From Intuition to Insight: GenAI’s Role in Supply Chain Planning

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Generative AI (GenAI) is a type of artificial intelligence (AI) that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request.
According to IBM, GenAI relies on sophisticated machine learning models called deep learning models, or those algorithms that simulate the learning and decision-making processes of the human brain. 

“These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users’ natural language requests or questions and respond with relevant new content,” IBM explains in a tutorial on GenAI.

GenAI provides productivity benefits across a wide range of organizations and functions, with supply chain planning being one area that’s ripe for such tech-enabled improvements. By analyzing vast datasets, identifying intricate patterns and predicting future trends, GenAI helps teams make more informed planning decisions. For example, GenAI can analyze historical sales data, weather patterns and economic indicators to predict future demand fluctuations. 

Companies are also using GenAI to pinpoint potential disruptions in the supply chain (e.g., natural disasters, geopolitical events or supplier issues) and implement contingency plans, pivot and mitigate risk.   

GenAI Comes of Age in Supply Chain 
In “How Generative AI Improves Supply Chain Management,” a team of Harvard Business Review (HBR) authors discusses the challenges that modern supply chain planning teams face when designing and optimizing their networks. The article also details how, over the past few decades, advances in information technologies have allowed firms to move from decision-making on the basis of intuition and experience to more automated and data-driven methods. 

“As a result, businesses have seen efficiency gains, substantial cost reductions, and improved customer service,” the authors explain. “Unfortunately, business planners and executives still need to spend considerable time and effort to understand the recommendations coming out of their systems, analyze various scenarios, and conduct what-if analyses.”

Recent advances in large language models (LLMs)—a type of GenAI—are reducing the time to make decisions from days and weeks to minutes and hours, dramatically increasing planners’ and executives’ productivity and impact. For example, the authors say the technology can help planners quickly answer questions like: 

• What would be the additional transportation cost if overall product demand increased by 15%? What would be the additional procurement cost if retailer R uses products only from factory F?
• Can we fulfill all demand if we shut down factory F?
• How much would the total cost of producing product P be reduced if the cost of type M raw material were $1 less per unit?

Planners can also use GenAI to update the mathematical models of a supply chain’s structure and the business requirements to reflect the current business environment, HBR points out, noting that LLM-based technology will soon be able to support end-to-end decision-making scenarios. “We envision that in the next few years LLM-based technology will support end-to-end decision-making scenarios,” the authors add. 

Thinking Beyond the AI Use Cases
In “How GenAI Reimagines Supply Chain Management,” Boston Consulting Group talks about how GenAI can simplify user interfaces, automate operations and decision making, and generate actionable insights from large data sets. In the supply chain planning space, it can transform human-machine collaboration and enable faster and more accurate decision making. 

“It can connect disparate systems and, in the more mature cases, enable autonomous orchestration—coordinating activities and processes without manual intervention,” BCG points out. For best results, the consultancy says organizations must think beyond the limitations of past AI adoption approaches that focused on individual use cases. 

“Effective implementation involves aligning GenAI deployment with business objectives and identifying workflows where the technology can add the most value,” it recommends. “Prioritizing the right areas, rethinking end-to-end workflows, and building a partner ecosystem will ensure that GenAI promotes sustainable improvements to ways of working, automation and analytics.”