technology, processes were manual and people intensive. There are numerous other examples.

There are still many challenges that will impede this transformation. AI based operations require accurate and contextual data and companies have realised that large parts of their legacy data stores contain inaccurate or incomplete datasets. Context is often lacking, e.g. inventory data contains many actual and inadvertent duplications. There are inaccuracies found in shipment and manifest data, and frequently a lack of critical data, usually from the edges of large supply chain networks. But as more automation is involved in the manufacturing process, along with the storage, transport and distribution activities, the quality of data will improve.

The larger challenges are cultural, often related to the solutions provided by large enterprise technology vendors. These organisations have spent decades creating and implementing very sophisticated operational platforms and are the gatekeepers to the massive datasets accumulated over time. These implementations and their ongoing support and maintenance business models are now under severe threat.

Some vendors are embracing the advantages Generative AI can bring, especially if they are able to exploit the rich datasets their clients have accumulated. But as their customers begin to understand how inexpensive exploiting and operating this AI driven revolution will be, questions will be asked. A similar dilemma occurred when private corporate networks were replaced by the Internet and the World Wide Web. The development and deployment of software applications was transformed as companies realised that they could open up their internal systems to customers and partners improving service levels while at the same time reducing costs.

Many of the established enterprise vendors could not survive and there was a wave of mergers and restructurings in response. The impact was also felt at senior management levels within their customers. Some executives that had built careers around championing mega IT projects with commensurately large budgets, found that their business case and projected ROI’s had evaporated.

As has always been the case, companies that provide great products and services to their customers, tend to do well. But survival also depends on being able to anticipate events in the future. With technology this is very difficult given how quickly things can change. In the information intensive business that is logistics and transport, data is currency and accuracy and context informs its exchange rate.

As the article referenced at the start of this piece clearly illustrates, Generative AI that can access this currency enables very rapid development of custom solutions at extremely low cost – think minutes, hours and days, instead of months. The low costs come from both cheap compute capability because of cloud services, and the replacement of general software developers by a very small number of AI specialists.

Because of this, new kinds of logistics operations are likely to emerge and as usual, many may fail, but the winners will change the game. At a recent conference related to AI and knowledge graphs, one of the panelists suggested that it was perfectly reasonable to assume that in the next five years, there could be a one person organisation that provided a range of real time data services to businesses across the globe, that would be generating over a $billion in revenues. The audience did not think this was outlandish or unreasonable.

Perhaps we are reaching the point where a company comprised of a handful of AI specialists, informed by a few experienced logistics practitioners, would be managing several global supply chains as a 4PL? Either way, the capabilities to do this already exist and they are getting better by the day.

Author – Ken Lyon

Source: Ti Insight 

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