Manager, Supply Chain Intelligence

Lutron Electronics Co., Inc•Coopersburg, PA

11d

About The Position

Lutron Electronics Co., Inc. has an immediate opening for a Manager, Supply Chain Intelligence within our Operations Information and Action (OIA) group. In this role, you will lead cross-functional efforts to improve how our global supply chain makes decisions, takes action, and learns from outcomes. You will combine supply chain expertise, analytical and AI fluency, and change leadership skills to deliver practical, responsible, and measurable improvements in service, quality, resilience, cost, working capital, and productivity.

Requirements

Bachelor's degree required; Degree in Supply Chain, Operations Management, Engineering, Business, Analytics, Information Systems, or a related field preferred
12+ years of progressive experience across supply chain, manufacturing, operations, continuous improvement, digital transformation, analytics, automation, data, or AI-enabled business change.
Deep understanding of supply chain or manufacturing decisions, workflows, performance management, and cross-functional tradeoffs.
Demonstrated success leading complex transformation or technology-enabled change from opportunity definition through adoption and measurable value realization.
Experience translating operational problems into data, analytics, automation, or AI use cases and partnering with technical teams through delivery.
Practical fluency with modern analytics, automation, and AI tools sufficient to explore data, prototype concepts, assess outputs, and communicate effectively with technical specialists.
Proven ability to teach, coach, build communities, and lead matrixed teams without relying on direct authority.
Strong executive communication, stakeholder management, critical thinking, and change-leadership skills.

Nice To Haves

Experience leading and delivering value in intelligence-based change management initiatives, including Decision Intelligence, Artificial Intelligence, and/or other business process streamlining frameworks.
Experience spanning both an Operations role and a data, analytics, digital, product, process-improvement, or AI-focused role.
Experience in more than one supply chain domain, such as planning, S&OP, procurement, manufacturing, logistics, fulfillment, finance, quality, or customer operations.
Experience with use-case portfolio management, product management, experimentation, process redesign, or operating-model development.
Working knowledge of responsible AI, data governance, master data, data quality, enterprise architecture, cybersecurity, and AI lifecycle controls.
Experience with machine learning, optimization, generative AI, agentic AI, decision automation, simulation, or digital-twin concepts in an operational setting.

Responsibilities

Lead the Operations Decision Intelligence and AI Portfolio: Partner with Operations and enterprise leaders to identify, define, and prioritize decisions where Decision Intelligence (DI), analytics, automation, or AI can materially improve business outcomes. Maintain a transparent portfolio and roadmap that balances strategic value, feasibility, data readiness, operational risk, time to value, and organizational capacity. Build business cases and define outcome measures tied to service, cost, working capital, quality, resilience, productivity, responsiveness, employee experience, and customer experience. Guide high-impact initiatives from discovery and prototyping through deployment, adoption, sustainment, benefit tracking, and scale.
Design Better Human and AI Decisions: Map critical decision workflows, including signals, context, tradeoffs, decision rights, actions, outcomes, feedback, and failure modes. Determine when a person, AI system, or human-AI combination is best suited to perform the work or make the decision. Design fit-for-purpose controls for recommendations and automated actions, including approval, reversibility, escalation, monitoring, auditability, and accountable ownership. Use experiments, prototypes, and operational evidence to validate decision quality and workflow performance before scaling.
Serve as AI Champion for Operations: Represent Operations needs, constraints, risks, and priorities in enterprise AI planning and governance processes. Translate operational problems into clear use cases, user needs, data requirements, decision requirements, and measurable success criteria. Ensure Operations deployments comply with enterprise standards for responsible AI, security, privacy, architecture, approved tools, and technical sustainment. Coordinate across Operations, data/intelligence center of excellence, IT, information product owners, analysts, data professionals, vendors, and other delivery partners to move solutions into reliable use.
Build an Operations Decision Intelligence & AI Network: Identify and recruit subject-matter experts from Operations functions and facilities who can contribute part-time to DI/AI initiatives alongside their primary roles. Define contributor expectations, working methods, sponsorship, learning paths, and recognition in partnership with functional and plant leaders. Coach contributors in decision discovery, problem framing, experimentation, responsible tool use, adoption, and outcome measurement. Create a community that shares reusable patterns, lessons, prototypes, and success stories across Operations.
Teach and Lead Organizational Change: Develop practical education for Operations leaders, practitioners, and contributors on Decision Intelligence, AI capabilities and limitations, responsible use, and human-AI collaboration. Facilitate cross-functional conversations that make tradeoffs explicit, establish clear decision ownership, and convert insights into action. Lead workflow and role redesign needed to embed new capabilities into daily management and operational processes. Use clear narratives and decision-focused communication to build understanding, sponsorship, trust, and sustained adoption.
Prototype, Learn, and Scale: Use approved analytics, automation, generative AI, and prototyping tools to test concepts and demonstrate value without assuming responsibility for all production engineering. Work with technical partners to define production requirements, integration needs, controls, testing, deployment, and support models. Monitor usage, decision quality, operational outcomes, unintended effects, and user feedback after deployment. Stop, redesign, or scale initiatives based on evidence; capture lessons-learned so the operating model improves with each cycle.