10 Generative AI Supply Chain Use Cases in 2024

The global supply chain has been continuously evolving, striving to achieve the most significant advantages in efficiency, cost reduction, and customer satisfaction. However, it faces increasing complexities due to growing customer expectations, rapid market fluctuations, and a rising need for sustainable practices. 

Artificial intelligence, particularly generative AI, offers promising solutions to address these challenges. By leveraging the power of generative AI, supply chain professionals can analyze massive volumes of historical data, generate valuable insights, and facilitate better decision-making processes.

In fact, in March 2023, Microsoft announced Microsoft Dynamics 365 Copilot, an AI-driven assistant integrated into CRM and ERP systems.¹

Copilot in Microsoft Supply Chain Center has a news model that gathers all the supplier-related news that can potentially affect supply chains, such as natural disasters and geopolitical situations. When notified, supply chain managers can send AI-generated and targeted emails to suppliers with Azure OpenAI Service. This is the first direct implementation of generative AI systems into supply chain operations. Figure 1 demonstrates how the assistant works, representing a significant advancement in supply chain visibility.

Figure 1. The technical mechanism of Copilot in Microsoft Supply Chain Center

Source: Microsoft Dynamics 365 Blog²

In this article, we will list and explain the top 10 potential generative AI supply chain use cases.

Generative AI Supply Chain Use Cases

1- Demand forecasting

Generative AI creates models that can analyze large amounts of historical sales data, incorporating factors such as seasonality, promotions, and economic conditions. By training the AI model with this data, it can generate more accurate demand forecasts. This helps businesses better manage their inventory, allocate resources, and anticipate market trends.

2- Supply chain optimization

Generative AI models can analyze various sources of visual or textual data, such as traffic conditions, fuel prices, and weather forecasts, to identify the most efficient routes and schedules for transportation. The AI can generate multiple possible scenarios, and based on the desired optimization criteria, it can suggest the best options for cost savings, reduced lead times, and improved operational efficiency across the supply chain.

For more information on such technologies, you can check our article on the AI uses cases for supply chain optimization.

3- Supplier risk assessment

By processing large volumes of data, including historical supplier performance, financial reports, and news articles, generative AI models can identify patterns and trends related to supplier risks. This helps businesses evaluate the reliability of suppliers, anticipate potential disruptions, and take proactive steps to mitigate risk, such as diversifying their supplier base or implementing contingency plans.

4- Anomaly detection

By analyzing data across various aspects of the supply chain, generative AI models can identify unusual patterns or deviations from the norm. This can help businesses quickly detect potential issues, such as bottlenecks, quality problems, or unexpected changes in demand, and address them before they escalate.

5- Product development

Generative AI can process market data, customer feedback, and competitor information to generate insights about potential gaps or opportunities in the market. This can guide businesses in the development of new products or services that cater to emerging trends or customer satisfaction criteria.

6- Sales and operations planning

Generative AI solutions can integrate data from sales, marketing, production, and distribution to generate more accurate and comprehensive plans. This helps businesses align their strategies across departments, optimize resource allocation, and better respond to changes in demand and market conditions.

7- Price optimization

Generative AI models can analyze factors such as customer demand, competitor pricing, and market conditions to generate optimal pricing strategies. These strategies can help businesses maximize revenue, profit margins, and market share while maintaining a competitive edge.

8- Transportation and routing optimization

Generative AI can play a significant role in transportation and routing optimization within supply chain management. By analyzing vast amounts of data from various sources, AI can generate efficient transportation plans, save time, and improve the overall efficiency of supply chain logistics. 

Generative AI can enable:

• Route optimization with minimized expenses and timely deliveries
• Vehicle and fleet optimization with vehicle wear and tear, and resource utilization
• Dynamic routing with adaptation to disruptions and delays

With these innovative solutions, it can help to maintain a resilient supply chain.

9- Inventory Management

Generative AI models can analyze demand patterns, lead times, and other factors to determine the optimal inventory levels at various points in the supply chain. By generating suggestions for reorder points and safety stock levels, AI can help businesses warehouse management by minimizing stockouts, reducing excess inventory, and lowering carrying costs.

10- Financial optimization in supply chain

Moreover, the use of generative AI in supply chain financial services and operations can significantly benefit supply chain management by improving efficiency, reducing risks, and enhancing decision-making processes. 

The utilization of generative AI for financial operations of the supply chain can help supply chain leaders to solve many problems.

Credit risk assessment

Generative AI can analyze large volumes of data, including credit history, financial statements, and market information, to assess the creditworthiness of suppliers, partners, or customers. This helps supply chain stakeholders to manage financial risks, make informed decisions about extending credit, and identify potential defaults or disruptions in the chain.

Fraud detection and prevention

Generative AI models can analyze transaction data, identify patterns and anomalies, and detect potential cases of fraud in the supply chain. This helps businesses minimize financial losses, protect their reputation, and ensure the integrity of their supply chain operations.

Risk management

AI can analyze various types of risks, such as currency fluctuations, interest rate changes, or geopolitical events, and generate insights to help businesses develop risk mitigation strategies. This can help supply chain stakeholders better manage financial risks and maintain supply chain stability.

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External Links

• 1. “Introducing Microsoft Dynamics 365 Copilot – Microsoft Dynamics 365 Blog.” Microsoft Cloud Blogs, 6 March 2023. Accessed 26 April 2023.
• 2. “Optimizing Supply Chain Performance through Collaboration using Generative AI – Microsoft Dynamics 365 Blog.” The Microsoft Cloud Blog, 16 March 2023. Accessed 2 January 2024.