# Logistics Engineers

> 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.

- **SOC code:** 13-1081.01
- **Canonical URL:** https://singulariki.com/roles/role-13-1081-01
- **Also known as:** Logistics Engineer, Reliability Engineer, Supportability Engineer, Systems Engineer, Acquisition Logistics Engineer, Logistics Specialist, Aero Logistics Engineer (Aeronautical Logistics Engineer), Auto Logistics Engineer (Automotive Logistics Engineer)
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Identify cost-reduction or process-improvement logistic opportunities.
- Analyze or interpret logistics data involving customer service, forecasting, procurement, manufacturing, inventory, transportation, or warehousing.
- Prepare logistic strategies or conceptual designs for production facilities.
- Conduct logistics studies or analyses, such as time studies, zero-base analyses, rate analyses, network analyses, flow-path analyses, or supply chain analyses.
- Develop logistic metrics, internal analysis tools, or key performance indicators for business units.
- Identify or develop business rules or standard operating procedures to streamline operating processes.
- Interview key staff or tour facilities to identify efficiency-improvement, cost-reduction, or service-delivery opportunities.
- Design plant distribution centers.
- Apply logistics modeling techniques to address issues, such as operational process improvement or facility design or layout.
- Review contractual commitments, customer specifications, or related information to determine logistics or support requirements.
- Evaluate the use of inventory tracking technology, Web-based warehousing software, or intelligent conveyor systems to maximize plant or distribution center efficiency.
- Propose logistics solutions for customers.

**Emerging tasks** (O*NET):
- Evaluate the use of technologies, such as global positioning systems (GPS), radio-frequency identification (RFID), route navigation software, drone or robotic technology, or satellite linkup systems, to improve transportation efficiency.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Systems Analysis _(transferable_skill)_
- Engineering and Technology _(knowledge)_
- Writing _(essential_skill)_
- Complex Problem Solving _(transferable_skill)_
- Systems Evaluation _(transferable_skill)_
- Written Comprehension _(ability)_
- Mathematical Reasoning _(ability)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Mathematics _(essential_skill)_
- Active Learning _(essential_skill)_
- Judgment and Decision Making _(transferable_skill)_

**Skills in demand:**
- Systems Analysis _(Specialized Skill)_
- Writing _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Mathematics _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Active Learning _(Common Skill)_
- Time Management _(Common Skill)_
- Critical Thinking _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- SAP software _(hot technology, in demand)_
- Autodesk AutoCAD _(hot technology)_
- C++ _(hot technology)_
- Microsoft Access _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Power BI _(hot technology)_
- Microsoft Project _(hot technology)_
- Microsoft SharePoint _(hot technology)_
- Microsoft SQL Server _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 81st percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 87th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 95th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 55th percentile (Moderate) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 8th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 16.7% growth (Growing fast); 26.4k annual openings; 241k → 281.3k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $80,880; 235,640 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 41% automation, 52% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** task iteration.

**Tasks most handed to AI here:**
- Develop or maintain cost estimates, forecasts, or cost models. _(2.5% of measured AI use; task iteration)_
- Determine feasibility of designing new facilities or modifying existing facilities, based on factors such as cost, available space, schedule, technical requirements, or ergonomics. _(0.8% of measured AI use; task iteration)_
- Analyze or interpret logistics data involving customer service, forecasting, procurement, manufacturing, inventory, transportation, or warehousing. _(0.5% of measured AI use; directive)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me develop or maintain cost estimates, forecasts, or cost models.
- Help me determine feasibility of designing new facilities or modifying existing facilities, based on factors such as cost, available space, schedule, technical requirements, or ergonomics.
- Help me analyze or interpret logistics data involving customer service, forecasting, procurement, manufacturing, inventory, transportation, or warehousing.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-13-1081-01_