Manager, Supply Chain Planning Intelligence

Our vision is to transform how the world uses information to enrich life for all .Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

We are seeking a Manager with 10+ years of experience to lead the Supply Chain Planning Intelligence team within our Enterprise Planning Solutions (EPS) organization, part of Micron’s IT Enterprise AI function. This is not a traditional supply chain applications role—it is a planning transformation leadership  this position, you will lead a hybrid team of Industry Analysts, Data Scientists, and Solution Architects to build and scale the intelligence layer across Micron’s supply chain planning ecosystem.

Your team will focus on making planning smarter, faster, and increasingly autonomous, independent of the underlying tools or platforms.

Responsibilities:

AI-Led Planning Transformation (Primary Focus):
Define and own the Planning Intelligence roadmap — what decisions Applications Teams should be automating, augmenting, or accelerating with AI in the next 12–24 months. Lead adoption of Generative AI, agentic workflows, and evolving AI tools across supply chain planning domains. Identify and scale high-impact use cases — exception-based planning, AI scenario generation, natural-language planning insights, decision journaling. Drive end-to-end AI lifecycle from problem framing through PoC to production deployment.

Ensure AI capabilities improve actual planning outcomes — fill rates, decision latency, manual effort reduction — not just deployment counts. Define the architecture for a unified Planning Intelligence layer that works with or without specific planning tools underneath.

EPS Team Integration & Org

Collaboration:

Earn trust from an existing team of Business Analysts, Software Engineers, and Solution Architects who have deep SAP, Kinaxis and Blue Yonder expertise. Apply the team's intake rule: no planning AI or analytics work starts without a named decision owner, identified execution system, and assigned data product owner. Partner with Data Engineering and Analytics Ops teams to consume governed Snowflake data products for every planning initiative.

Executive Leadership:
Act as the primary interface between Supply Chain business leaders, IT Enterprise AI leadership, and the EPS organization. Translate AI capabilities into planning business value — present in terms of clear and measurable outcomes, not model accuracy. Enable executive visibility with clear objectives and key results. Partner with planning teams globally to ensure AI solutions reflect ground-level planning reality.

Team Leadership & Capability Building:
Lead and grow a hybrid team of Industry Analysts, Data Scientists, and Solution Architects. Build a culture of outcome ownership — every AI initiative owns its business metric, not just its technical delivery. Develop the team's ability to operate at the boundary of AI and supply chain planning — a rare and high-value set of skills.

Minimum Qualifications:

Sufficient understanding of supply chain planning concepts — demand, supply, inventory, order promising, IBP — to frame AI problems accurately

Proven experience leading AI, GenAI, or advanced analytics programs from ideation to production at scale

Strong understanding of AI/ML architectures, agentic systems, and LLM-based workflows

Familiarity with planning platforms (Blue Yonder, Kinaxis, SAP IBP) as context for embedding AIUnderstanding of modern data platforms (Snowflake, data products, semantic layers)

Preferred Qualifications:

Familiarity with enterprise AI tooling — LLM platforms, Copilot, agentic frameworks

Curiosity about how AI can reshape planning workflows beyond what current tools support

Strong Agile delivery expertise — roadmap management, prioritization, risk management

Ability to engage credibly with Data Scientists and Solution Architects on architecture and design decisions

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