Generative AI in Logistics: use cases and benefits
- October 21, 2024
- 6 mins
[REPORT]
Artificial Intelligence in Logistics: 6 applications
Generative Artificial Intelligence (Generative AI or GenAI) represents a major technological breakthrough for the logistics and supply chain sector. According to a recent Gartner survey, nearly two-thirds of supply chain managers plan to deploy this disruptive technology within the next 12 months (50%) or have already implemented it within their organizations (14%). Its disruptive potential opens vast opportunities to rethink and deeply optimize logistics processes.
In this article, we explore in detail the multiple concrete use cases of Generative AI in the logistics industry. We will see how this disruptive technology enables companies to differentiate themselves by offering a truly intelligent, flexible, and data-driven supply chain to meet the challenges of an ever-evolving world.
Classic AI vs. Generative AI: What are the differences?
Classic AI: Solving specific tasks
Classical Artificial Intelligence relies on algorithms and mathematical models manually programmed to replicate certain human cognitive abilities. It primarily uses rule-based techniques defined by experts, without significant autonomous learning capabilities. Classical AI is thus effective for solving specific tasks such as image or text recognition, translation, anomaly detection, and more. However, it remains limited in its ability to adapt to complex problems.
In the logistics industry, classical AI has automated low-value repetitive tasks like sorting, storage, and inventory management through systems using computer vision, voice recognition, or robotics. It has also played a crucial role in optimizing high-value complex processes such as production planning, supply chain management, and delivery route optimization. By analyzing vast amounts of data, classical AI provides recommendations to support human decision-making, thereby enhancing operational efficiency.
Discover our article dedicated to AI use cases in logistics.
However, machine learning and deep learning techniques have gradually supplanted traditional AI by enabling systems to learn and improve autonomously from data, surpassing the limitations of rule-based approaches.
GenAI: Creating new data
Unlike traditional AI, which is limited to predefined tasks, Generative AI (GenAI) possesses the unique capability to autonomously create entirely new content such as texts, images, or models. Based on deep learning models trained on vast amounts of data, GenAI can detect and replicate underlying patterns, structures, and correlations to generate realistic and relevant content.
Leveraging these self-learning capabilities, GenAI offers a broad range of advanced skills. It can efficiently analyze and classify all types of structured and unstructured data, rapidly model and optimize operational strategies using real-time data, generate automated multi-format decision-making content (texts, visualizations, etc.), synthesize key insights from large volumes of complex data, and provide instant responses to queries.
Use cases of Generative AI in Logistics
Demand Forecasting
Many organizations are now leveraging the power of Generative Artificial Intelligence to optimize their demand management. By ingesting and analyzing large volumes of historical sales data, market trends, consumer behaviors, and other key variables, these AI systems can model future customer demand with high precision and in real-time.
Production Planning
Risk Anticipation
Generative AI systems now provide businesses with an unprecedented tool to enhance the resilience of their supply chains. By ingesting and cross-referencing massive datasets—such as historical activity records, market conditions, weather events, and geopolitical context—these systems can proactively identify and model potential risks.
Beyond predictive analysis, Generative AI offers decisive added value. Upon request from planners, it can generate detailed risk assessments, simulate various crisis scenarios, and propose tailored risk mitigation strategies. With dynamic simulations and AI-generated recommendations, planners can implement adjusted contingency plans and effective countermeasures to ensure operational continuity.
Supplier and Subcontractor Selection
Generative Artificial Intelligence now provides businesses with a powerful tool to streamline and enhance their supplier selection processes. By ingesting and cross-referencing massive volumes of data—such as performance history, technical expertise, cost structures, risk profiles, and more—these next-generation AI systems can generate highly refined decision analyses.
💡 Walmart has experimented with an innovative use of Generative AI for negotiations with its suppliers. An AI chatbot conducted negotiations with 89 equipment suppliers, such as cart providers, successfully reaching agreements with 64% of them. These agreements resulted in an average of 1.5% cost savings and an additional 35 days of payment terms. Furthermore, 83% of the suppliers appreciated this automated negotiation experience. This demonstrates that Generative AI can not only secure better commercial terms but also provide a positive experience for partners.
Product Design
In product design, Generative AI can rapidly generate and evaluate hundreds of alternative designs based on predefined criteria (functionality, aesthetics, durability, etc.). Whether it’s designing new mechanical parts, more efficient consumer products, or attractive packaging, Generative AI significantly accelerates the innovation process by exploring a vast design space.
By processing massive datasets on the properties of existing materials and exploring various combinations, Generative AI can propose new materials with optimized characteristics (efficiency, durability, resistance, etc.) or suggest improvements to current materials.
💡 Fashion designer Stella McCartney, known for her eco-friendly values, has embraced Generative AI to push the boundaries of sustainable fashion. As a pioneer in this field, her brand leverages this cutting-edge technology to design innovative, environmentally friendly materials and fabrics.
Customer Experience Personalization
Generative AI opens up new possibilities for creating personalized content at scale, tailored to each customer’s unique preferences and context. For example, in online shopping, it can generate product descriptions, visuals, or even personalized video demos based on each visitor’s interests and profile, significantly enhancing engagement and conversion rates.
In call centers, Generative AI assists agents by generating personalized responses to customer inquiries based on their interaction history and specific context.
Moreover, Generative AI enables the creation of immersive and unique brand experiences, such as virtual avatars capable of interacting naturally and personally with customers.
Beyond marketing and customer service, Generative AI also revolutionizes product co-creation and personalization. Customers can now design custom-made products (clothing, accessories, etc.), which Generative AI then translates into 3D models ready for manufacturing.
💡 Zalando, Europe’s leading online fashion retailer, has deployed a revolutionary virtual assistant powered by OpenAI’s Generative AI, ChatGPT. This assistant helps customers navigate the website and mobile app more intuitively, offers personalized fashion advice, answers frequently asked questions, and much more.
Predictive Equipment Maintenance
Generative AI enables the creation of highly effective predictive maintenance models by analyzing vast amounts of data from machine sensors and monitoring systems. By ingesting these complex temporal data sets (vibrations, temperatures, pressures, etc.), Generative AI can detect changes and accurately model the normal and abnormal behavior of equipment.
With these predictive models, it becomes possible to anticipate potential failures long before they occur by identifying early signs of degradation. Generative AI can then generate personalized recommendations on the optimal time to schedule maintenance interventions based on the actual condition of each machine.
This predictive approach allows for optimized maintenance planning by intervening only when truly necessary, rather than following a fixed schedule based on statistical averages. This significantly reduces unexpected downtime and costs associated with breakdowns while maximizing the lifespan of the equipment.
Global Trade Optimization
For companies engaged in international trade, Generative Artificial Intelligence helps navigate the complexities of global networks while ensuring compliance and cost control through:
- Historical and current trade data analysis: By analyzing historical and current trade data (volumes, costs, timelines), Generative AI models existing trade flows and identifies bottlenecks.
- Regulatory and logistical data integration: Ingesting regulatory, tariff, and logistical data from multiple sources allows for a unified representation of global trade constraints.
- Scenario simulations: Generating “what-if” scenarios evaluates the impact of regulatory changes, new trade agreements, alternative sourcing strategies, and more.
- Optimized import/export strategies: Recommending cost-effective, timely, and compliant import/export strategies by considering all relevant factors.
- Efficient trade routes and transport modes: Identifying the most efficient and cost-effective trade routes and transport modes based on specific constraints of each flow.
Logistics Network Design
Generative AI enables the exploration of numerous potential configurations for logistics networks, including warehouse locations, hubs, distribution centers, and transportation schemes. By rapidly generating and evaluating alternative scenarios, Generative AI can model various network configurations using massive data inputs such as customer demand patterns (volumes, locations, seasonality), delivery time constraints, transportation and operational costs, site capacities, and more.
For each scenario, the AI evaluates expected performance in terms of total costs (transportation, real estate, labor), service levels (delivery times, service rates), and environmental impacts (CO2 emissions from transportation). It then compares these scenarios to identify the optimal logistics network configurations based on prioritized performance criteria (costs, customer service, carbon footprint).
Generative AI provides logistics experts with a powerful tool to design resilient, agile, and sustainable logistics networks by exploring a wide array of possible solutions quickly and objectively. This approach helps businesses understand the strengths, weaknesses, and trade-offs of each option, enabling them to make well-informed decisions.
Order Preparation
Generative AI optimizes order preparation processes in logistics warehouses for efficient operations. By analyzing real-time data such as order volumes, product locations, and warehouse traffic patterns, it dynamically adjusts operator routes to collect items in the most optimal sequence. This significantly reduces travel time and increases overall productivity.
💡 Amazon leverages Generative AI to create “synthetic data” that simulates various scenarios its robots might encounter during peak times like Cyber Monday. This AI-generated synthetic data trains the robots’ learning systems to better recognize, sort, and inspect millions of items for shipping, optimizing Amazon’s logistics operations during peak seasons.
Real-Time Tracking and Visibility
Generative AI enhances real-time tracking and visibility of shipments by providing precise, up-to-date information on their location and status. By ingesting tracking data streams from multiple sources, it synthesizes these complex datasets into clear and easily interpretable visual representations. These include dynamic maps showing live positions, detailed timelines tracing the complete journey, and status indicators highlighting any delays or issues.
Dynamic Pricing
Generative AI can analyze large volumes of historical and real-time data on transportation costs (fuel, labor, etc.), available capacities, customer demand levels, market conditions, and more. It models and anticipates the evolution of these various factors. Based on these predictive models, GenAI can generate simulations and “what-if” scenarios to assess the impact of different dynamic pricing strategies on costs and revenues.
Generative AI can recommend and create optimized dynamic pricing structures, taking into account multiple constraints and complex business objectives. This enables companies to implement competitive, data-driven dynamic pricing strategies that are tailored to market fluctuations.
💡 During the 2022 peak season, FedEx leveraged dynamic pricing for its home delivery services, resulting in a $150 million profit. Surcharges were adjusted weekly based on the shipping volumes from customers compared to the start of the year.
Dynamic Route Optimization
The last-mile delivery represents a significant operational and financial challenge for logistics players, accounting for up to 50% of the total delivery costs. Generative Artificial Intelligence (GenAI) enables dynamic and continuous optimization of delivery routes to address this crucial challenge.
By integrating and analyzing a continuous stream of real-time data on traffic conditions, weather, delivery priorities, and other variables, GenAI proactively recalculates and updates optimal routes. This dynamic adaptation of routes maximizes operational efficiency on the field, reduces fuel consumption and CO2 emissions, and ultimately improves customer satisfaction with a more reliable and responsive service.
Dynamic optimization offers great flexibility to adapt to unforeseen circumstances, even after the vehicles have departed. For example, if an incident disrupts the initial route, the system can immediately recalculate an alternative path to avoid it. Similarly, if a new priority order is added during the route, GenAI determines the optimal way to integrate it into the schedule without causing delays or unnecessary detours.
Thanks to this continuous adjustment capability, delivery operators can effectively manage unforeseen events while maximizing the productivity of their routes and providing high-quality service to end customers.
💡 Kardinal offers a next-generation route optimization solution powered by AI. Our innovative technology continuously adapts routes in real-time based on changing conditions, ensuring more efficient and realistic deliveries.
Continuous route optimization solution
Generative Artificial Intelligence represents a true technological disruption for the supply chain industry. As demonstrated through these numerous concrete use cases, GenAI provides companies with new tools to deeply rethink their operational processes and enhance their agility, productivity, and resilience in the face of an ever-evolving world.
Whether it’s demand forecasting, production planning, risk management, supplier selection, or product design, Generative AI offers augmented intelligence that dynamically optimizes operational strategies. Its unique ability to generate new and relevant content—be it analytical visualizations, simulations, or tailored recommendations—greatly facilitates decision-making.
