Generative AI in Supply Chains

Companies are progressively integrating artificial intelligence (AI) into their supply chains for tasks like demand planning and procurement. They are also investigating its potential in other areas, such as standardizing processes and enhancing last-mile delivery efficiency.

What is Gen AI?

"Gen AI" refers to "Generative AI," and is a subset of artificial intelligence focused on creating new content, rather than merely analyzing existing data. Unlike traditional AI, which often works by recognizing patterns and making predictions based on historical data, Generative AI can produce original text, images, music, and other forms of content. Examples include language models like which generate human-like text, and image generators that create visuals based on text descriptions. What is interesting is that this technology leverages on advanced algorithms and large datasets to generate outputs that are often indistinguishable from those created by humans.

Applying Gen AI in Supply Chains

Generative AI (GenAI) can revolutionize supply chain planning by leveraging on advanced algorithms to generate accurate demand forecasts, optimize inventory levels, and streamline logistics. We can analyse historical data, market trends, and external factors to predict future needs and automate replenishment. GenAI also simulates changes in the supply chain network, models risk scenarios, and generates insights for improving supplier relationships and last-mile delivery.

By automating complex processes and providing data-driven recommendations, GenAI enhances decision-making, reduces costs, and improves overall efficiency in supply chain management.

Use Case in Logistics: Last Mile Dynamic Route Optimization

In the logistics operations, one of the major challenges is routing in real time.

GenAI can be used to continually update and optimize delivery or pickup routes based on changing factors like traffic conditions, weather and the priority of deliveries. This leads to increased efficiency, reduced fuel consumption and improved customer satisfaction.

Additionally, GenAI can personalize delivery windows based on customer preferences and historical data, leading to a more efficient and responsive last-mile delivery process.

Avoid Clumsy Application of GenAI in Supply Chain Business

It is important to note that although Generative AI (GenAI) is a powerful tool, it has its limitations and should not be viewed as a standalone strategy. Instead, concentrate on the business value it can provide and develop a strategic roadmap to effectively integrate it and drive meaningful impact within the organization.

Additionally, data privacy and security concerns arise, as sensitive supply chain information could be exposed or misused. GenAI's outputs can sometimes be inaccurate or unreliable, potentially impacting operational decisions and efficiency. Ethical issues, such as generating misleading information or deepfakes, can also pose challenges, while the complexity of integrating GenAI into existing systems might disrupt operations.

Lastly, overreliance on AI could erode human expertise and critical thinking. To mitigate these risks, organisations must implement robust data governance, validate AI outputs, and maintain a balance between technological and human input.