Business - Supply Chain Manufacturing Ops & AI Transformation Delivery - Manager

Position: Business Performance - Supply Chain Manufacturing Ops & AI Transformation Delivery - Manager
Location:

Anywhere in Country

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We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

** The opportunity*
* As a Manager in our Supply Chain Manufacturing practice, you will lead market-facing delivery of manufacturing transformation programs that combine operational improvement, digital technology, advanced analytics, and AI. You will work directly with client executives, plant leaders, operators, engineers, quality, maintenance, supply chain, IT, OT, data, and AI teams to turn business priorities into implementable solutions that improve productivity, reliability, quality, visibility, decision-making, and manufacturing agility.

You will help clients establish the trusted data and knowledge foundations required to scale predictive, generative, and agentic AI across plant and network operations.

** Your key responsibilities*
* As a Manager in Supply Chain Manufacturing, you will be responsible for driving digital and AI-enabled solutions, plant transformation, and the application of frameworks essential to our clients' manufacturing goals.

+ Lead client-facing manufacturing transformation work streams and programs from opportunity shaping through design, implementation, deployment, and value realization.

+ Act as the two-way liaison between design and delivery teams by translating client needs into solution requirements and translating reusable capabilities into practical engagement plans.

+ Partner with the design the team prioritize solution enhancements, validate use cases, shape demonstrations, assess implementation readiness, and define the documentation, training, and support needed for scalable delivery.

+ Capture lessons, recurring requirements, configuration patterns, technical constraints, and delivery feedback from engagements and incorporate them into the design backlog and solution roadmap.

+ Assess manufacturing processes, performance gaps, user needs, data flows, controls, and technology constraints across plant and network environments.

+ Translate manufacturing priorities into process designs, functional requirements, user stories, data requirements, integration requirements, acceptance criteria, deployment roadmaps, and measurable outcomes.

+ Guide solution design across ERP, MES/MOM, connected worker, SCADA, historians, LIMS, QMS, EAM/CMMS, WMS, APS, industrial data platforms, knowledge platforms, AI services, analytics, and reporting environments, based on engagement needs.

+ Lead functional design, configuration oversight, prototyping, testing, validation, cutover, training, change adoption, hypercare, and benefits tracking.

+ Facilitate workshops and decision forums across operations, engineering, quality, maintenance, supply chain, IT, OT, cybersecurity, data, and technology-vendor stakeholders.

+ Manage project scope, plans, resources, economics, risks, dependencies, decisions, quality, and executive communications.

+ Lead and coach multidisciplinary delivery teams, review work products, and establish clear accountability for outcomes.

+ Support technical sales through solution shaping, demonstrations, estimates, proposals, implementation approaches, and responses to requests for proposal.

+ Identify follow-on opportunities based on client outcomes and emerging manufacturing priorities while maintaining trusted client relationships.

+ Shape and deliver manufacturing AI use cases such as predictive maintenance, quality intelligence, root-cause analysis, process optimization, intelligent scheduling, energy optimization, knowledge assistants, copilots, and AI-enabled frontline workflows.

+ Design manufacturing data and knowledge foundations that connect structured, unstructured, time-series, event, image, document, and engineering data across plant, edge, and cloud environments.

+ Lead the definition of manufacturing ontologies, common data models, semantic models, and semantic layers covering assets, equipment hierarchies, materials, products, orders, batches, recipes, processes, quality events, maintenance records, people, locations, and performance measures.

+ Guide the implementation of knowledge graphs that connect operational entities, relationships, events, documents, and business rules to support contextual search, multi-hop reasoning, traceability, explainability, and reusable AI services.

+ Define and implement retrieval-augmented…