Resilient and flexible supply chains (SCs) are essential for organizations to thrive in today’s rapidly changing global economy. Traditional SCs often suffer from a lack of real-time data on inventory levels, shipment conditions, and customer preferences. This limited visibility can result in inventory shortages, delayed deliveries, and challenges for the workforce in responding to unexpected disruptions. Amidst all this uncertainty, executives face growing pressure to address environmental, social, and governance (ESG) concerns to ensure operations are conducted ethically and sustainably. These challenges have recently sparked increased discussions around digitalization and digital transformation that optimize planning, sourcing, manufacturing, and transportation to improve operational visibility while developing resilience. Within this notion, artificial intelligence (AI) enables learning and evolution from large datasets and establishes new benchmarks for SC efficiency. Generative artificial intelligence (Gen AI), a subset of AI, creates content – such as text, images, videos, and code – based on user inputs, adding the potential to strengthen the overall SC structure. The following highlights the capabilities of Gen AI:

It can generate synthetic data that mirrors real-world scenarios without containing confidential information. This augmented data can significantly improve the robustness and adaptability of anomaly detection models, thereby enhancing their performance in detecting real-world variations.
It simulates SC disruption scenarios by assessing potential risks, identifying vulnerabilities, and developing contingency plans. It plays a key role in minimizing the impact of disruptions on the supply chain.
It is trained by images, videos, and sensor data collected to analyze warehouse inventory levels or transportation procedures to identify potential discrepancies. This opportunity allows companies to monitor deviations and trigger alerts proactively.

It creates reports and data visualizations that provide actionable insights to improve communication and collaboration between SC stakeholders.
It designs chatbots/ virtual assistants that can be integrated into SC management systems to answer questions, provide real-time data updates, and automate routine tasks to improve overall efficiency.

According to Gartner 2024 Hype Cycle [1], although the expectations of Gen AI in logistics have begun to attract more attention, adoption will take more than a decade. The volume and quality of data gathered throughout the chain are critical inputs for AI modeling. According to IDC’s survey [2], 40% of SC companies invest in GenAI to leverage it for warehouse resource planning, workforce strategizing, logistics solutions, multi-enterprise connectivity, and process improvements. For example, Target has recently started using a Gen AI-powered chatbot to assist employees across its 2,000 stores that can answer on-the-job process questions, coach new team members, support store operations management, and more. GenAI also enhances Target’s digital consumer experience by improving product search capabilities to get the most relevant results. Additionally, it summarizes product reviews in conversational language. Similarly, a U.S. telecom company, Verizon has launched a Gen-AI backed initiative to stop 100,000 customers from leaving its service this year by predicting why a customer is calling, connecting them with a suitable agent, and reducing the store visit time. Amazon leverages GenAI in many ways, such as improving product listing to help sellers specify detailed information with less work, creating more engaging advertisements by supporting advertisers, developing a new product (Amazon One) to enable customers to use their palms to make payments, generating review highlights to see what other customers say about the products, developing its technologies to extend the omnichannel capabilities, reducing time and effort required to deliver personalized customer experiences.

Integrating Gen AI into SC operations is a pivotal advancement that necessitates increased attention to data privacy, fairness, workforce design, and ethical/legal risks. Successfully harnessing the power of AI requires a collaborative effort. Thus, establishing clear guidelines and regulations is essential to foster trust and ensure responsible use in business.

[1] Hype Cycle for Supply Chain Execution Technologies, 2024

[2] International Data Corporation’s Future Enterprise Resiliency & Spending Survey (Wave 2, 2023)