Artificial intelligence, particularly generative AI, presents new opportunities to address longstanding supply chain challenges. By analyzing large volumes of historical and real-time data, generative AI can produce actionable insights that improve decision-making, efficiency, and resilience.
One notable example is Microsoft Dynamics 365 Copilot, an AI-driven assistant integrated into CRM and ERP systems. In Microsoft Supply Chain Center, Copilot can:
- Aggregate supplier-related news (e.g., natural disasters, geopolitical events)
- Notify supply chain managers
- Generate targeted supplier communications using Azure OpenAI Service
This marks one of the first direct implementations of generative AI into operational supply chain management.1
Figure 1. The technical mechanism of Copilot in Microsoft Supply Chain Center
Source: Microsoft Dynamics 365 Blog2
Generative AI Supply Chain Use Cases
1- Demand forecasting
Generative AI models can process historical sales data along with seasonality, promotions, and economic indicators to produce accurate demand forecasts. These insights help businesses:
- Anticipate market trends
- Optimize inventory
- Allocate resources
By training the AI model with this data, it can generate more accurate demand forecasts.
Real-life Example: AI-Powered Inventory Forecasting
Amazon uses advanced AI algorithms to forecast demand and optimize inventory levels across its vast network of warehouses. The system analyzes massive datasets, including historical sales data, customer behavior, and external factors such as holidays and promotions.
By integrating generative AI, Amazon can predict which products will be in demand at specific times, ensuring that its warehouses are stocked efficiently. This not only reduces storage costs but also helps avoid stockouts during peak demand periods like Prime Day or the holiday season.
Amazon’s inventory turnover is one of the highest in the industry, allowing the company to maintain a leaner inventory while still meeting customer expectations for fast delivery.
2- Transportation and Route Optimization
AI analyzes real-time data from traffic conditions to fuel prices, modeling multiple transportation scenarios to select optimal routes that meet cost reduction and timing goals. Generative AI can continuously generate optimized replenishment plans based on real-time demand signals, supplier lead times, and inventory levels.
Real-life Examples
UPS’s ORION system has saved over 10 million gallons of fuel annually while improving on-time delivery performance3 . DHL has seen a 15% increase in on-time deliveries and a 20% reduction in shipment delays using its AI-powered MySupplyChain platform4 .
3- Supply chain optimization
By analyzing data from traffic conditions to fuel prices, generative AI can model multiple transportation and scheduling scenarios, selecting those that meet optimization goals such as cost reduction or shorter lead times.
Real-life Example: Unilever Supplier Risk Assessment
Unilever, a global consumer goods giant, has integrated AI into its supply chain to mitigate supplier risks. By leveraging machine learning and generative AI models, Unilever can monitor external events such as political unrest, natural disasters, and market shifts that might affect its suppliers. This system generates risk scores for each supplier and suggests alternative sourcing options when disruptions are likely.
For more information on such technologies, you can check our article on the AI uses cases for supply chain optimization.
4- Supplier risk assessment
Generative AI evaluates supplier performance using historical data, financial reports, and industry news to detect early signs of risk. This enables:
- Proactive procurement strategies
- Supplier diversification
- Contingency planning
5- Anomaly detection
AI models can flag irregularities in operational data such as production slowdowns, sudden demand spikes, or quality issues before they escalate, enabling faster response times.
6- Product development
Generative AI analyzes market trends, competitor products, and customer feedback to:
- Suggest feature improvements
- Identify gaps in the market
- Generate design concepts
7-Self-Healing Supply Chain Systems
Walmart’s Self-Healing Inventory system detects imbalances in stock levels, then automatically redirects product to where it’s needed most — before issues show up in stores. This represents a new generation of autonomous supply chain management that operates without human intervention.
These intelligent systems predict potential disruptions and automatically implement corrective actions, maintaining supply chain continuity and reducing the need for manual oversight.
8- Contract Analysis and Procurement
Contract analysis is aided by automatically extracting key information from contracts and generating summaries or insights. Review and compare contract terms, identify risks and help ensure compliance. AI supports contract negotiations and renewals by providing data-driven recommendations.
This automation significantly reduces the time procurement teams spend on contract reviews and helps ensure better compliance across supplier relationships.
9- Price optimization
Generative AI considers customer demand patterns, competitor pricing, and market conditions to recommend dynamic pricing strategies that:
- Maintain competitiveness
- Maximize revenue
- Protect margins
10- Inventory Management
Generative AI can set reorder points and safety stock levels based on:
- Demand variability
- Supplier lead times
- Seasonal fluctuations
This helps reduce carrying costs and minimize stockouts.
11- Financial optimization in supply chain
Moreover, the application of generative AI in supply chain financial services and operations can significantly benefit supply chain management by enhancing efficiency, mitigating risks, and improving decision-making processes.
The utilization of generative AI for financial operations within the supply chain can help supply chain leaders address numerous challenges.
Credit Risk Assessment
Generative AI evaluates the creditworthiness of suppliers, partners, and customers by analyzing credit history, financial statements, and market data. This enables informed lending decisions, early detection of default risks, and proactive financial risk management.
Fraud Detection & Prevention
AI models analyze transaction patterns and anomalies to identify potential fraud in supply chain operations. This reduces financial losses, safeguards brand reputation, and ensures operational integrity.
Risk Management
AI assesses risks such as currency volatility, interest rate shifts, and geopolitical events, generating insights to support targeted mitigation strategies and maintain supply chain stability.
FAQ
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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