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SOC 17-2112 · O*NET 17-2112.00
Industrial engineers correspond to the exact U.S. O*NET/SOC occupation 17-2112.00, Industrial Engineers: they 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. Official scope: 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. Core tasks include estimating production costs and the impact of design changes, planning manufacturing and assembly sequences, setting quality and reliability objectives, consulting stakeholders, evaluating equipment and drawing precision, recommending improvements, and maintaining records. U.S. wage and employment figures on this page come from BLS OEWS 2025 and BLS employment projections for 2024–2034 as presented by O*NET (the exact 17-2112.00 occupation, designated Bright Outlook). Exact occupational wage data for mainland China were not obtained and are recorded as a visible gap; Chinese qualification and safety requirements are identified separately.
Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((75+75+50)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.
Taking responsibility | Fit depends on whether you can do three things within the same systems assignment: substantiate the system and data (define objectives and constraints, collect and analyze production data, build and validate an integrated system model, and produce reviewable analysis and recommendations); uphold standards and safety (follow organizational and regulatory requirements for quality, reliability, safety, compliance, cost, and production constraints); and accept clearly defined engineering responsibility (take responsibility for your own system design and analytical conclusions, while escalating or referring safety conclusions and compliance decisions beyond your authority). Test this through a verifiable experience, such as producing a systems-analysis workflow and improvement assessment or modeling simulated production data and practicing tradeoffs, rather than relying only on an interest questionnaire.
Caution signals | The official scope and tasks involve system design, data and models, and cross-functional tradeoffs. Insufficient data or invalid model assumptions, a proposal conflicting with safety or cost constraints, conclusions exceeding the evidence, or a recommendation mistaken for an engineering or compliance determination can waste investment or create quality and safety risks. This page therefore addresses data and model quality, safety and cost tradeoffs, consistent definitions across functions, and the limits of conclusions and authority separately. The U.S. occupation is designated Bright Outlook. O*NET's summary and employment trends indicate much faster-than-average projected growth of 11% for 2024–2034, about 25,200 openings annually, and about 351,100 people employed in 2024. If cross-functional negotiation, system complexity, or pressure to balance multiple objectives is difficult for you, weigh these practical costs carefully.
Industrial engineers (O*NET/SOC 17-2112.00, Industrial Engineers) 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. Official scope: 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. Core tasks include estimating production costs, cost-saving methods, and the spending impact of product design changes to support management decisions; planning and establishing manufacturing and assembly operation sequences for parts or products; analyzing statistical data and product specifications to determine standards and establish finished-product quality and reliability objectives; consulting customers, suppliers, employees, and management about purchases, product and production specifications, manufacturing capabilities, or project status; communicating with management and user personnel to develop production and design standards; evaluating the precision and accuracy of production and testing equipment and engineering drawings to develop corrective actions; recommending better utilization of personnel, materials, and utilities; and recording or overseeing information recording to keep drawings current and document production problems. Common titles include Industrial Engineer, Process Engineer, Quality Engineer, Production Engineer, Continuous Improvement Engineer, and Plant Engineer. U.S. data on this page cover the exact 17-2112.00 occupation. Mainland Chinese qualification and safety systems are identified separately using sources for that jurisdiction; the two cannot be applied interchangeably.
Career direction comparison
The following comparison helps distinguish career directions. Each row addresses the same dimensions: the direction's core work and outputs, its key difference from this occupation, and the preferences it may suit. Evidence links in the table lead to official U.S. occupational pages.
| Career direction | Core work and outputs | Key difference | Preferences it may suit |
|---|---|---|---|
| Industrial Engineers (this occupation) | Design and evaluate integrated industrial production systems, balancing human factors, quality, inventory, logistics, and costs | Centers on integrated production-system design and evaluation | People willing to use engineering methods and data to improve systems |
| Manufacturing Engineers (adjacent) | Design manufacturing processes and production methods |
AI's impact on the occupation
Insufficient evidence: no occupation-specific evidence was obtained for generative AI adoption rates, productivity, net employment effects, or replacement timelines among industrial engineers. Editorial trial candidates include data organization, model-code assistance, proposal research, and report drafting. Verify system objectives, methodological assumptions, cost and quality constraints, and proposal feasibility. AI Impact Score 67% is an editorial estimate. Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((75+75+50)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.The three tasks, ratings and sources are listed in the FermatMind method source record on this page.
FermatMind AI Impact Score estimation method v1
FermatMind / 费马测试 · 2026-10-18
Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
Mainland Chinese wage reference
Classification note | U.S. SOC 17-2112.00 is Industrial Engineers within engineering occupations. Human factors engineers and ergonomists (17-2112.01), manufacturing engineers (17-2112.03), mechanical engineers (17-2141.00), quality control systems managers (11-3051.01), logistics engineers (13-1081.01), and project management specialists (13-1082.00) are separate occupations. International classifications such as ISCO-08 indicate only the broad placement of industrial engineering roles; they cannot replace U.S. SOC or mainland Chinese occupational classifications and statistical definitions.
Occupational definition | Industrial engineers on this page correspond to U.S. SOC 17-2112.00, Industrial Engineers. The official scope is to 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. Mainland Chinese roles in industrial engineering and production-system optimization have similar tasks, but qualification and safety systems belong to different jurisdictions. Figures are not converted between them.
U.S. wage reference
2025 SOC17-2112 wage percentiles 10 / 25 / 50 / 75 / 90: $74,370 / 83,600 / 102,440 / 129,250 / 159,860; median hourly wage: $49.25. Current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI.
| Wage distribution position | Annual wage reference | Conversion boundaries | How to interpret this position |
|---|---|---|---|
| Bottom 10% | 10th-percentile annual wage: $74,370 (2025, BLS OEWS, exact occupation 17-2112.00) | Official annual wages, not personal monthly income | The 10th percentile is a wage-distribution threshold, not minimum pay, starting pay, or an identification of less-experienced workers. |
| Median (50%) | Median annual wage: $102,440 (2025, BLS OEWS, exact occupation 17-2112.00) |
Career fit guide
Direct answer | Fit for industrial engineering is indicated by the ability to turn production and material-flow data into reviewable system models and improvement proposals and make explainable tradeoffs within safety, quality, and cost constraints, rather than by personality labels. Interest code CIR (Conventional–Investigative–Realistic) indicates a direction only; preferences do not equal talent or a hiring conclusion.
You may be better suited： Signal one | Can you turn production data into reviewable systems analysis and proposals?
Conditions requiring caution： Signal two | Can you make tradeoffs within safety, quality, and cost constraints and explain the reasoning?
Try an entry exercise： Signal three | Can you identify matters beyond your authority and escalate or refer them?
Work pressures, risks, and professional boundaries
Direct answer | Industrial engineers' main risks fall into four groups: proposals conflicting with safety or cost requirements, data or model quality defects, conclusions exceeding the evidence, and inconsistent cross-functional definitions or assumptions. Priorities are traceable records of data, assumptions, and review, with appropriate wording and escalation when uncertainty or authority limits arise.
Evidence scope | Risk items draw on O*NET 17-2112.00 tasks—cost analysis, production planning, quality and reliability, consultation, evaluation and correction, and improvement documentation—and work activities, plus relevant safety and quality requirements. Mainland Chinese institutional boundaries are editorial summaries subject to current regulations and organizational procedures.
Typical scenario：Undetected conflicts between proposals and safety or cost requirements
Main roles：Industrial engineer; safety and finance leads
Entry, qualifications, and career development
Direct answer | Entry usually combines a relevant degree with data-analysis and systems-modeling training. Build experience in project analysis, proposal design, and implementation before gradually taking on design leadership and cross-departmental coordination. Where safety or quality systems are involved, verify qualifications under organizational rules. Both paths require verifiable project results and recommendations as evidence of ability, rather than certificates alone.
Career outlook and related transitions
Current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI. Bright Outlook is O*NET's current designation, not a personal employment guarantee. The 2025 wage percentiles and projections are separate; exact mainland Chinese occupational wages remain unavailable. Transition distances are editorial comparisons.
United States · Exact occupation 17-2112.00
12% (much faster than average)
Employment growth
Overall employment projections do not establish AI causation. Growth measures total employment change; openings include growth and replacement needs and are not net new jobs.
National projections do not equal actual hiring or individual employment outcomes; distinguish growth from replacement openings.
According to O*NET 17-2112.00, this occupation designs, develops, tests, and evaluates integrated systems for managing industrial production processes, including human work factors, quality control, inventory control, logistics and material flow, cost analysis, and production coordination. Typical tasks include estimating production costs and design-change impacts, planning manufacturing and assembly sequences, analyzing statistics and specifications to establish quality and reliability objectives, consulting stakeholders, evaluating equipment and drawing precision, recommending improvements, and maintaining records.
Manufacturing engineers focus on manufacturing process and production-method design; human factors engineers and ergonomists on human factors and ergonomics assessments; quality control systems managers on quality management systems and compliance; logistics engineers on logistics and supply-chain systems; and project management specialists on project schedules and delivery management. Work objects, qualifications, and responsibility boundaries differ; titles alone do not make roles interchangeable.
Choose a real task from this page for a small practice project or a conversation with someone in the field. Use the planning guides to set a date, an output and feedback questions before deciding whether to continue.
Fit signals | Tasks and work activities in U.S. O*NET 17-2112.00 show that this occupation centers on estimating production costs and design-change impacts; planning manufacturing and assembly sequences; analyzing statistical data and specifications to set quality and reliability objectives; consulting customers, suppliers, and management; developing production and design standards; evaluating equipment and drawing precision; and recommending improvements. Work primarily involves data analysis, system design, and cross-functional communication, with production-site visits. The interest code is CIR (Conventional–Investigative–Realistic); key skills include active listening, critical thinking, mathematics, and systems analysis. If you are willing to use engineering methods and data to improve production efficiency, quality, and cost while taking responsibility for system design, these daily requirements may align with your working style.
This checklist is for self-reflection only. It is not a hiring standard, ability score, assessment of job competence, or engineering, safety, compliance, or investment advice. This page covers the exact U.S. O*NET/SOC occupation 17-2112.00, Industrial Engineers: designing, developing, testing, and evaluating integrated systems for managing industrial production processes, including human work factors, quality control, inventory control, logistics and material flow, cost analysis, and production coordination. Official scope: 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. wage and employment figures come from BLS OEWS 2025 and BLS employment projections for 2024–2034 as presented by O*NET (the exact 17-2112.00 occupation). Exact occupational wage data for mainland China were not obtained and are recorded as a visible gap; the two jurisdictions' legal and safety systems cannot be applied interchangeably.
To assess your fit for industrial engineering, first complete a verifiable systems exercise using public or simulated materials. Define a fictional production system, such as an assembly plant with both long lead times and high inventory costs. Write a step-by-step process covering system objectives and constraints, production and material-flow data collection, development and analysis of an integrated system model (including human factors, quality, inventory, logistics, and costs), identification of improvements and assessment of side effects, and presentation of recommendations to management and stakeholders. Mark checkpoints and possible problems at each step: insufficient or inconsistently defined data, invalid model assumptions, conflicts with safety or cost requirements, conclusions beyond the evidence, or recommendations mistaken for engineering or compliance determinations. Then check whether data quality, model assumptions and safety, and consistent definitions across functions are indispensable parts of your process; whether engineering judgments, safety conclusions, and compliance decisions beyond your authority are escalated or referred; and whether your analysis leaves an auditable record. Test yourself through a concrete deliverable, rather than relying solely on an interest questionnaire.
Industrial engineers' daily work can be divided into three streams: (1) modeling and analysis—collect production and material-flow data, analyze cost, quality, and capacity, and build integrated system models; (2) design and tradeoffs—plan and optimize manufacturing and assembly processes while balancing human factors, quality, inventory, logistics, and costs; and (3) consultation and implementation—work with customers, suppliers, and management to establish production and design standards, recommend improvements, and follow implementation. All three share the same engineering and data requirements but serve different outputs: modeling and analysis establish evidence, design and tradeoffs develop proposals, and consultation and implementation support decisions and execution.
Primarily data analysis, system design, and cross-functional communication, with production-site visits; requires balancing multiple objectives within safety and cost constraints.
Production-system improvements affecting investment, quality, and safety rely on this evidence, demanding greater care with data quality and the limits of conclusions.
Scope boundaries: this page covers only the exact O*NET/SOC occupation 17-2112.00, Industrial Engineers, centered on designing, developing, testing, and evaluating integrated industrial production systems. Adjacent occupations—manufacturing engineers, human factors engineers and ergonomists, quality control systems managers, logistics engineers, project management specialists, and industrial engineering technologists and technicians—differ in work objects, qualifications, and responsibilities and cannot be interchanged by title alone. Engineering judgments, safety conclusions, and compliance decisions depend on authorized responsibilities and must be made by qualified personnel under organizational rules and applicable regulations.
| Dimension | Main direction | Comparison direction |
|---|---|---|
| Core purpose | Turn production and material-flow data into reviewable system models and improvement proposals, and make recommendations under multiple constraints. | Manufacturing engineers focus on designing manufacturing processes and production methods and generally do not take responsibility for overall integrated production-system design and evaluation. |
| Main work objects and basis | The work concerns integrated systems, data, and processes in industrial production, using engineering methods, quality standards, and safety procedures. |
Estimate production costs, cost-saving methods, and the spending impact of product design changes for management review, decisions, and control (O*NET task: estimate production costs and change impacts).
Plan and establish manufacturing and assembly operation sequences for parts or products to promote efficient utilization (O*NET task: plan manufacturing and assembly sequences).
Analyze statistical data and product specifications to determine standards and establish finished-product quality and reliability objectives (O*NET task: establish quality and reliability objectives).
Confer with customers, suppliers, employees, and management about purchases, product and production specifications, manufacturing capabilities, or project status, and communicate with management and user personnel to develop production and design standards (O*NET task: consultation and communication).
Evaluate the precision and accuracy of production and testing equipment and engineering drawings to develop corrective-action plans (O*NET task: evaluate equipment and drawings).
Recommend improvements in the utilization of personnel, materials, and utilities, and record or oversee the recording of information to keep drawings current and document production problems (O*NET tasks: recommend improvements; record information).
Before accepting a role, verify each of the following items relating to O*NET/SOC 17-2112.00 and mainland Chinese qualification and safety systems. Any unverified item remains pending verification; recruitment advertising cannot substitute for checking it.
Basis: the official O*NET 17-2112.00 definition and task list.
Basis: O*NET Job Zone Four and educational attainment information.
Basis: qualification, safety, and quality regulations in the job's jurisdiction and organizational procedures.
Basis: the job posting and organizational production and performance-assessment procedures.
| Produces manufacturing processes and engineering methods, with a narrower process focus |
| People interested in manufacturing process design |
| Human Factors Engineers and Ergonomists (adjacent) | Assess human factors and ergonomics and improve work systems | Produces human factors and ergonomics assessments; generally excludes cost and inventory analysis | People interested in evaluating human–system interaction |
|---|
| Quality Control Systems Managers (adjacent) | Manage quality management systems and compliance | Produces systems management and compliance outcomes; generally excludes production-system design | People interested in quality management and compliance |
|---|
| Logistics Engineers (adjacent) | Design logistics and supply-chain systems | Produces logistics and supply-chain systems, with a logistics focus | People interested in logistics and supply-chain design |
|---|
| Project Management Specialists (adjacent) | Manage project schedules, resources, and delivery | Produces project delivery management outcomes; generally excludes system design | People interested in project coordination and delivery management |
|---|
Conclusion | Industrial engineers and adjacent occupations differ in their work objects and outputs. This occupation centers on designing, developing, testing, and evaluating integrated industrial production systems, including human factors, quality, inventory, logistics, and costs. Manufacturing engineers focus on manufacturing processes and production methods; human factors engineers and ergonomists on human factors and ergonomics assessments; quality control systems managers on quality management systems and compliance; logistics engineers on logistics and supply-chain systems; and project management specialists on project schedules and delivery management. Interchanging roles by title alone can misrepresent methods and responsibility boundaries.
Transition guidance | These directions differ in methods, qualifications, and responsibilities. Moving between them requires relevant training and experience; authority for engineering judgments, safety conclusions, and standards development does not transfer automatically between roles.
Editorial analysis: assistance with data organization, model code, and report drafting may be evaluated. Validate data, model assumptions, and proposal constraints. A technical demonstration does not establish production effectiveness or confer approval authority.
Insufficient evidence: no occupation-specific measurements of generative AI adoption rates or productivity were obtained. Governance frameworks and tool capabilities do not establish expanding industry adoption, established implementation standards, or widespread adoption.
Current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI. This page has obtained no causal research on generative AI's net employment effects.
| Work direction | Task | Current changes | Human control points |
|---|---|---|---|
| Estimate production costs and change impacts | Cost analysis | Editorial trial suggestion: tools may assist with data organization and cost-model calculations. Occupation-specific adoption rates or effects have not been verified. | Responsibility for cost assumptions, definitions, and conclusions. |
| Plan manufacturing and assembly sequences | Production planning | Editorial trial suggestion: tools may assist with scheduling calculations and comparison of proposals. Occupation-specific adoption rates or effects have not been verified. | Judgment about process design, capacity, and constraints. |
| Establish quality and reliability objectives | Quality and reliability | Editorial trial suggestion: tools may assist with statistical analysis and charts. Occupation-specific adoption rates or effects have not been verified. | Standards setting, quality judgment, and accountability. |
| Consult stakeholders to develop standards | Consultation and communication | Editorial trial suggestion: tools may help organize communication and standards materials. Occupation-specific adoption rates or effects have not been verified. | Cross-functional consultation, standards development, and accountability. |
| Evaluate equipment and drawing precision | Evaluation and correction | Editorial trial suggestion: tools may help organize assessment records. Occupation-specific adoption rates or effects have not been verified. | Precision assessment, corrective proposals, and accountability. |
| Recommend improvements and maintain records | Improvement and documentation | Editorial trial suggestion: tools may help organize recommendations and documentation. Occupation-specific adoption rates or effects have not been verified. | Responsibility for recommendation feasibility and document accuracy. |
| Collaborate with production, quality, and supply-chain teams | Cross-functional collaboration | Editorial trial suggestion: tools may help organize collaboration materials. Occupation-specific adoption rates or effects have not been verified. | Responsibility for consistent definitions and assumptions and for coordination. |
Why can employment evidence yield different conclusions? Task exposure, organizational adoption, productivity change, and job numbers are four different measurement objects. Their samples, methods, and periods also differ, so one cannot be inferred from another.
The following editorial comparison considers task objects and potential assistance for trials; actual conditions, data, and methods require verification. No occupation-specific measurements of relative acceleration or adoption sequence were obtained.
What AI mainly changesEditorial trial suggestion: explore assistance with data organization, statistics, and code.
Still human responsibilitiesDefining system objectives, selecting methods, and taking responsibility for conclusions.
What AI mainly changesTools may assist with proposal research and parameter calculations.
Still human responsibilitiesJudging proposal safety and tradeoffs between cost and quality.
What AI mainly changesTools may assist with communication materials and draft standards.
Still human responsibilitiesResponsibility for cross-functional consultation, standards development, and implementation.
When digital tools enter engineering workflows, accountability must remain continuous. Identify who is responsible at every stage, from objective confirmation and tool processing to human review, documentation, and authorization, and retain traceable evidence.
Define system objectives, data and conclusion boundaries, permitted tools, and information that must not be sent externally, including production data, process and equipment information, unpublished proposals, and costs.
Limit tool use to data organization and statistics, code and modeling assistance, report and chart drafting, and research. Tools must not directly issue safety conclusions, quality determinations, or engineering decisions.
Responsible personnel must verify data, models, and proposals item by item, confirm consistency with site conditions and standards, and assess compliance with engineering specifications and quality requirements.
Record tools and versions, key inputs, changes, and their grounds so results are reproducible and traceable for subsequent verification and review.
Identify decision-makers for engineering, quality and safety, and production changes for the specific task. Do not assume this occupation universally lacks judgment authority or that every decision belongs to a regulatory authority.
Tool-generated analysis or conclusions are treated as confirmed results, introducing errors into proposals or decisions
Control measures:Label generated content as provisional and verify data and models item by item before use
Production data and process and equipment information are sent externally, creating data and business risks
Control measures:Follow organizational information requirements; confirm authorization and the minimum necessary scope before use
Tool versions and inputs are not recorded, making the original basis difficult to reconstruct in a dispute
Control measures:Consistently record tools, versions, and key inputs, and retain change histories
Incorrect statistical or modeling guidance leads to faulty proposals and cost judgments
Control measures:Check results against data and model assumptions; do not substitute tool conclusions for validation
Missing review creates unclear accountability, making responsibility difficult to determine when problems arise
Control measures:Identify who is responsible for review, approval, safety, and quality in the actual task. Check organizational responsibilities and applicable requirements without generalizing a universal personal qualification.
Use AI for research, data processing, and modeling assistance, but do not treat generated content as a conclusion. First build solid industrial engineering, data analysis, and systems modeling skills.
Editorial suggestion: trial data, modeling, and drafting tools in an authorized environment and measure their effects; review system objectives and proposal constraints. No general time-saving effect has been established.
Specify digital-tool boundaries in design procedures, including whether tools may process data, assist with code, or draft materials; define data rules and mechanisms for reviewing and reverting tool outputs.
Use tools for organization and preliminary analysis, but do not substitute automated conclusions for data verification, proposal safety assessments, or engineering judgment.
Insufficient evidence: no occupation-specific evidence was obtained for generative AI adoption rates, productivity, net employment effects, or replacement timelines among industrial engineers. Editorial trial candidates include data organization, model-code assistance, proposal research, and report drafting. Verify system objectives, methodological assumptions, cost and quality constraints, and proposal feasibility.
Source:ILO task-exposure limitations and editorial analysisOriginal material ↗
O*NET lists computer work, statistics, and systems-analysis tasks. CIR does not establish that every role has no coding requirement. Check the target role's software, modeling, and scripting requirements; tools do not replace method selection and validation.
Source:O*NET tasks; specific assessments are editorial analysisOriginal material ↗
Editorial analysis: data organization, model-code assistance, and report drafting are trial candidates. No measurements establish which task changes first or relative acceleration in this occupation. Multi-objective tradeoffs require checking specific constraints.
Source:O*NET tasks; specific assessments are editorial analysisOriginal material ↗
Current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI.
Source:Current BLS overall projections, not AI causationOriginal material ↗
Model output does not confer authority over system proposals, engineering approvals, or production changes. Verify decision-makers, organizational responsibilities, and jurisdiction for the actual task; do not refer everything to authorities or impose a universal personal certificate.
Source:O*NET tasks; specific assessments are editorial analysisOriginal material ↗
No evidence was obtained for an occupational disappearance year or replacement probability; task-exposure research is not a replacement timetable.
Source:ILO task-exposure limitations and editorial analysisOriginal material ↗
Editorial suggestion: first practice defining system objectives, validating data and models, balancing cost and quality, and consulting across functions. Then assess modeling and research tools in an authorized environment. No evidence establishes that these skills are irreplaceable or that efficiency improves generally.
Source:O*NET tasks; specific assessments are editorial analysisOriginal material ↗
Editorial suggestion: first practice defining system objectives, validating data and models, balancing cost and quality, and consulting across functions. Then assess modeling and research tools in an authorized environment. No evidence establishes that these skills are irreplaceable or that efficiency improves generally.
Insufficient evidence: no national wage figure specific to this occupation was obtained. The National Bureau of Statistics reports average wages by industry category, not occupation; this page does not substitute industry figures.
Insufficient evidence: no national wage figure specific to this occupation was obtained. Private-unit data are also reported by industry category and cannot be treated as industrial engineers' wages.
Insufficient evidence: mainland Chinese public occupational statistics do not establish a national wage distribution precisely corresponding to U.S. O*NET/SOC 17-2112.00, Industrial Engineers. The National Bureau of Statistics publishes average wages by industry category, not occupation. This page does not fill the gap with industry averages, recruitment offers, or U.S. data.
Mainland China section | Mainland Chinese roles in industrial engineering and production-system optimization have tasks similar to industrial engineers; their qualification and safety requirements are governed by applicable regulations and organizational procedures. Public occupational statistics do not separately report a national wage distribution for this occupation: the National Bureau of Statistics publishes average wages for employees in urban units by industry category, without distinguishing occupations. Under the evidence policy, this page records statistical definitions and visible gaps only, without substituting industry averages, recruitment offers, or U.S. wages for exact occupational figures.
| Educational background | Common role directions | Explanation and verification guidance |
|---|---|---|
| Bachelor's degree (industrial engineering, mechanical engineering, or a related field) | Industrial engineering assistant; production analysis | O*NET's education survey reports 59% bachelor's degree, 16% some college without a degree, and 11% master's degree. Job Zone Four's several years of preparation describes an occupational group, not a uniform experience requirement for this occupation. Check mainland Chinese job postings. |
| Relevant degree plus data analysis and systems modeling skills | Industrial engineer; process engineer | Editorial verification suggestion: check the target posting's education, data, and systems-method requirements. No uniform sample representing most mainland Chinese roles was obtained. |
| Specialist experience and system optimization skills | Lean, supply-chain, or quality engineering | Specialist assignments or leading system design usually require more project experience. Job authorization and engineering judgment authority depend on organizational procedures. |
| Engineering or safety qualifications (depending on role and industry requirements) | Production-system roles involving safety, quality, and compliance | Check licensing or safety requirements for the actual industry, project, and activity. Do not impose a universal personal certificate based on the industrial engineer title. Verify education, methods, job authorization, and production-change authority separately. |
Conditional broad industry reference: if the target employer is classified under manufacturing, NBS2025 Tables 2 / 5 report non-private/private average annual wages of 113,594 / 76,055 yuan; for scientific research and technical services, the figures are 182,064 / 83,560 yuan. Verify the employer's industry. These are separately reported national pretax industry averages covering all occupations, not industrial engineers' median, starting, or take-home pay; exact occupational wages remain unavailable.
O*NET's industry page identifies manufacturing and professional, scientific, and technical services as major U.S. industries for this occupation. National Bureau of Statistics average wages are industry-based and do not distinguish occupations; this page does not substitute them for occupational wages.
Some roles are in logistics, energy, and healthcare. Employment scale and performance criteria differ; this page does not supply substitute figures.
Classifications and statistical definitions differ; no numerical conversion is made.
Insufficient evidence: no occupation-specific evidence was obtained for generative AI adoption rates, productivity, net employment effects, or replacement timelines among industrial engineers. Editorial trial candidates include data organization, model-code assistance, proposal research, and report drafting. Verify system objectives, methodological assumptions, cost and quality constraints, and proposal feasibility.
| Official annual wages, not personal take-home pay |
| The median is the distribution midpoint, not typical experience, a personal starting salary, or a pay progression. |
| Top 10% | 90th-percentile annual wage: $159,860 (2025, BLS OEWS, exact occupation 17-2112.00) | Official annual wages, not personal take-home pay | The 90th percentile is a wage-distribution threshold; it does not identify workers' education, experience, qualifications, or task mix. |
|---|
Wages · 10th percentile：10th-percentile annual wage: $74,370 (2025, BLS OEWS, exact occupation 17-2112.00)；Scope | U.S. national 10th-percentile annual wage for 17-2112.00 (2025, presented by O*NET). Limit | A lower-distribution threshold, not a starting salary or a figure representing any particular employer or region.
Wages · 25th percentile：25th-percentile annual wage: $83,600 (2025, BLS OEWS, exact occupation 17-2112.00)；Scope | U.S. 25th-percentile annual wage for this occupation (2025, presented by O*NET). Limit | The lower-quartile threshold； industry and region affect actual income.
Wages · Median：Median annual wage: $102,440 (2025, BLS OEWS, exact occupation 17-2112.00)；OEWS reports pretax regular wage rates, including incentive pay such as production bonuses, but excluding overtime premiums, shift differentials, nonproduction bonuses, and employer benefit costs. The median is the distribution midpoint, not a personal starting salary.
Wages · 75th percentile：75th-percentile annual wage: $129,250 (2025, BLS OEWS, exact occupation 17-2112.00)；Scope | U.S. 75th-percentile annual wage for this occupation (2025, presented by O*NET). Limit | The upper-quartile threshold, generally associated with industry, region, and specialist capabilities.
Wages · 90th percentile：90th-percentile annual wage: $159,860 (2025, BLS OEWS, exact occupation 17-2112.00)；Scope | U.S. 90th-percentile annual wage for this occupation (2025, presented by O*NET). Limit | An upper-distribution threshold, strongly influenced by industry, region, and responsibility scope； it does not represent most workers.
The median is the distribution midpoint, not entry-level pay; percentiles do not identify years of experience.
BLS OEWS2025, with national occupational wages presented by O*NET. OEWS reports pretax regular wage rates, including incentive pay such as production bonuses, but excluding overtime premiums, shift differentials, nonproduction bonuses, and employer benefit costs.
Editorial verification suggestion: check the target region, employer, actual duties, and offer definitions. This page has not estimated each condition's wage effect.
Verify wages, employment, and openings separately. Industry wages cover this occupation within that industry; national percentiles are not personal offers.
| Industry | Median wage for this occupation within the industry | How to interpret |
|---|---|---|
| Professional, scientific, and technical services | $107,610/year; 2025 median wage for SOC17-2112 within this industry | The median for this occupation within the industry, not the industry average across all occupations; fabricated metal product manufacturing is separately $93,170/year. |
| Computer and electronic product manufacturing | $105,370/year; 2025 median wage for SOC17-2112 within this industry | The median for this occupation within the industry, not the industry average across all occupations; fabricated metal product manufacturing is separately $93,170/year. |
| Transportation equipment manufacturing | $102,390/year; 2025 median wage for SOC17-2112 within this industry | The median for this occupation within the industry, not the industry average across all occupations; fabricated metal product manufacturing is separately $93,170/year. |
| Machinery manufacturing | $99,070/year; 2025 median wage for SOC17-2112 within this industry | The median for this occupation within the industry, not the industry average across all occupations; fabricated metal product manufacturing is separately $93,170/year. |
Employer type (manufacturers, engineering consultancies, professional services), industry and product category, region, and specialist experience affect income level and stability.
Specialist experience and responsibility for system design or quality management affect earning potential and progression.
Region and living costs, industry, and production cycles affect workload and income. Regional median wages were not obtained and are recorded as a visible gap.
Current period: current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI. The shared historical figures above have consumers outside the selected scope； the scope conflict is retained in this round.
Current period: current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI. The shared historical figures above have consumers outside the selected scope； the scope conflict is retained in this round.
Current period: current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI. The shared historical figures above have consumers outside the selected scope； the scope conflict is retained in this round.
O*NET occupational interest baseline
Interest code CIRO*NET 17-2112.00 has interest code CIR (Conventional–Investigative–Realistic).
Understanding this interest code: CIR combines Conventional (a preference for handling information and data through established procedures), Investigative (a preference for researching and analyzing problems), and Realistic (a preference for engineering practice and hands-on implementation). It suggests a combination of data procedures, inquiry, and engineering practice in the occupation, rather than labeling individuals.
Which Holland interest types are closer to industrial engineering?
O*NET 17-2112.00 has interest code CIR (Conventional–Investigative–Realistic). This assessment helps you explore preferences for data processing, investigation, and engineering practice.
Do not use it this way:The interest code indicates a direction, not ability or a hiring conclusion. A high preference score cannot replace practical systems-work experience.
Test my Holland career interestsWhich Big Five traits are more relevant to this work?
Current O*NET work styles include attention to detail, dependability, and intellectual curiosity. These offer behavioral observation cues, not evidence that Big Five measures predict performance in this occupation.
Do not use it this way:Work styles are descriptive cues. Practical task experience can help check preferences; no measured evidence of the extent of reduced fit was obtained.
View my Big Five personality traitsCan MBTI determine whether industrial engineering suits me?
No validation studies were obtained showing that MBTI types predict industrial engineering competence or performance. A skills list does not establish type validity; examine data-analysis and systems-tradeoff behavior.
Do not use it this way:The skills list indicates a direction, not your current abilities; test it against practical work evidence.
Understand my MBTI preferencesCan the Enneagram tell whether this occupation suits me?
No validation studies linking the Enneagram to performance in this occupation were obtained; work activities are not evidence of Enneagram fit.
Do not use it this way:Frequent activities do not imply monotonous work: together, these activities form the core of industrial engineering.
Explore my Enneagram patternsCan I become an industrial engineer if mathematics is not my strength?
This occupation involves statistics, models, engineering economics, and mathematics; BLS lists calculus and trigonometry, among other subjects. Editorial suggestion: assess foundations through data-analysis and model-assumption tasks. No universal IQ or mathematics-score hiring threshold was obtained; work context cannot establish a mathematics threshold.
Do not use it this way:Work-context percentages reflect respondents' reports and do not predict an individual's working conditions.
Understand my intelligence and reasoning performanceDo industrial engineers need high emotional intelligence?
Cross-functional consultation requires clearly explaining objectives, constraints, and proposals and listening to feedback. No universal emotional-intelligence score threshold or performance-validation studies were obtained.
Do not use it this way:This editorial self-assessment is not a hiring standard or ability score.
Understand my emotional intelligence abilitiesCloser fit:Fit signal: assess fit for industrial engineering (this occupation) through consistent completion of core operations in real tasks, turning anomalies and constraints into actionable issues, and accepting its methods and documentation requirements.
Note:People who struggle with multi-objective tradeoffs, cross-functional consultation, or overtime.
View related occupations:Industrial Engineers (this occupation)Closer fit:Fit signal: assess fit for manufacturing engineering through consistent completion of core operations in real tasks, turning anomalies and constraints into actionable issues, and accepting its methods and documentation requirements.
Note:People who prefer overall system design to process details.
View related occupations:Manufacturing EngineersCloser fit:Fit signal: assess fit for human factors engineering and ergonomics through consistent completion of core operations in real tasks, turning anomalies and constraints into actionable issues, and accepting its methods and documentation requirements.
Note:People unwilling to conduct specialist human factors assessments.
View related occupations:Human Factors Engineers and ErgonomistsCloser fit:Fit signal: assess fit for quality control systems management through consistent completion of core operations in real tasks, turning anomalies and constraints into actionable issues, and accepting its methods and documentation requirements.
Note:People who prefer engineering design to systems management.
View related occupations:Quality Control Systems ManagersCloser fit:Fit signal: assess fit for logistics engineering through consistent completion of core operations in real tasks, turning anomalies and constraints into actionable issues, and accepting its methods and documentation requirements.
Note:People who prefer to focus on production systems rather than logistics.
View related occupations:Logistics EngineersCloser fit:Fit signal: assess fit for project management specialist work through consistent completion of core operations in real tasks, turning anomalies and constraints into actionable issues, and accepting its methods and documentation requirements.
Note:People unwilling to take responsibility for project schedules and delivery.
View related occupations:Project Management SpecialistsNo personality label guarantees fit; observe rigorous analysis, objective tradeoffs, and sustained communication.
Introversion is not an exclusion criterion. This occupation requires cross-functional consultation; check whether you can explain models and proposals clearly.
Extraversion does not guarantee performance; examine analytical rigor, documentation, and multi-objective tradeoffs.
No validation studies establishing which MBTI type best suits this occupation were obtained.
O*NET interest code CIR: Conventional, Investigative, Realistic. Occupational interest descriptions do not establish personal competence.
No validation studies showing personality tests predict performance in this occupation were obtained; O*NET work styles do not establish the validity of individual tests.
This occupation involves statistics, models, engineering economics, and mathematics; BLS lists calculus and trigonometry, among other subjects. Editorial suggestion: assess foundations through data-analysis and model-assumption tasks. No universal IQ or mathematics-score hiring threshold was obtained; work context cannot establish a mathematics threshold.
Cross-functional consultation requires clearly explaining objectives, constraints, and proposals and listening to feedback. No universal emotional-intelligence score threshold or performance-validation studies were obtained.
No validation studies linking the Enneagram to performance in this occupation were obtained; work activities are not evidence of Enneagram fit.
Editorial suggestion: use simulated production data to explain system objectives, model validation, cost and quality constraints, and proposal selection. Compare preferences for overall systems versus process, human factors, or logistics specialisms. The exercise is not a hiring assessment.
Potential consequences：Safety risks or wasted investment
Reduce risk：Check safety and cost constraints before proposing changes and retain the reasoning behind tradeoffs
Basis：O*NET 17-2112.00 occupational profile and task list · FermatMind career content field and evidence-definition rules
Typical scenario：Data or model quality defects
Main roles：Industrial engineer; methodology lead
Potential consequences：Incorrect system judgments
Reduce risk：Apply data quality controls, validate models, and review results
Basis：O*NET 17-2112.00 occupational profile and task list · FermatMind career content field and evidence-definition rules
Typical scenario：Conclusions exceeding the evidence
Main roles：Industrial engineer; management
Potential consequences：Misguided decisions
Reduce risk：State conclusions within data and assumption limits and identify uncertainty
Basis：O*NET 17-2112.00 occupational profile and task list · FermatMind career content field and evidence-definition rules
Typical scenario：Inconsistent cross-functional definitions or assumptions
Main roles：Industrial engineer; project manager
Potential consequences：Conflicting conclusions or rework
Reduce risk：Align and document definitions and assumptions; conduct cross-functional reviews
Basis：O*NET 17-2112.00 occupational profile and task list · FermatMind career content field and evidence-definition rules
Typical scenario：Production and process information sent externally
Main roles：Industrial engineer; information security lead
Potential consequences：Data and business risks
Reduce risk：Follow organizational information requirements; confirm authorization and the minimum necessary scope before use
Basis：O*NET 17-2112.00 occupational profile and task list · FermatMind career content field and evidence-definition rules
Typical scenario：Making engineering or safety conclusions beyond one's authority
Main roles：Industrial engineer; supervisor
Potential consequences：Unauthorized conclusions create accountability issues
Reduce risk：Verify job authorization and approval responsibilities for actual engineering, quality and safety, and production-change tasks. Do not portray all industrial engineers as lacking judgment authority.
Basis：O*NET 17-2112.00 occupational profile and task list · FermatMind career content field and evidence-definition rules
| Career direction | Initial tasks | Evidence to prepare | Work samples you can create without experience | Difference from this occupation |
|---|---|---|---|---|
| Industrial Engineers (this occupation) | Analyze production data and assist process and system optimization under supervision | Engineering training and course records; analysis and project samples | A step-by-step systems analysis and proposal assessment | Centers on integrated production-system design and evaluation |
| Manufacturing Engineers (adjacent) | Assist with manufacturing process and production-method design | Process training records | A process proposal explanation | Produces manufacturing processes, with a narrower process focus |
| Human Factors Engineers and Ergonomists (adjacent) | Assist human factors and ergonomics assessments | Human factors methodology training records | A human factors assessment explanation | Focuses on human–system interaction and ergonomics assessment; cost factors cannot universally be excluded |
| Quality Control Systems Managers (adjacent) | Assist quality management systems and compliance work | Quality-management training records | A quality-system explanation | Focuses on quality-system management, with collaboration and task overlap in production-system design |
| Logistics Engineers (adjacent) | Assist logistics and supply-chain proposal analysis | Logistics training records | A logistics proposal explanation | Produces logistics and supply-chain systems, with a logistics focus |
In mainland China, check target postings for education, data analysis, modeling, and activity-specific requirements. No sample representing most roles was obtained.
Verify education, methods, and task abilities separately; no comparison of competence-prediction validity is available.
Relevant U.S. roles follow employer and project requirements. Mainland Chinese qualification and safety requirements depend on local rules and organizational procedures.
Handle and retain production data, process and equipment information, unpublished proposals, and costs only within the minimum necessary scope under organizational requirements.
Current O*NET work styles include attention to detail, dependability, and intellectual curiosity. Work-style descriptions are not individual scale validity or hiring standards.
Core skills include active listening, critical thinking, reading comprehension, and mathematics. Transferable skills include complex problem solving, systems analysis, systems evaluation, and coordination.
A simulated production and cost dataset with normal and abnormal examples.
A blank systems-analysis and cost-assessment template.
A blank checklist for proposal tradeoffs and side-effect assessment.
For the three supplied problem types—insufficient or inconsistently defined data, invalid model assumptions, and proposals conflicting with safety or cost requirements—describe identifying features and responses.
Identify matters beyond your personal authority: engineering judgments, safety conclusions, and compliance decisions.
For each problem, explain identification evidence and the consequences of your response; describe risks from compressed or omitted steps.
Finish with a reflection: if efficiency objectives conflict with safety requirements, how would you choose and explain your reasoning?
Does not involve real production lines, real equipment, or identifiable production data.
Mark whether each step was completed independently or under guidance.
Identify steps involving safety, quality, and cost that require following standards rather than deciding independently.
Record review comments, dates, and supporting grounds for later reflection.
Show the complete chain from objective definition through data analysis, tradeoffs, and recommendations, not only the conclusion.
Exclude identifiable information about real production lines or processes, and specify evidence and assumptions.
Explain which data or standards check tool outputs underwent before use; label illustrative numbers as examples.
| Stage | Potential checks | Observable evidence | Boundary note |
|---|---|---|---|
| Resume and initial screening | Industrial engineering project experience, data analysis and modeling skills, and implemented results | Project and analysis samples; outcome records | Self-assessment and course records cannot replace organizational assessment or job authorization |
| Interview and scenario questions | Systems analysis, proposal tradeoffs, and cross-functional communication | Scenario responses and proposal explanations | One scenario exercise cannot replace a comprehensive assessment of design and evaluation abilities |
| Systems and analysis assessment |
Record the quality, safety, cost, and data requirements you observe.
Identify the practical cost you find hardest to accept and decide whether it is manageable.
List key requirements under applicable procedures: quality standards and safety requirements, cost and capacity constraints, and data recording and review.
Write response and escalation plans for two typical risks: production information sent externally and unauthorized engineering conclusions.
Identify at least two deviations against engineering specifications and record corrective actions.
| Qualification or preparation item | Applicable population | Role in this occupation | Whether generally required | How to obtain it | Approximate investment | Limitations |
|---|---|---|---|---|---|---|
| Bachelor's degree or higher (industrial engineering, mechanical engineering, or a related field) | Entry and engineering roles | Check the specific role's education requirements; U.S. survey education percentages are listed separately | For mainland China, check the target posting | Complete relevant degree studies | According to program duration | A degree does not guarantee job authorization or design-decision authority |
Typical responsibilities：Analyze production data and assist process optimization under guidance.
Evidence for advancement：Submit analysis and optimization samples and obtain mentor feedback.
Qualification boundaries：Depends on organizational requirements; engineering and data foundations are needed.
Next step：Work toward independently completing systems analysis and proposal design.
Typical responsibilities：Independently perform systems analysis, proposal design, and cost-quality tradeoffs.
Evidence for advancement：Submit proposals and assessment records.
Qualification boundaries：Depends on organizational requirements; relevant knowledge is needed for safety and quality work.
Next step：Learn to lead complex systems projects.
Typical responsibilities：Lead complex systems projects and mentor newcomers.
Evidence for advancement：Submit complex cases and outcome records.
Qualification boundaries：Depends on organizational requirements; experience and specialist capabilities are usually needed.
Next step：Turn experience into reusable methods and standards.
Typical responsibilities：Oversee production-system strategy and engineering standards.
Evidence for advancement：Submit strategy and performance-assessment records.
Qualification boundaries：Determined by organizational governance requirements.
Next step：Contribute to organizational production and operations strategy.
Typical responsibilities：Assist with quality-data collection, organization, and analysis.
Evidence for advancement：Submit quality data and analysis records.
Qualification boundaries：Depends on organizational requirements; quality and statistical foundations are needed.
Next step：Learn to perform quality and reliability analysis independently.
Typical responsibilities：Establish quality and reliability objectives and analyze deviations.
Evidence for advancement：Submit quality objectives and analysis records.
Qualification boundaries：Depends on organizational and quality-system requirements.
Next step：Contribute to quality systems and improvement proposals.
Typical responsibilities：Lead reliability improvement and quality-system optimization.
Evidence for advancement：Submit improvement and validation records.
Qualification boundaries：Determined by organizational procedures and engineering collaboration requirements.
Typical responsibilities：Assist with organizing logistics and inventory data.
Evidence for advancement：Submit logistics data and data-organization records.
Qualification boundaries：Depends on organizational requirements; logistics and data foundations are needed.
Next step：Learn to perform logistics and inventory analysis independently.
Typical responsibilities：Analyze and optimize logistics, inventory, and material flow.
Evidence for advancement：Submit logistics analysis and proposal records.
Qualification boundaries：Depends on organizational requirements; production and logistics experience is usually needed.
Next step：Contribute to supply-chain system design and optimization.
Typical responsibilities：Lead supply-chain proposal design and cross-departmental coordination.
Evidence for advancement：Submit proposals and coordination records.
Qualification boundaries：Determined by organizational procedures and collaboration requirements.
Can analyze production data and assist process and system optimization under guidance, follow procedures, and accept feedback.
Can independently perform systems analysis, proposal design, and cost-quality tradeoffs and submit recommendations.
Can lead complex systems projects or cross-departmental improvements, mentor newcomers, and produce verifiable results.
Takes organizational responsibility for production-system strategy, engineering standards, and performance.
United States · Exact occupation 17-2112.00
23,100/year
Average annual job openings
Overall employment projections do not establish AI causation. Growth measures total employment change; openings include growth and replacement needs and are not net new jobs.
Does not establish：National projections do not equal actual hiring or individual employment outcomes; distinguish growth from replacement openings.
Mainland China · No exact occupational measure available; only industry-category average wages (industry proxy)
Insufficient evidence
Mainland Chinese occupational wages
The National Bureau of Statistics publishes average wages by industry category without distinguishing occupations; these data cannot produce occupational-level figures for industrial engineers.
Does not establish：Industry proxy; no substitution with industry averages, recruitment offers, or U.S. data.
These are skill-adjacency relationships, not automatic promotion or a way to avoid additional preparation.
Transferable skills：Production and process foundations.
Skills to develop：To develop: manufacturing processes and engineering methods.
View career detailsTransferable skills：Systems and data foundations.
Skills to develop：To develop: human factors and ergonomics assessment methods.
View career detailsTransferable skills：Production-system and process capabilities.
Skills to develop：To develop: production management and team leadership.
View career detailsTransferable skills：Quality and data foundations.
Skills to develop：To develop: quality systems and compliance management.
View career detailsTransferable skills：Engineering and project capabilities.
Skills to develop：To develop: engineering management and governance experience.
View career detailsTransferable skills：Logistics and inventory foundations.
Skills to develop：To develop: logistics and supply-chain system design.
View career detailsTransferable skills：Project coordination and communication skills.
Skills to develop：To develop: project management methods and delivery management.
View career detailsTransferable skills：Site and data capabilities.
Skills to develop：To develop: site measurement and equipment operations.
View career detailsBLS OEWS 2025, presented by O*NET on the 17-2112.00 page for the exact occupation: median annual wage $102,440 and median hourly wage $49.25; annual wages at percentiles 10 / 25 / 75 / 90 are $74,370 / 83,600 / 129,250 / 159,860. The 2024–2034 employment outlook projects much faster-than-average growth of 11%, about 25,200 annual openings, about 351,100 people employed in 2024, and about 389,600 in 2034. All figures cover the exact 17-2112.00 occupation, designated Bright Outlook.
No national wage figure can be read precisely for this occupation. The National Bureau of Statistics publishes average wages for employees in urban units by industry category without distinguishing occupations, so these data cannot produce occupational-level figures for industrial engineers. This page records the gap under its evidence policy rather than substituting figures.
O*NET work-context figures show daily email use for 93%, daily face-to-face discussions for 88%, daily work in environmentally controlled indoor settings for 83%, workweeks exceeding 40 hours for 80%, daily telephone conversations for 73%, and teamwork rated extremely important by 60%. Main risks include undetected conflicts between proposals and safety or cost requirements, data or model quality defects, conclusions exceeding the evidence, inconsistent cross-functional definitions or assumptions, production and process information sent externally, and unauthorized engineering or safety conclusions.
It will affect information-processing tasks rather than replace system design, proposal tradeoffs, and engineering judgment. Generative AI can assist data processing and statistics, code and models, report and chart drafting, and research. People remain responsible for defining system objectives, assessing proposal safety and cost tradeoffs, cross-functional consultation, and standards development. This page provides no replacement probability, adoption rate, or timetable.
O*NET places this occupation (17-2112.00) in Job Zone Four (considerable preparation needed). Its education survey reports 59% requiring a bachelor's degree, 16% some college without a degree, and 11% a master's degree; several years of related experience or on-the-job training are typically needed. Related mainland Chinese roles generally require a degree in industrial engineering, mechanical engineering, or a related field, plus data-analysis and systems-modeling capabilities.
This page does not invent a universal certificate list. Education, training, engineering or safety qualifications, job authorization, authority over design conclusions, and production-change authority are distinct statuses. Training does not equal certification, and certification does not confer authority for design decisions, safety determinations, or production changes. In mainland China, current regulations and competent-authority requirements prevail.
Main risks include undetected conflicts between proposals and safety or cost requirements, data or model quality defects, conclusions exceeding the evidence, inconsistent cross-functional definitions or assumptions, production and process information sent externally, and unauthorized engineering or safety conclusions. Priorities are traceable records of data, assumptions, and review, with appropriate wording and escalation when uncertainty or authority limits arise.
For the exact U.S. occupation 17-2112.00, 2024–2034 projections indicate much faster-than-average growth of 11% and about 25,200 annual openings; the occupation is designated Bright Outlook. The mainland Chinese wage gap is recorded. Potential transitions include manufacturing engineering, human factors and ergonomics, quality systems, logistics and supply chains, architectural and engineering management, and project management. Each requires relevant additional training or qualifications; transferable skills do not mean automatic promotion.
This round checked the SOC17-2112 definition, tasks, CIR, education, and 9 work-context items; all 2025 wage percentiles and industry medians were verified. Current BLS 2025–2035 projections for the exact occupation SOC17-2112: employment rises from 365,100 to 410,600, growth of 12%, with 23,100 annual openings. Openings include growth and replacement needs, not net new jobs or actual hiring, and do not establish causation by generative AI. Chinese NBS data provide conditional industry references.
This round, on 2026-10-11, checked O*NET Updated2026, all 2025 wage percentiles and industry medians, 2025–2035 projections, NBS2025, and OEWS definitions. Shared older-period metadata retain a scope conflict.
Source card | Source register: this page lists O*NET 17-2112.00 occupational profile, details, national wages, industry distribution, hot technologies, certification directory, and national training entries; BLS Occupational Employment and Wage Statistics (OEWS 2025), employment projections, and methodology; National Bureau of Statistics average annual wages of employees in urban units; the Work Safety Law, Product Quality Law, and Personal Information Protection Law of the People's Republic of China; the ILO generative AI occupational exposure index; the NIST Artificial Intelligence Risk Management Framework; and entries for adjacent occupations such as manufacturing engineers, human factors engineers and ergonomists, and logistics engineers.
Usage limitations：U.S. occupational description; does not cover mainland Chinese qualification and safety systems.
Usage limitations：Exact occupational scope; not an individual employment conclusion.
Usage limitations：Exact occupational scope; cannot be attributed to generative AI.
Usage limitations：The original methodology document prevails.
Usage limitations：Industry-category data without occupational distinctions; this occupation is not separately reported.
Usage limitations：Current legal texts and competent-authority requirements prevail.
Usage limitations：Original legal texts and competent-department interpretations prevail.
Usage limitations：Current legal texts and competent-authority requirements prevail.
Usage limitations：U.S. industry distribution; does not establish a mainland Chinese mapping.
Usage limitations：Occupational-level description, not an engineering standard or safety code.
Usage limitations：The certification directory does not replace organizational and competent-authority requirements for job authorization and engineering licensing.
Usage limitations：Task exposure does not equal automatic job disappearance or a specific adoption rate.
Usage limitations：Internal definition rules, not an external factual source.
Usage limitations：Not an engineering standard or safety code.
Usage limitations：Technology mentions do not establish ability, adoption, or automation levels.
Usage limitations：A training search entry point does not replace education and qualification requirements.
Usage limitations：Editorial design, not universal employer requirements or hiring standards.
Usage limitations：Occupational-level description, not a prediction of an individual's work.
Usage limitations：Adjacent occupational scope, not this occupation.
Usage limitations：Adjacent occupational scope, not this occupation.
Usage limitations：Adjacent occupational scope, not this occupation.
Usage limitations：Database and classification information do not establish authorization.
Usage limitations：Maps titles to SOC; does not establish licensing authority.
Usage limitations：Exact occupational scope; excludes benefits and bonuses; percentiles do not equal starting salaries or experience levels.
Usage limitations：Employment trends cover the exact U.S. occupation; this page's employment outlook uses the same scope.
Usage limitations：Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
U.S. wages: 2025; current projections: 2025–2035; exact SOC17-2112 scope. Shared older-period material is retained for protected consumers. Exact mainland Chinese occupational wages, measured AI effects, and personality-test validity were not obtained.
Big Five personality · MBTI · Intelligence and reasoning · Emotional intelligence
| Human factors engineers and ergonomists focus on human factors and ergonomics assessments and generally do not take responsibility for overall integrated production-system design and cost analysis. |
| Core process | Confirm objectives—collect data—model and analyze—assess tradeoffs—develop standards and recommendations—communicate and implement, working alongside production and quality teams. | Quality control systems managers focus on quality management systems and compliance and generally do not take responsibility for production-system engineering design and cost analysis. |
|---|
| Main outputs | Production and material-flow data, cost and capacity analyses, system models and validation records, process and standards documents, and improvement recommendations and implementation records. | Logistics engineers focus on logistics and supply-chain systems; their work cannot be treated as interchangeable with this occupation's integrated production-system design. |
|---|
| Responsible parties | Take responsibility for the accuracy of your system design, data, and analytical conclusions, and escalate or refer safety and compliance conclusions beyond your authority under organizational procedures. | Project management specialists focus on project schedules and delivery management and generally do not take responsibility for production-system engineering design and analytical execution. |
|---|
| Authority and approval | Engineering judgments, safety conclusions, and compliance-related decisions must be made by qualified personnel or competent authorities under organizational procedures. | You must not independently make decisions beyond your professional competence or assigned responsibilities. |
|---|
| Limits of conclusions | This page does not promise recruitment, licensing, or hiring outcomes at any organization. | Qualification and safety systems differ between the United States and mainland China. Check them separately; they cannot be applied interchangeably. |
|---|
Basis: organizational safety and quality procedures.
Basis: organizational occupational health and protection requirements.
Entry routes commonly include manufacturers, engineering consultancies, and professional-service organizations; project work and cross-functional collaboration are common.
Mainland Chinese roles vary by industry, product category, and region; performance criteria depend on organizational announcements.
| Modeling, cost and quality analysis, and proposal design |
| Analysis and proposal samples |
| A single assessment does not establish the ability to independently lead system design and implementation |
| Onboarding and probation | Following engineering and safety procedures, cross-functional collaboration, and documentation | Project records and review and change histories | Authority over design conclusions and production changes depends on organizational procedures and announcements |
|---|
Record their objections and your responses; address constraints and counterexamples directly.
Use this to decide whether to continue pursuing the direction and write a specific question to test next.
| Data analysis and modeling training |
|---|
| Analytical roles |
| Provides foundations in statistics, modeling, and optimization |
| Relatively common |
| Take courses or practice through projects |
| Insufficient evidence: specific course durations and fees have not been verified |
| Training does not replace method selection or responsibility for conclusions |
| Engineering economics and cost-analysis training | System-design roles | Supports cost and investment analysis | Relatively common | Coursework or practical project experience | Insufficient evidence: specific course durations and fees have not been verified | Analysis does not replace financial or investment decisions |
|---|
| Quality and safety knowledge | Production-system roles | Supports judgment about quality standards and safety constraints | Relatively common | Coursework or collaboration with quality and safety teams | Insufficient evidence: specific course durations and fees have not been verified | Knowledge does not replace safety determinations or compliance conclusions |
|---|
| Engineering or safety qualifications (depending on industry requirements) | Roles involving safety or quality systems | Check specific industry, project, and activity requirements; an occupation title does not imply a universal certificate | Depends on industry and role | Check the responsible authority, conditions, and applicable population against actual licensing or operational requirements | According to assessment arrangements | Certification does not confer permission for design decisions or production changes |
|---|
| Project management training | Project leadership roles | Improves project coordination and delivery capabilities | Depends on the role | Attend training or practice through projects | Insufficient evidence: specific course durations and fees have not been verified | Training does not replace management responsibility |
|---|
Next step：Advance quality and reliability standards.
Typical responsibilities：Oversee quality and reliability systems.
Evidence for advancement：Submit system and assessment records.
Qualification boundaries：Determined by organizational governance requirements.
Next step：Contribute to organizational quality strategy.
Next step：Advance supply-chain and operations-system improvements.
Typical responsibilities：Oversee supply-chain and logistics strategy.
Evidence for advancement：Submit strategy and assessment records.
Qualification boundaries：Determined by organizational governance requirements.
Next step：Contribute to organizational supply-chain strategy.
