How Multimodal LLMs Address Supply Chain Challenges

Adam J. Fleischer
|  Created: July 8, 2024
How Multimodal LLMs Address Supply Chain Challenges

The modern supply chain is a complex and dynamic network that spans across industries and the globe, encompassing a wide range of processes, everything from procurement and production all the way to distribution and delivery. As global supply chains become increasingly intricate, supply chain managers face numerous challenges, including demand forecasting, inventory management, logistics optimization and risk mitigation. The traditional methods of tackling these challenges come up short more often than not due to their inability to process and analyze today’s incredibly vast amounts of diverse data.

Enter multimodal Large Language Models (LLMs). At the intersection of artificial intelligence (AI) and data analytics, multimodal LLMs are advanced AI systems capable of processing and integrating data from multiple sources, including text, images, audio and video. These models – such as OpenAI's GPT-4o, Google's Gemini and Claude 3.5 Sonnet – have become the talk of the town (pun intended…) by leveraging deep learning techniques to understand and generate human-like responses. Multimodal LLMs have already become incredibly versatile and powerful tools for addressing supply chain challenges and will become more influential as time marches on.

As supply chain researchers Ralf W. Seifert and Richard Markoff say in their article, How will Large Language Models impact supply chains?, “…it is clear that LLMs are taking great leaps forward, are here to stay and are already impactful in certain venues.”

What Makes Multimodal LLMs Unique

A 'multimodal' Large Language Model (LLM) is an advanced artificial intelligence system that has been designed to process and generate multiple forms of data, including text, images, audio and sometimes even video. Unlike traditional LLMs, which focus on text-based inputs and outputs, multimodal LLMs can understand and generate content across many different modalities.

LLM code example
Multimodal LLM processes text, images, audio, and video data

The ability of multimodal LLMs to incorporate varied types of data enables a more comprehensive understanding of complex scenarios. For example, multimodal LLMs can analyze a combination of textual data from logistics reports, visual data from satellite images and real-time sensor data from IoT devices. This allows them to recognize complex patterns and generate insights that are far more nuanced and actionable when one compares them to the responses generated by single-modal LLMs working with just one type of data (usually text, but not always). 

For example, multimodal LLMs have a valuable role to play in the following areas of supply chain management:

Enhancing Demand Forecasting: One of the primary challenges in supply chain management is accurate demand forecasting. Multimodal LLMs can significantly enhance forecasting accuracy by analyzing a myriad of data sources, including historical sales data, market trends, social media sentiments and even weather forecasts. By assimilating such diverse data points, multimodal LLMs can identify patterns and predict demand fluctuations more effectively, helping businesses, for example, optimize inventory levels and reduce the problems that come from inventory stockouts or overstocking​.

Managing Procurement: Ahead-of-the-curve procurement teams are starting to use chatbots and virtual assistants powered by LLMs to assist with procurement tasks. For example, these LLM tools can help teams analyze vendor information and determine which vendor is the best fit for their organization’s needs. Procurement teams also use LLMs to analyze their spend data, helping to identify important trends and patterns.

Optimizing Logistics and Distribution: Logistics optimization, which involves managing the movement of goods from suppliers to customers in the most efficient manner, is an ideal application for the unique capabilities of multimodal LLMs.  By analyzing real-time traffic data, weather conditions and other geopolitical factors, multimodal LLMs can create optimal shipping routes and scheduling. For instance, during a natural disaster, these models can process satellite imagery and weather reports to reroute shipments with the goal of minimizing delays. This dynamic, near-real-time approach to logistics management has the potential to significantly improve an organization’s operational efficiency and customer satisfaction​​.

Improving Risk Management: Supply chains are vulnerable to a variety of risks, including disruptions that are caused by natural disasters, geopolitical tensions and wild fluctuations in financial markets. Multimodal LLMs have the uncanny ability to provide early warnings to problems by monitoring relevant sources for potential risk indicators. They can simulate different scenarios to help you assess the impact of potential disruptions, and then they can also recommend proactive measures to minimize impacts. This predictive capability of multimodal LLMs is nothing short of astounding and can help businesses build more resilient supply chains that can withstand all sorts of unexpected challenges​, even black swan events.

Supercharging Supplier Collaboration and Management: Effective supplier collaboration is in important aspect of maintaining a smooth and efficient supply chain. Multimodal LLMs can analyze communication patterns, contract details and performance metrics to identify areas for improvement in supplier relationships. By providing insights into supplier performance and reliability, multimodal LLMs help businesses make better sourcing decisions and improve partnership management​.

The State of LLM Adoption for Supply Chain

According to a June 2024 McKinsey article, “Revolutionizing procurement: Leveraging data and AI for strategic advantage,” CPOs expect data, analytics and gen AI to play a core role in every business decision by 2030. Yet, most of these CPOs also admitted that they did not have the infrastructure in place to take advantage of new data-driven tools, like multimodal LLMs.

Ai-assisted supply chain illustration
CPOs foresee data, analytics, AI central to supply chains by 2030

However, there are always early adopters, those on the vanguard of new tech, the evangelists who help ignite progress. For example, Azad Ratzki, chief technology officer at BlueGrace Logistics, says in his article, LLMs, Optimization and Automation: Supply Chain's Defenses Against Market Swings: “LLMs have stormed the tech landscape in recent years, and their applications in supply chain management are nothing short of transformative.” 

Supply chain solutions provider Blue Yonder has integrated generative AI capabilities, including LLMs, into its products since late 2023. Blue Yonder Orchestrator is the company’s framework for building generative AI-based supply chain capabilities. It is used to design intelligent assistants (“AI Agents”) to support supply chain managers and executives. Blue Yonder is partnering with its customers to configure these AI Agents to automate tasks and enhance decision-making speed and supply chain resilience. Its AI Agents can proactively identify opportunities and risks, make recommendations and execute decisions through the use of a set of APIs. 

For years, Walmart has used AI to create a more efficient supply chain. The company is passionate about ensuring that products are available where, when and how its customers want them. Walmart uses LLMs to analyze large datasets and optimize its supply chain operations, including for demand forecasting, inventory management and logistics.In addition to the company’s internal usage, in March 2024, Walmart started selling its AI-powered logistics technology named Route Optimization as a SaaS solution through Walmart Commerce Technologies

These are but a few examples that illustrate how LLMs are being leveraged today to address supply chain challenges. The future holds more of this approach for all supply chain professionals who continue to pursue the goals of enhancing efficiency, resilience and overall supply chain performance.

The Future is Changing Fast

Multimodal LLMs represent a significant advancement in the field of artificial intelligence and offer nothing short of transformative potential for supply chain management. By leveraging their ability to process and integrate large amounts of highly diverse data, businesses can overcome many of the supply chain obstacles they face. As these ground-breaking LLMs continue to evolve, their impact on enhancing the efficiency, resilience and sustainability of global supply chains will grow, becoming essential tools for creating a more connected and intelligent future.


About Author

About Author

Adam Fleischer is a principal at, a technology marketing consultancy that works with technology leaders – like Microsoft, SAP, IBM, and Arrow Electronics – as well as with small high-growth companies. Adam has been a tech geek since programming a lunar landing game on a DEC mainframe as a kid. Adam founded and for a decade acted as CEO of E.ON Interactive, a boutique award-winning creative interactive design agency in Silicon Valley. He holds an MBA from Stanford’s Graduate School of Business and a B.A. from Columbia University. Adam also has a background in performance magic and is currently on the executive team organizing an international conference on how performance magic inspires creativity in technology and science. 

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