Generative AI: A quantum leap in autonomous supply chains

In the last couple of years, Generative AI (Gen AI) has emerged as one of the most revolutionary business technologies in history, redefining how people consume and experience the world. In a recent study with HFS, 53% of supply chain and procurement executives stated that they are shifting funds from other resources to fund Gen AI initiatives. This highlights the urgency for companies to surpass the limitations of traditional AI systems to unlock supply chain operations that can learn, adapt, and make autonomous decisions.

But why are supply chain leaders so excited about it? Gen AI has the potential to revolutionize how supply chains operate within organizations, enabling a true network of networks approach. Gen AI can process large amounts of data to deliver novel insights in the form of text, audio, video, and synthetic data. By enabling supply chain teams to make better decisions faster, the technology can help increase service levels and enhance processes. And while it will take a few years to realize its full potential, supply chain teams can tap into the power of Gen AI today.

Let’s explore five ways Gen AI can help enhance supply chain transformation and resilience.

1. Resolving order holds and cuts quickly with streamlined operations

Gen AI plays a crucial role in automating order entry, extraction, validation, and transmission. This automation not only enhances efficiency, but also curbs unnecessary costs, contributing to a more robust bottom line. The powerful technology is also instrumental in managing order exceptions, providing tailored solutions for smooth processing. With Gen AI, customers receive notifications about item substitutions, order cuts, and other changes, improving transparency and trust. This proactive approach enhances the customer experience and reduces the need for customer service interventions.

The integration of Gen AI in self-service capabilities empowers customers to manage inquiries and submit orders independently. By reducing the cost of managing queries and improving the overall customer experience, supply chain leaders can rely on a more reliable order fulfillment process with fewer disruptions.

2. Bringing transparency to contract management

Managing a contract lifecycle can be a time-sensitive and arduous process. Drafting, reviewing, negotiating, and appreciating legal bindings to compliances all require a delicate balance of the vendor-client relationship. However, stakeholders and suppliers can use Gen AI to understand each other better, mitigate risk, and quickly assess real-time contractual implications.

For example, imagine signing a contract with a transportation carrier that includes fine print on lane deviation in local languages. Unless your routes are carefully planned and adhered to for every fulfillment, your transportation cost will see a tiny spike. An individual spike may seem insignificant but it can add up quickly across multiple suppliers and vendors. Time and money are lost checking and translating each invoice against contractual terms for every vendor. However, Gen AI can process all the contracts, understand them, and immediately flag which invoices are overcharging based on the agreed-upon terms. Beyond financial savings, the level of trust among suppliers improves when results are aligned to expectations. 

3. Cutting delivery time with dynamic routes and logistics optimization

Port strikes, natural calamities, seasonal spikes, and trade wars are some of the common challenges that threaten on-time, in-full deliveries. With logistics costs for the transportation and logistics market expected to reach $14.39 trillion in 2029, it’s important for companies to evaluate their routes and logistics strategies to allow for future growth. This is where gen AI could be a game-changer in combating disruptions.

Training Gen AI models on climate, traffic patterns, delivery schedule, modes of transport, and vehicle capacity data enables logistics team members to identify the correlation between various parameters to find the best route to fast-track deliveries. Taking the model further by mapping it to inventory levels helps to minimize missed deliveries due to stockouts. Teams can scale these Gen AI models and make them flexible to sense demand signals and reroute supplies while making environmentally friendly choices.

4. Highlighting and mitigating supply chain risks

Gen AI is transforming how businesses approach supply chain risk management today. It uses advanced algorithms and machine learning techniques to monitor news portals, social media, and other sources to identify upcoming risks. These risks could be political turmoil, natural disasters, or trade agreements that have gone wrong. Supply chain teams can map the location of these risks with their suppliers’ locations to identify the disruption hotspots and initiate stop-gap solutions to avoid stock-outs or missed deliveries.  

One of the biggest problems for supply chain leaders is understanding the severity of risk and evaluating the need to respond and invest resources. Often an incident can have a low impact probability and all you need is to wait it out for complete resolution. Decision-making becomes easier—and operations more resilient—with Gen AI recommending the best course of action.

5. Making environmental, social, and governance (ESG) reporting dynamic and personalized

From chief sustainability officers to analysts, generating an annual ESG report means grappling with data management challenges, especially when navigating multiple enterprise resource planning systems. Generating an accurate view of an organization’s ESG returns for its investors, customers, employees and other stakeholders is especially difficult due to the numerous frameworks and standards, each varying in indicators and approaches, that exist. With supply chain teams already operating at maximum capacity, Gen AI can ease the pain.

Gen AI can analyze large volumes of ESG data, recognize patterns and trends, and present the information in comprehensive reports. Models can be trained to comply with relevant frameworks and standards to help ensure reports align with set guidelines. Additionally, it can continually monitor and analyze ESG data, improving real-time reporting and updates. This helps stakeholders stay up to date with a company's sustainability performance on an ongoing basis, alleviating the need for teams to produce annual or periodic reports.

Gen AI can even customize ESG reports to meet the specific requirements of every stakeholder, providing the right level of detail for the topics of interest, and adjusting the language and tone to suit the audience's level of expertise.

Gen AI opens doors to significant possibilities but tread cautiously

While Gen AI is a revolutionary tool in the right hands it should not act as the final source of truth. A Gen AI model is not concerned with what is true but what it believes is the most likely response to a question. Since models are trained on vast amounts of data both internal and external, including copyrighted material, there’s a risk that generated content may inadvertently infringe on intellectual property rights. Organizations need to be aware of these risks, as well as other concerns including unintentional biases and hallucinations.

Organizations must design a well-governed Gen AI framework that accepts well-crafted prompts, understands response edge cases, and aligns data structures to accept expected responses. Without these steps, the results from the large language models may not be in a contextual, usable format. For those who can get these housekeeping items in order—not to mention the underlying need to continually upskill talent—Gen AI can catapult supply chain operations to an entirely new level.

About the authors:

Peter Anderson is global supply chain lead at Genpact. Gaurav Goel is global supply chain planning leader at Genpact. Genpact offers AI solutions to business.

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