Logistics Engineers
Business and Financial Operations Occupations- O*NET-SOC コード
- 13-1081.01
Design or analyze operational solutions for projects such as transportation optimization, network modeling, process and methods analysis, cost containment, capacity enhancement, routing and shipment optimization, or information management.
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
0.157
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
0.46
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 30 件 · β ≥ 0.5 のタスク 30 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） |
|---|---|---|
| Propose logistics solutions for customers. | コア | 0.5 |
| Interview key staff or tour facilities to identify efficiency-improvement, cost-reduction, or service-delivery opportunities. | コア | 0.5 |
| Direct the work of logistics analysts. | コア | 0.5 |
| Design plant distribution centers. | コア | 0.5 |
| Develop specifications for equipment, tools, facility layouts, or material-handling systems. | コア | 0.5 |
| Review contractual commitments, customer specifications, or related information to determine logistics or support requirements. | コア | 0.5 |
| Prepare or validate documentation on automated logistics or maintenance-data reporting or management information systems. | コア | 1 |
| Identify or develop business rules or standard operating procedures to streamline operating processes. | コア | 0.5 |
| Develop or maintain cost estimates, forecasts, or cost models. | コア | 0.5 |
| Determine feasibility of designing new facilities or modifying existing facilities, based on factors such as cost, available space, schedule, technical requirements, or ergonomics. | コア | 0.5 |
| Determine logistics support requirements, such as facility details, staffing needs, or safety or maintenance plans. | コア | 0.5 |
| Conduct logistics studies or analyses, such as time studies, zero-base analyses, rate analyses, network analyses, flow-path analyses, or supply chain analyses. | コア | 0.5 |
| Analyze or interpret logistics data involving customer service, forecasting, procurement, manufacturing, inventory, transportation, or warehousing. | コア | 0.5 |
| Provide logistics technology or information for effective and efficient support of product, equipment, or system manufacturing or service. | コア | 0.5 |
| Evaluate effectiveness of current or future logistical processes. | コア | 0.5 |
| Apply logistics modeling techniques to address issues, such as operational process improvement or facility design or layout. | コア | 0.5 |
| Evaluate the use of inventory tracking technology, Web-based warehousing software, or intelligent conveyor systems to maximize plant or distribution center efficiency. | コア | 0.5 |
| Develop logistic metrics, internal analysis tools, or key performance indicators for business units. | コア | 0.5 |
| Identify cost-reduction or process-improvement logistic opportunities. | コア | 0.5 |
| Evaluate the use of technologies, such as global positioning systems (GPS), radio-frequency identification (RFID), route navigation software, or satellite linkup systems, to improve transportation efficiency. | コア | 0.5 |
| Prepare logistic strategies or conceptual designs for production facilities. | コア | 0.5 |
| Design comprehensive supply chains that minimize environmental impacts or costs. | コア | 0.5 |
| Develop or document reverse logistics management processes to ensure maximal efficiency of product recycling, reuse, or final disposal. | コア | 0.5 |
| Conduct environmental audits for logistics activities, such as storage, distribution, or transportation. | コア | 0.5 |
| Create models or scenarios to predict the impact of changing circumstances, such as fuel costs, road pricing, energy taxes, or carbon emissions legislation. | コア | 0.5 |
| Review global, national, or regional transportation or logistics reports for ways to improve efficiency or minimize the environmental impact of logistics activities. | コア | 0.5 |
| Determine requirements for compliance with environmental certification standards. | コア | 0.5 |
| Provide logistical facility or capacity planning analyses for distribution or transportation functions. | コア | 0.5 |
| Develop or document procedures to minimize or mitigate carbon output resulting from the movement of materials or products. | コア | 0.5 |
| Assess the environmental impact or energy efficiency of logistics activities, using carbon mitigation software. | 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: 13-1081.01 Logistics 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.