Reimagining Supply Chain Operations with Agentic AI

In a fast-growing world and competitive market, supply chain operations in retail and CPG organizations often face challenges such as achieving higher demand forecast accuracy, end-to-end visibility of inventory, efficient warehouse operations, optimizing routes and last-mile delivery, and addressing sustainability goals.

Retailers and CPG organizations require innovative solutions to address these challenges while achieving their supply chain goals cost-effectively. Traditional AI has been utilized in supply chains for prediction and process optimization; however, the inherent limitations of these AI solutions do not enable the resolution of these challenges at scale, speed, and cost-effectively. There is always a constant and acute need for human intervention to monitor, decide, and coordinate.

How can Agentic AI Transform Supply Chain Operations?

Agentic AI enables the reimagination of supply chain operations by creating a thoroughly unified, autonomous, resilient, faster, and more cost-effective system. The core attributes of Agentic AI can help transform supply chain planning and operations in the following ways:

1. Context Understanding: Agents can make sense and understand the complex dynamics of supply chain operations through a unified intelligence via integrations to various internal enterprise sources such as ERP, price optimization, demand forecasting system, warehouse management system, order management system, transport management system and external supply chain data sources such as supplier applications, track & trace integration with carriers, customer data from social media, market and demographic data from third party providers and weather data from open APIs.
2. Decision Making: Agents can make informed supply chain decisions based on a deeper understanding of the context, supply chain goals, and performance. Agentic decision-making is not limited to a specific function within the supply chain, but rather a holistic decision that is optimized for outcomes throughout the supply value chain. An agentic AI can make a sequence of orchestrated holistic decisions by connecting the dots from base price changes and their impact on demand forecast, auto supplier selection, and order placement based on their ability to accommodate demand changes, review and change inventory replenishments to stores, recalibrate routes, and reschedule DC and store inbound and outbound shipments.
3. Scenario Simulation: Agentic AI can support autonomous 'what-if' simulations of various supply chain scenarios, leveraging digital twins, to make the best possible, optimized decisions that meet supply chain goals. Agentic AI, together with digital twins, facilitates autonomous strategic supply chain planning and operational decision-making support. An agentic AI solution can help with network design and strategic location planning of DCs, fulfilment hubs, and stores by holistically balancing the trade-off between business objectives of supply chain cost, service level, and wastage. The combined power of Agentic AI, together with Digital Twin, IoT, and RFID, enables proactive and predictive maintenance of the factory, supporting strategic planning of assets and fleet management.
4. Adaptability: Supply chain is prone to delays, disruptions, and risks from various internal and external factors, including spikes in order volume, system downtime, supplier issues, and adverse weather conditions. Intelligent process automation is limited to be rules based and traditional AI is limited to predictions but agentic can adapt to real-time intelligence, identify and leverage a new parameter real-time such as tariff hike, supplier disruption, weather, or a viral customer trend, and decide and act real-time, thus allowing a business to embrace disruption and sudden changes, allowing supply chain to be adaptive and resilient and not rigid and reactive.
5. Autonomous Execution: One of the biggest values of an Agentic-enabled supply chain operation is its ability to orchestrate across various supply chain nodes, systems, and data sources, enabling unified and seamless execution of the decisions made. Agentic AI Solution can deliver significant value to last-mile order fulfilment by predicting at-risk orders and suppliers, deciding on substitute products, alternate fulfilment options, and optimized routes, as well as seamless orchestration through integration with carriers and CRM systems for customer communications and order promise recovery.
6. Self-Learning: Supported by reinforcement and deep learning algorithms, Agents continuously learn the outcomes of the decisions through both machine and human feedback mechanisms. This allows for continuous training and refinement of various supply chain domain LLMs and SLMs. For example, an Agentic AI can better support a retailer and CPG business with supplier management and sustainability compliance. An agentic AI-enabled supplier management process facilitates zero-touch supplier onboarding, continuous monitoring of supplier contractual risk and performance, and optimization. Similarly, Agentic solutions facilitate more sustainable practices through product design, organic product substitutes, packaging design, and energy utilization, achieved through self-learning and continuous improvement.

Agentic AI is being increasingly experimented with and adopted at scale by leading retail and CPG brands. The largest e-commerce retailer has utilized Agentic AI for enhanced demand forecasting and supply chain optimization, leading to a substantial reduction in stockouts. A leading global mass merchant has leveraged Agentic AI to optimize inventory levels across its store network, resulting in reduced inventory costs. A leading logistics company has adopted an Agentic solution to autonomously optimize routes, considering port conditions, carrier capacity, and constraints, resulting in significant savings in transportation costs, improved service levels, and reduced carbon footprints.

Conclusion

Agentic AI is poised to transform supply chain operations in the retail and consumer goods sectors by transforming them from being rigid, reactive, manual, and costly to being adaptive, resilient, autonomous, and cost-effective. Human oversight and governance will be crucial in the early stages of adopting Agentic AI in the supply chain, as they are necessary to gain trust, facilitate agentic decisions and actions, and support agent training for improved and responsible supply chain performance.

By leveraging Agentic AI, businesses can significantly improve key performance indicators (KPIs) across various aspects of the supply chain, such as:

• Cost: Agentic AI optimizes inventory, logistics, labour, and transportation costs.
• Stock Availability: Agentic AI optimizes inventory levels, balancing access to inventory and preventing stockouts, to enhance the customer experience.
• Wastage: Accurate demand forecasting and inventory management reduce overstock and surplus inventories, thus minimizing wastage in both perishable and durable goods.
• Reducing Carbon Footprint: Agentic AI's ability to optimize transportation routes and logistics operations leads to lower fuel consumption and reduced carbon emissions.
• ESG (Environmental, Social, and Governance): Agentic AI monitors ESG metrics and ensures compliance, enabling businesses to meet their sustainability goals and adhere to ethical standards.
• Customer Experience: By improving service levels, stock availability, and reducing wastage, Agentic AI enhances the overall customer experience, ensuring that customers receive their products on time and in good condition.