How Agentic AI Elevates Service Supply Chain Planning

An AI agent can interpret a request, investigate an issue, and coordinate a workflow. But when the task is deciding which service parts to stock across a global network, there is another requirement: a credible way to predict how those decisions will affect equipment availability, inventory investment, and customer service.

That was a central theme in a recent discussion I hosted with Steven Caldwell, Servigistics General Manager; Dr. Vipul Agrawal, Vice President of Product Management; and Vivek Dube, Vice President of Research and Development. Their perspectives point to a practical principle for service leaders evaluating AI: examine the decision science beneath the agent.

Service supply chains demand a different approach

Manufacturing planning often aligns material availability with a production schedule. Service parts planning must also prepare for needs whose timing and location are uncertain. A part may be needed infrequently, yet its absence can keep an aircraft grounded, a medical system unavailable, or a piece of industrial equipment out of service.

Caldwell emphasized that the objective extends beyond moving inventory efficiently. It includes equipment uptime, asset availability, and readiness. The network must support those outcomes while making effective use of the money invested in parts.

The problem becomes more difficult when parts move through repair and return loops, multiple sources can satisfy a requirement, and inventory is distributed across several levels of a global network. A stocking decision at one location affects what other locations can deliver. Evaluating each part and location independently can miss those relationships.

A predictive digital twin connects decisions to consequences

Dube described the importance of representing the service supply chain accurately in software. That representation must include its structure, circular flows, business priorities, and uncertainty. It must also support forecasting, optimization, and planning at the scale and speed the business requires.

Agrawal explained why a predictive digital twin is central to this approach. A view of current inventory tells a business what it has today. A predictive model helps estimate how that inventory will perform under future conditions—and how a different decision could change the outcome.

Multi-echelon optimization uses those relationships to evaluate inventory across the connected network. The question becomes how to position parts across locations to meet service objectives while balancing investment and risk. Order planning then helps determine how to move toward those targets through procurement, repair, and replenishment decisions.

This foundation also supports strategic analysis. If a company expands into a new region, changes a service commitment, or faces longer supply lead times, it can evaluate scenarios before committing resources. The model gives leaders a basis for comparing consequences.

Give language models the right tools

Large language models bring valuable capabilities to this environment. They can interpret natural language, summarize information, explain results, and help coordinate tasks. Those capabilities make sophisticated planning tools easier to access and use.

The leaders emphasized that the optimization calculations still require dedicated tools. Access to enterprise records, by itself, does not provide a language model with a validated model of uncertainty, network dependencies, or the tradeoffs between inventory and service.

An agent can help translate a business request into a scenario, invoke the appropriate planning tools, and present their results. For example, a leader might ask how to reduce procurement spending while protecting priority service commitments. The agent can help organize the analysis; the forecasting and optimization engines evaluate the consequences of the available choices.

That division of responsibilities is essential. Language capabilities help people express intent and understand results. Purpose-built decision science supplies the quantitative foundation for evaluating the plan.

Evaluate the foundation behind the experience

For service leaders, a compelling demonstration should open a deeper conversation. How does the system represent the service network? How does it account for uncertainty and repair flows? Which tools calculate the recommendations? How are results tested, explained, and connected to measurable business outcomes?

Caldwell brought the discussion back to the relationship between inventory investment and inventory performance. Businesses can achieve improvements through lower inventory, better service, or a combination of both. They can also use scenario analysis to make better strategic decisions.

Agentic AI creates new ways to access, coordinate, and act on that intelligence. Its value grows when the underlying models can evaluate the decisions that matter to the business. For service supply chains, that makes the optimization foundation a central part of the AI strategy.

Watch the full conversation with Servigistics leaders Steven Caldwell, Dr. Vipul Agrawal, and Vivek Dube to explore how optimization science and Agentic AI are shaping the future of service supply chain planning.