Enhancing supply chain efficiency in the retail and consumer goods industry with agentic systems

The supply chain challenge continues

Retailers and consumer goods companies have faced constant change, particularly in supply chains. New sales and distribution models, such as online sales, omnichannel approaches, direct-to-consumer sales, and complex ecosystems, have evolved. External disruptions are frequent, with 90% of leaders reporting supply chain challenges in 2024¹.

Supply chain agility and resiliency rely on fast and accurate decision making. Poor decisions or slow responses lead to missed promises, negatively impacting revenue and customer satisfaction, and increasing costs due to inefficient shipments and higher inventory levels.

To address these challenges, there is an urgent need to improve both the quality and speed of decision making in supply chain management.

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Enter agents and agentic systems

Agentic systems offer a revolutionary opportunity to enhance decision making quality and speed. Triggered by business events, agents collect and analyze relevant data to either act directly or recommend actions.

Microsoft announced the ability to build autonomous agents using Microsoft Copilot Studio during Microsoft Ignite in October 2024. In a supply chain context, this capability could, for example, allow for the identification and action upon alternative supply sources in the event of a delayed shipment, with minimal human intervention.

Overview of agentic systems

In the context of agentic systems, an agent refers to a system capable of autonomous decision making and action. These systems can pursue goals independently without direct human intervention. Agentic systems have the following characteristics:

Autonomy. They operate independently, making decisions and executing tasks without human oversight, escalating to a human when necessary.
Context aware. They interpret data and adjust actions accordingly.
Goal orientation. They can aim to achieve specific objectives.
Learning. They enhance their performance by using new data and past outcomes.
Reasoning and decision making. Agents use reasoning to process information, infer relationships, and make decisions.
Perception and sensing. Agents perceive their environment through sensors or other means, which allows them to be triggered by changes in the process.
Skills and capabilities. Agents possess specific skills or capabilities to perform tasks. These skills can be learned or programmed.
Memory. An agent’s memory stores relevant information for decision making and future actions.

Agents can be programmed to pursue specific objectives once activated. For instance, when searching for an alternative supply source, they can prioritize cost minimization rather than selecting the first available option.

Agents are already delivering value for customers—for example, one customer has autonomous agents reviewing shipping invoices with more use cases planned. Over time, agents can be developed for various tasks across the organization, with Microsoft Copilot serving as the ‘UI for AI’.

Have we heard this before?

This may sound like RPA (Robotic Process Automation). You might also question how an agent differs from a copilot.

RPA employs rules-based automation, while agents enhance this capability by reasoning over data and using large language models (LLMs) to extract relevant information from extensive datasets. Whereas an RPA-based solution is rigid in terms of the scenarios that it can address and requires programming to make changes, an agent-based process automation solution can learn and improve over time, resulting in more effective outcomes.

Agents operate autonomously, unlike copilots who assist users in real-time. An agent can work within Copilot, aligning with the Microsoft vision of Copilot as the UI for AI. In the future, users will have one copilot but multiple agents including many working autonomously behind the scenes.

How agents can operate in the retail and consumer goods (RCG) supply chain

Agents can be widely applied across the RCG supply chain to automate repetitive tasks, analyze vast amounts of data for insights, and improve supply chain management. An ideal use case involves tasks that are human-intensive, repetitive, and require real-time decision making, where AI can significantly boost efficiency and accuracy. The criteria for an ideal use case includes high data availability, clearly defined achievable outcomes, and the potential for measurable improvements in revenue and cost savings.

AI agents can play a crucial role in retail store performance and inventory management practices. An agent can autonomously monitor performance data to alert the store manager when store performance metrics fall below a defined threshold. By comparing performance across similar stores, the agent can identify areas for improvement and recommend actions to improve store performance.

Agents can help to avoid stockout and overstock situations at retail locations. By analyzing data from various sources (such as sales, inventory, promotions, and external events), an agent can identify when a sales spike is misaligned with the forecast, leading to a potential shortage, and alert the supply chain team. The agent recommends a replenishment order which it can automatically generate to help ensure optimal stock levels, lower carrying costs, and reduce the likelihood of stockouts or surplus inventory.

Mitigating challenges with agentic AI

Disruptions across the supply chain often lead to product shortages and low case fill rate (CFR), leading to the complex daily task of allocating inventory across your customers. An agent can analyze customer orders, current inventory levels, and product substitution options to identify potential CFR situations. The agent allocates inventory by prioritizing orders based on predefined criteria such as customer loyalty, customer segmentation, order value, SLA fines, and urgency.

One of the biggest challenges facing RCG companies in 2025 is assessing the impact of tariffs. AI agents can evaluate and recommend alternative suppliers from different regions to mitigate the risk of high tariffs. This diversification strategy helps in maintaining a steady supply of materials while minimizing costs. By continuously monitoring tariff regulations and market conditions, an AI agent can suggest cost-saving measures such as bulk purchasing before tariff hikes or shifting production to countries with lower tariffs. An agent can assist in negotiating better terms with suppliers by analyzing market conditions and historical pricing data. This helps to ensure that companies get the best possible deals despite tariff fluctuations.

What’s next?

Consider the significant amount of time and effort that it takes today to answer the question: “How can I optimize my supply chain to boost sales by 10%?”.

Although this might feel like a supply chain question, it involves finance, sales, marketing, and possibly manufacturing. It’s such a complex question that answering it is likely to need days or weeks of analysis.

Today, agents integrated into Copilot enable users to ask specific questions in defined areas. This capability will expand in scope and complexity over time, eventually leading to a comprehensive redesign of business applications.

Project Sophia envisions agents, copilot, and business applications converging into an infinite research canvas.

Designed with an AI first approach, Project Sophia lets you ask business questions by analyzing data from various disparate systems and inputs. The AI guides you to view different perspectives, helping you understand and act on insights holistically.

Project Sophia reimagines the user experience, supporting each job function to address questions from their perspective while integrating strategic and tactical approaches.

Getting started with agentic systems

Increasing AI’s potential to scale value chain optimization in retail, consumer goods

Agentic AI lends itself well to navigating the complexity of routes to market—integrating manufacturing and sales strategies, selling through multiple channels or direct to consumer, managing multiple product lines and businesses, and integrating marketing and sales efforts globally.

Agentic AI is an integral tool that gives LLMs agency, with the ability to act autonomously. Whereas LLMs have previously been used to perform tasks including generating text and summarizing documents, they have not been able to act on their recommendations. Agentic AI on the other hand, is designed to drive goal-based optimizations and can dynamically adapt and execute goals with high predictability and minimal human oversight. Together, advancements in generative AI and agentic AI will redefine strategic value and productivity derived from technology, incorporating more advanced decision making processes with greater accuracy and speed.

Identify business problems and scenarios for more strategic engagement

As you consider how to use AI agents in a strategic manner, it is vital to frame applications of agentic AI in the larger context of identifying line of business processes that lend themselves to automation: optimizing time-consuming and mundane tasks/scenarios; establishing user trust in the agent’s capabilities and establishing clear operational guardrails for agentic AI including data governance, privacy, security; and instilling confidence in the agent’s value delivery, extending collaborative work management beyond task tracking to planning and execution functions.

The integration of agentic AI and generative AI into business applications signifies a monumental shift in how organizations can approach problem solving, strategic planning, and operational efficiency. By using advanced AI capabilities, businesses can anticipate a future where decision making is not only faster and more accurate, but also more insightful and holistic. This convergence of technology paves the way for innovative solutions and unprecedented levels of productivity, firmly with AI at the core of tomorrow’s business landscape.