Developments in robotics have come a long way since Karel Čapek conceived Rossum’s Universal Robot in his 1920 play and flirted with the idea of the artificial worker.
As the father of all thinking robots, “Freddy” would be proud of the outstanding progress his descendants have made since researchers at the AI Department of the University of Edinburgh christened him in the early 1970s.
AI is a tool of human creation which, if managed well, can be of great benefit to humankind.
While AI is still in its infancy, the more it is used, the better it will get. The middle part of the 2020s will be looked back upon as a defining moment in history that changed the way we did things. The field of supply chain, specifically project logistics, is no exception.
The advantages of AI in project logistics are many, but one of the most impactful areas is in the field of product design and transport engineering. Having AI collate data on project site location – route information, bridges along the route, port capabilities and regional geopolitical news, etc. – can yield not only the optimum route, but also where to manufacture and the best mode of transport to get it to the project site.
Companies have already developed programs to help streamline workflow processes in operations and warehousing, or provide route optimization based on live traffic patterns, or better support inventory management based on order-in-hand and expected seasonal demand.
Aiding Procurement Decisions
Let’s consider a scenario where an engineering, procurement and construction (EPC) contractor is deciding whether to procure from Europe, India or China for a project in the Midwestern United States.
In this case, AI could be instructed to model the risks, pros and cons of purchasing from each area, with parameters set as wide as was deemed necessary. Along with production costs or physical routing issues, considerations could include potential tariff changes or route closures, weather events, strike and delay, or even the likelihood of negative public attitudes to human rights in the source location.
Such an analysis would go far beyond conventional considerations of choosing which carrier has the most ideal ship or aircraft to carry the shipment.
Bigger organizations can be expected to invest and make proprietary developments to optimize their service offerings and even possibly experiment a tier-based, fee-supported service for AI-enabled service or analysis. Small to medium organizations that do not have the capital, or are riskaverse, will depend on SaaS providers for off-the-shelf offerings, which could give them a level playing field with the competition in their space.
Rhenus’ Automation division is already pioneering Robotic Process Automation (RPA), benefiting clients with increased productivity, reduced costs and better efficiency. Rhenus Automation is taking advantage of generative AI, partnering with IBM. We will expand its use across businesses on need-based situations that align with our overall strategy. We do so based on the conviction that it is only a matter of time before AI becomes an integral part of all business models: industries will have to learn how to adapt. Those who do not will be left behind.
With AI, Rhenus has the opportunity to be a predictive Logistics Solutions Provider (pLSP) where we can utilize our in-depth analysis of past client requirements and current market trends to help proactively prepare for potential challenges in the predictable future.
Changing Engineering Roles
One thing is for sure, from its codeto- image infancy, AI has already advanced to pre-teenage years of exploring text-to-design concepts. Already happening to an extent, in time, engineers will refine their ability to conceptualize so that AI can generate complete product designs based on data fed to the model, with final tweaking taking place on a review basis.
There will be birth and growing pains. These include risks to privacy – a major concern – and the risk that shared data is vulnerable to cyber attack. Over-reliance on AI could affect the flexibility of a business to cater to its customers, while multiskilled workers will need to “upskill” continuously to stay relevant. As much as AI will be a tool to assist operations, it will also review performance. AI has also already interviewed and hired a human employee.
The impetus to overcome these challenges appears unstoppable. While AI may not yet be ready to override safety considerations, experiments are underway to use it in areas where margins for error are at issue – such as in quality control, forward planning, demand forecasts, etc.
In the supply chain, parallels can be drawn with identifying tolerance level to g-forces, for example, where the sum total of various calculations that ensure staying within tolerance limits is considered one key to the “art” of transport engineering. Today, it is a task that AI could easily take over.
In such a scenario, transport engineering jobs would also be likely to take on more of an editorial role – where the final results of AI are tweaked to requirements specific for the product or the project transport. Other job profiles in logistics will also be redefined as AI matures to every-day use.
We have seen just a glimpse of what AI can do.
