How supply chains benefit from using generative AI

Across the end-to-end supply chain, the buzzworthy technology adds extra capabilities to AI tasks and promises a simplified user experience.

In brief:

• These use cases exist today, and whether you win or lose in the market may soon depend on having the best AI models and the data quality to match them.
• To begin, identify a business need and then embolden it with generative technology, whether in planning, sourcing, manufacturing or delivery. 

Plan

Generative AI adds simplicity to interactions throughout tech-enabled planning efforts. The “chat” function of one of these generative AI tools is helping a biotech company ask questions that help it with demand forecasting. For example, the company can run what-if scenarios on getting specific chemicals for its products and what might happen if certain global shocks or other events occur that change or disrupt daily operations. Today’s generative AI tools can even suggest several courses of action if things go awry. Risk management may be the most promising area, particularly in preparing for risks that supply chain planners haven’t considered.

Source

One leading US retailer built bots using generative AI to negotiate cost and purchasing terms with vendors in a shorter time frame, noting that this early effort has already reduced costs by bringing structure to complex tender processes. The technology presents the opportunity to do more with less, and when vendors were asked how the bot performed, over 65% preferred negotiating with it instead of with a human at the company. We have also seen instances where companies are using generative AI tools to negotiate against each other!

Beyond negotiations, generative AI presents an opportunity to improve supplier relationships and management, with recommendations on what to do next. These tools are useful to quickly extract information from large contracts and help you better prepare for renewal discussions, for example.

Make

Generative AI in supply chain presents the opportunity to accelerate from design to commercialization much faster, even with new materials. Companies are training models on their own data sets, and then asking AI to find ways to improve productivity and efficiency. Predictive maintenance is another area where generative AI can help determine the specific machines or lines that are most likely to fail in the next few hours or days. This can help improve the overall equipment effectiveness (OEE) — one of the most important manufacturing metrics.

For instance, one leading industrial manufacturing company in Europe partnered with a tech leader to use generative AI for factory automation and product lifecycle management, shortening the product development lifecycle and boosting efficiency with automated inspection processes.

Move

How is generative AI used in logistics? Here is an example: One of the biggest logistics companies in the US is using a proprietary AI platform to optimize picking routes within its warehouses, boosting workforce productivity by about 30% while slashing operational costs through optimized space and materials handling. While this is not a new use for AI, the generative component offers added dimensions of customization — say, optimizing based on less fuel, or to prioritizing certain deliveries or considering many other factors in a user-friendly application. Chatting with its customized tool helped the company understand if its trade network was optimized, and it even offered suggestions for improvement.

Get started today

While generative AI is a powerful tool with certain limitations, it is not a strategy. Focus on the business value and define a roadmap to shape and impact the organization, guided by three steps:

1. Focus on domain-wide transformation: Pinpoint high-impact use cases, envisioning a cohesive ecosystem that synergizes with traditional business models and unlocks possibilities.
2. Coordinate organization collaboration: Discuss the implications and identify the required skills across functions, going beyond technical roles.
3. Keep an open mind — and guard against the risks: Implement proof-of-concept pilot initiatives to learn more, drive quick wins and strive for scalable adoption.