Industrial Engineers
Architecture and Engineering Occupations- O*NET-SOC コード
- 17-2112.00
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.
職業名とタスク記述は、公表された英語のまま表示します。ラベル（タスクの種類を含む）は翻訳しています。
AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.037
0.000ここに掲載された値の範囲0.745職業群単位の値
尺度・母数・出典
観測エクスポージャー指数、公開されたまま0–1
O*NETタスクへの対応づけが基準
SOC 2018の職業単位で公表された値で、同じSOC 2018コードを持つO*NET職業はすべてこの値になります
- データ出典: ILO公表時点: 2025
0.37
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の細分類（4桁）単位で公表された値です。米国労働統計局（BLS）の公式対応表（ISCO-08→2010 SOC、2010 SOC→2018 SOC）を公表どおり適用してこの職業に結び付けており、対応は全部または一部です
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
AI 曝露度（OpenAI ルーブリック）
評価済みタスク 20 件 · β ≥ 0.5 のタスク 20 件（100.0%）
β = 直接的な露出（E1）+ 0.5 × ツール利用時の露出（E2）。原典リポジトリの定義に従います。
- 原典の単位：O*NET 27.2 のタスク → O*NET 31.0 の職業コード
- 評価済みタスクはすべて O*NET 31.0 のタスク一覧にあります。
- 出典
- OpenAI "GPTs are GPTs" exposure rubric
- 版
- gh-main-0471612
- ライセンス
- MIT License, Copyright (c) 2024 OpenAI
タスク
O*NET® 31.0 Database のタスク記述です。コアタスクを先に表示します。
| タスク | 種類 | β（OpenAI） |
|---|---|---|
| Analyze statistical data and product specifications to determine standards and establish quality and reliability objectives of finished product. | コア | 0.5 |
| Develop manufacturing methods, labor utilization standards, and cost analysis systems to promote efficient staff and facility utilization. | コア | 0.5 |
| Recommend methods for improving utilization of personnel, material, and utilities. | コア | 0.5 |
| Plan and establish sequence of operations to fabricate and assemble parts or products and to promote efficient utilization. | コア | 0.5 |
| Apply statistical methods and perform mathematical calculations to determine manufacturing processes, staff requirements, and production standards. | コア | 0.5 |
| Draft and design layout of equipment, materials, and workspace to illustrate maximum efficiency using drafting tools and computer. | コア | 0.5 |
| Review production schedules, engineering specifications, orders, and related information to obtain knowledge of manufacturing methods, procedures, and activities. | コア | 0.5 |
| Communicate with management and user personnel to develop production and design standards. | コア | 0.5 |
| Formulate sampling procedures and designs and develop forms and instructions for recording, evaluating, and reporting quality and reliability data. | コア | 0.5 |
| Record or oversee recording of information to ensure currency of engineering drawings and documentation of production problems. | コア | 0.5 |
| Study operations sequence, material flow, functional statements, organization charts, and project information to determine worker functions and responsibilities. | コア | 0.5 |
| Direct workers engaged in product measurement, inspection, and testing activities to ensure quality control and reliability. | コア | 0.5 |
| Implement methods and procedures for disposition of discrepant material and defective or damaged parts, and assess cost and responsibility. | コア | 0.5 |
| Evaluate precision and accuracy of production and testing equipment and engineering drawings to formulate corrective action plan. | コア | 0.5 |
| Complete production reports, purchase orders, and material, tool, and equipment lists. | コア | 1 |
| Estimate production costs, cost saving methods, and the effects of product design changes on expenditures for management review, action, and control. | コア | 0.5 |
| Coordinate and implement quality control objectives, activities, or procedures to resolve production problems, maximize product reliability, or minimize costs. | コア | 0.5 |
| Confer with clients, vendors, staff, and management personnel regarding purchases, product and production specifications, manufacturing capabilities, or project status. | コア | 0.5 |
| Schedule deliveries based on production forecasts, material substitutions, storage and handling facilities, and maintenance requirements. | 補足 | 0.5 |
| Regulate and alter workflow schedules according to established manufacturing sequences and lead times to expedite production operations. | 補足 | 0.5 |
職業情報
出典と帰属表示
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 17-2112.00 Industrial Engineers
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.