Agentic Supply Chain Orchestration Lead

We are seeking a highly experienced and innovative leader to design and implement an AI-driven agentic orchestration layer across the end-to-end supply chain planning ecosystem. This role will focus on improving forecast accuracy, production planning alignment, and supplier material release quality by continuously reconciling data flows across multiple applications.

The role will leverage autonomous and multi-agent AI systems to validate master data, detect discrepancies between upstream demand signals and downstream execution outputs, and proactively identify infeasibility across planning processes.

Job Responsibilities but not limited to:

• Design and implement an agentic orchestration framework to manage end-to-end supply chain planning and reconciliation processes.
• Develop automated master data validation capabilities to ensure consistency of BOMs, lead times, lot sizes, and supplier mappings across systems.
• Continuously reconcile demand, production, and material release signals across multiple applications to detect misalignments.
• Identify and decompose discrepancies into root causes including data issues, rule transformations, timing gaps, and constraints.
• Detect and explain infeasible plans early by evaluating production capacity, supplier constraints, and inventory limitations.
• Build and operate an AI-driven Supply Chain Control Tower for real-time visibility, alerts, and exception management.
• Enable AI-powered root cause analysis and decision support to accelerate resolution of planning inconsistencies.
• Integrate data and standardize planning logic across ERP, APS, and supplier systems to establish a single version of truth.
• Establish governance, monitoring, and continuous improvement mechanisms for agent-driven workflows, including audits and human-in-the-loop controls.

Basic Qualifications:

• Bachelor's degree in Supply Chain, Engineering, Data Science, Operations, or related field
• Minimum 8 years of experience in Supply Chain Planning (Demand, Supply, Production), Forecasting and MRP/MPS processes
• Strong understanding of: Forecast accuracy metrics (MAPE, Bias), Production planning and supplier scheduling
• Experience working with APS/Planning tools (Kinaxis, Blue Yonder, o9, etc.)
• Strong analytical and data skills (Excel, SQL; Python preferred)
• Experience in cross-functional environments involving Planning, IT, and Data team

Preferred Qualifications:

• 12 years of experience in Supply Chain Planning (Demand, Supply, Production), Forecasting and MRP/MPS processes
• Experience implementing or design:




• AI/ML-based supply chain solutions
• Agentic or workflow orchestration frameworks
• Digital Control Tower platforms




• Familiarity with:




• Microsoft Copilot Studio, Azure AI, or similar agent orchestration tools
• Data platforms such as Palantir, Snowflake or Databricks




• Experience in automotive or complex manufacturing environments

We are seeking a highly experienced and innovative leader to design and implement an AI-driven agentic orchestration layer across the end-to-end supply chain planning ecosystem. This role will focus on improving forecast accuracy, production planning alignment, and supplier material release quality by continuously reconciling data flows across multiple applications.

The role will leverage autonomous and multi-agent AI systems to validate master data, detect discrepancies between upstream demand signals and downstream execution outputs, and proactively identify infeasibility across planning processes.

Job Responsibilities but not limited to:

• Design and implement an agentic orchestration framework to manage end-to-end supply chain planning and reconciliation processes.
• Develop automated master data validation capabilities to ensure consistency of BOMs, lead times, lot sizes, and supplier mappings across systems.
• Continuously reconcile demand, production, and material release signals across multiple applications to detect misalignments.
• Identify and decompose discrepancies into root causes including data issues, rule transformations, timing gaps, and constraints.
• Detect and explain infeasible plans early by evaluating production capacity, supplier constraints, and inventory limitations.
• Build and operate an AI-driven Supply Chain Control Tower for real-time visibility, alerts, and exception management.
• Enable AI-powered root cause analysis and decision support to accelerate resolution of planning inconsistencies.
• Integrate data and standardize planning logic across ERP, APS, and supplier systems to establish a single version of truth.
• Establish governance, monitoring, and continuous improvement mechanisms for agent-driven workflows, including audits and human-in-the-loop controls.