In supply chain planning, the problem is often not lack of data. It is the time required to turn data into decisions. In today’s context of permanent uncertainty, classic planning cycles can move so slowly that decisions are already outdated by the time they are taken.  

Join this 30-minute webinar, followed by a 15-minute live Q&A. You will see how Sunstice supply chain planning solutions, powered by its agentic AI layer Helios™, reduce analysis effort, accelerate decision cycles, and improve the quality and accuracy of decision-making in supply chain planning.  

Through concrete use cases and a live demo, we will show how planning teams can investigate issues faster, understand trade-offs more clearly, align stakeholders more efficiently, and move from insight to action with greater speed and control.

The session will be led by Sunstice experts Gilles Lefebvre, Chief Product and R&D Officer, and Skander Beji, Pre Sales Consultant.  

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Who should attend

Supply Chain Planning leaders looking to accelerate decision-making and improve planning performance under permanent uncertainty.

S&OP, Demand Planning, and Supply Planning teams seeking practical ways to reduce analysis effort and move faster from issue detection to action.

Planning transformation, AI, and IT teams responsible for scaling governed AI, improving decision workflows, and connecting planning intelligence across systems.

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What you’ll learn

You will see how Sunstice Helios™ helps planning teams:

• Reduce the time spent investigating disruptions, exceptions, and performance gaps

• Understand root causes and evaluate trade-offs across service, cost, capacity, and inventory

• Use pre-built expert agents and configurable agents to support standard and company-specific planning decisions

• Move from sequential planning cycles to faster, more continuous decision-making

Built on the Sunstice Structured Agility® operating framework, Helios™ helps planning teams move from insight to action faster while keeping human judgment and governance at the center.