Design, develop, test, and evaluate integrated systems for managing industrial production processes, including human work factors, quality control, inventory control, logistics and material flow, cost analysis, and production coordination.
U.S. Workers
350,230
Median Salary
$101,140
10-Year Growth
+11.0%
Annual Openings
25,200
Typical entry: Bachelor's degree
20 of 20 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Plan and establish sequence of operations to fabricate and assemble parts or products and to promote efficient utilization.
AI: Fully automatable - Given sufficient process data and constraints, AI can plan and optimize sequences of fabrication and assembly operations using simulation and scheduling algorithms and produce executable operation plans.
Review production schedules, engineering specifications, orders, and related information to obtain knowledge of manufacturing methods, procedures, and activities.
AI: Fully automatable - AI can ingest production schedules, engineering specs, and orders to extract manufacturing methods and produce accurate summaries and actionable recommendations at scale.
Estimate production costs, cost saving methods, and the effects of product design changes on expenditures for management review, action, and control.
AI: Fully automatable - AI can model production costs, run sensitivity analyses, and propose cost‑saving measures and the projected effects of design changes when provided with the requisite cost and process data.
Draft and design layout of equipment, materials, and workspace to illustrate maximum efficiency using drafting tools and computer.
AI: Fully automatable - AI‑driven CAD and layout tools can automatically generate optimized equipment, material, and workspace layouts from constraints and data and output usable drafting files.
Regulate and alter workflow schedules according to established manufacturing sequences and lead times to expedite production operations.
AI: Fully automatable - Given manufacturing sequences, lead times, and real‑time data, AI scheduling and optimization systems can autonomously regulate and alter workflow schedules to expedite production when integrated with operations.
Apply statistical methods and perform mathematical calculations to determine manufacturing processes, staff requirements, and production standards.
AI: Fully automatable - Applying statistical methods and performing mathematical calculations is a core strength of AI given appropriate data and assumptions, enabling determination of processes and staffing quantitatively.
Complete production reports, purchase orders, and material, tool, and equipment lists.
AI: Fully automatable - Completing production reports, purchase orders, and material/tool/equipment lists is largely clerical and data‑driven, and can be fully automated when integrated with enterprise systems.
Formulate sampling procedures and designs and develop forms and instructions for recording, evaluating, and reporting quality and reliability data.
AI: Fully automatable - By 2025 AI systems can design statistical sampling plans, generate forms and instructions, and adapt templates to data and regulatory constraints with high reliability.
Schedule deliveries based on production forecasts, material substitutions, storage and handling facilities, and maintenance requirements.
AI: Fully automatable - Modern AI-driven planning and optimization tools can schedule deliveries accounting for forecasts, substitutions, storage, and maintenance constraints with a high degree of automation by 2025.
Coordinate and implement quality control objectives, activities, or procedures to resolve production problems, maximize product reliability, or minimize costs.
AI: Partial - AI can detect quality issues, generate QC procedures, and monitor metrics to resolve production problems, but implementing and coordinating cross‑functional quality initiatives still requires human leadership and on‑site action.
Communicate with management and user personnel to develop production and design standards.
AI: Partial - AI can generate communications, synthesize stakeholder input, and propose production/design standards, but it cannot fully replicate the interpersonal negotiation and leadership required to secure organizational buy‑in.
Recommend methods for improving utilization of personnel, material, and utilities.
AI: Partial - AI can analyze utilization data and simulation models to recommend methods but lacks full contextual judgment and implementation authority, so human validation and on-site knowledge remain necessary.
Develop manufacturing methods, labor utilization standards, and cost analysis systems to promote efficient staff and facility utilization.
AI: Partial - AI can generate manufacturing method proposals, labor standards, and cost models from data and best practices, but developing, validating, and implementing them in situ requires human engineering oversight and trials.
Analyze statistical data and product specifications to determine standards and establish quality and reliability objectives of finished product.
AI: Partial - AI can analyze statistical data and specs to propose standards and reliability targets, but defining acceptable objectives and tradeoffs requires cross‑functional human decision making.
Confer with clients, vendors, staff, and management personnel regarding purchases, product and production specifications, manufacturing capabilities, or project status.
AI: Partial - AI can draft communications, summarize specifications, and run routine coordination via chatbots, yet nuanced negotiation, relationship management, and real‑time stakeholder judgment require humans.
Study operations sequence, material flow, functional statements, organization charts, and project information to determine worker functions and responsibilities.
AI: Partial - AI can study and synthesize documentation (flow, org charts, functional statements) to propose worker functions, but lacks tacit, observational insight from the floor needed for complete role determination.
Record or oversee recording of information to ensure currency of engineering drawings and documentation of production problems.
AI: Partial - AI can automate recording, version control, and flagging of out‑of‑date drawings and production issues, but final validation and accountability for engineering documentation require human oversight.
Direct workers engaged in product measurement, inspection, and testing activities to ensure quality control and reliability.
AI: Partial - AI can provide inspection protocols, real-time analytics, and decision support for directing workers, but cannot fully replace on-site managerial judgement, authority, and human coordination as of 2025.
Evaluate precision and accuracy of production and testing equipment and engineering drawings to formulate corrective action plan.
AI: Partial - AI can analyze measurement and tolerance data to identify precision/accuracy problems and suggest corrective actions, but confirming equipment condition and implementing corrective measures need on‑site engineering judgment.
Implement methods and procedures for disposition of discrepant material and defective or damaged parts, and assess cost and responsibility.
AI: Partial - AI can propose dispositions, calculate cost implications, and suggest responsibility assignments, but final disposition and liability decisions typically require human authority and contextual judgment.