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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.
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 workstreams 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 generation approaches, including document RAG, hybrid retrieval, and graph-augmented RAG, using vector search, metadata, semantic relationships, and governed source content to ground AI responses.
Translate manufacturing knowledge into machine-readable structures, retrieval strategies, prompts, agent instructions, decision rules, and reusable context services that improve AI relevance and reduce unsupported outputs.
Establish AI delivery and governance requirements covering data quality, lineage, provenance, access controls, model evaluation, human oversight, cybersecurity, intellectual property, regulatory compliance, monitoring, and responsible AI.
Plan and execute AI pilots from use-case prioritization and value framing through data readiness, prototyping, evaluation, deployment, adoption, benefits tracking, and scaling across sites.
To excel in this role, you will need a blend of technical and business skills, including relationship management, commercial acumen, and communication. You should be adept at complex problem-solving and critical thinking, with a strong capacity for change management.
Manufacturing functional leadership: Strong understanding of production operations, planning and scheduling, shop-floor execution, operational excellence, asset productivity, maintenance, quality, warehouse operations, material flow, performance management, or new plant and line start-up.
Digital manufacturing technology: Implementation or delivery experience with manufacturing platforms such as MES/MOM, connected worker, electronic work instructions, EBR, digital performance management, maintenance, quality, warehouse, planning, industrial data, or analytics solutions.
Delivery translation: Ability to convert repeatable solution assets into client-specific delivery approaches and convert delivery feedback into clear requirements, priorities, reusable patterns, and roadmap recommendations for solutions.
Architecture and systems landscape: Working knowledge of how ERP and supply chain applications connect with MES/MOM, SCADA, historians, PLC-enabled data, LIMS, QMS, EAM/CMMS, WMS, APS, cloud, edge, integration, and reporting layers.
Requirements, data, and integration: Ability to lead process design, functional requirements, user stories, workflows, data mapping, interface requirements, master-data considerations, controls, roles, reporting, and acceptance criteria.
Implementation leadership: Experience leading design, configuration oversight, testing, validation, deployment, cutover, training, adoption, hypercare, value tracking, and continuous improvement.
Client and team leadership: Ability to facilitate executive and plant-level discussions, manage multidisciplinary teams, communicate complex topics clearly, coach colleagues, and build trusted relationships.
Commercial and delivery management: Experience managing scope, resources, economics, quality, risk, proposals, estimates, technical sales, and opportunities for extended services.
AI-enabled manufacturing: Ability to connect predictive, generative, and agentic AI capabilities with practical manufacturing use cases, operating workflows, controls, and measurable business outcomes.
Manufacturing knowledge engineering: Experience defining ontologies, taxonomies, entity relationships, business rules, and common manufacturing data models that create shared meaning across opera
