Supply Chain Scanner - Week of January 6, 2025
Weekly blog by Emily Atkins
Why execs are going all-in on AI for supply chain
Ninety percent of supply chain execs recently surveyed by IBM say their organization’s workflows will be digitized with intelligent automation and AI assistants by 2026.
That seems like an extraordinary level of AI adoption, when you consider that the technology is relatively new. But then, in my experience of reporting on developments in this sector, supply chain execs are often early adopters. Logistics and supply chain operations have a history of enthusiastically embracing new technologies. But that’s a topic for a different blog.
In the meantime, let’s look a little deeper into what’s behind the interest in AI. Three streams make up what consultancy McKinsey calls the “AI revolution”: generative (shortened to Gen AI), applied AI, and industrializing machine learning. Gen AI creates new content from unstructured data (such as text and images). Applied AI applies machine learning models for analytical and predictive tasks. Industrializing machine learning speeds up and reduces the risks inherent in the development of machine learning solutions.
Applied AI and industrializing machine learning, boosted by the widening interest in gen AI, have seen the most significant uptick in innovation in the past several years. Tech giant Microsoft just announced it is spending US$80 billion in 2025 to develop data centres that will support the massive computing needs of its AI services.
AI in supply chain has captured executives’ attention as they seek the usual objectives of streamlining operations and cost-effectiveness. In the aftermath of the pandemic – where labour shortages and transportation delays and heightened awareness of the fragility of global supply chains – execs have been searching hard for tools that can help make supply chains more robust.
And in AI they see plenty of potential. AI can be used to optimize shipping and delivery, manage warehouse capacity, track inventory, forecast demand for parts and components, improve worker safety, and help ensure the integrity of transaction records.
Specifically, within warehouses and DCs, AI tools can optimize layouts and plan the most efficient movement patterns for workers and equipment. Using machine learning models to provide floor plans and traffic patterns is not only faster than having humans do it, it can also provide multiple scenarios to accommodate peaks and flows.
Forecasting is another area where execs are seeing gains from using AI tools. It can analyze demand signals from marketing, production line, and point-of-sale systems, to help manufacturers balance inventory against costs. This allows for better warehouse capacity utilization and also gives planners the scope they need to optimize transport costs. AI
forecasting can also take inventory information shared from a customer to assess their demand and adjust the manufacturer’s demand forecasts accordingly.
Reducing human error is yet another of the benefits execs are seeing in AI. The tech can usually spot irregular behavior from both humans and machines before a human observer would. Manufacturers, 3PLs, and transportation providers are training algorithms to expose weakness in their workflows, employee mistakes, and defective product.
AI within enterprise systems is also being employed to catch and prevent errors in financial reporting. Document processing is another AI-powered service, that companies in the transportation and logistics space are increasingly adopting. Service providers like mely.ai, a CITT partner, can automate workflows such as order entry, shipping instructions, billing, invoicing, scheduling and delivery tracking, and more.
Worker safety can also be greatly improved by AI tools. Machine vision can detect and enforce the use of proper PPE and procedures, and analytics from telematics devices can ensure equipment and vehicles are being operated correctly. An-enabled wearables are even being developed that can help prevent workers from injuring themselves through improper lifting techniques, for example.
The wealth of opportunities for AI deployment does not stop there: it can also manage delivery routes and schedules, improve sustainability, and increase efficiency through the use of digital twins. AI’s applications are vast and seemingly ever-expanding.
But there are caveats. Those wishing to use the tech for the good of their enterprise need to understand the risks before they start. McKinsey offers a good overview of these concerns in their CEO’s guide to AI. But at their root, they cover the gamut from privacy and security, to reliability, organizational impact and environmental effects.
AI tools for supply chain certainly promise a lot. If they can deliver the benefits noted above at a reasonable cost, then it’s no wonder that AI is becoming the next big thing in supply chain.
But as with any new tech, and particularly one that is as powerful and potentially uncontrollable as artificial intelligence, there is a lot at stake if the investment proves unproductive or worse – destructive.
Where do you and your organization stand on AI adoption? Are you all-in or just dipping your toes? Let’s talk about it.
Join the conversation on Canada’s Logistics Community forum!
Emily Atkins
President
Emily Atkins Group
Emily Atkins is president of Emily Atkins Group and was editor of Inside Logistics from 2002 to 2024. She has lived and worked around the world as a journalist and writer for hire, with experience in several sectors besides supply chain, including automotive, insurance and waste management. Based in Southern Ontario, when she’s not researching or writing a story she can be found on her bike, in a kayak, singing in the band or at the wheel of her race car. LinkedIn: https://www.linkedin.com/in/emilyatkinsgroup/