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Key statistics

The identity block — every fact is canon, none is generated.

3 of 8

Track

Individual contributor

Family rungs priced

P1 · P2 · P3 · P4 · P5 · P6

13-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Manages day-to-day inventory operations, including kitting, shipping, and spare parts provisioning, with full ownership of assigned processes. At the Mid-Level Professional (P3) level, the role owns features or a sub-system end-to-end, adapts existing approaches, and drives project / team outcomes. Mentors juniors informally.

What this level does

Analyzes material supply chains to identify inefficiencies and independently recommends areas for improvement.
Manages material requirements across diverse product lines, planning day-to-day inventory work with milestone review.
Ensures capacity settings for MAKE materials are current and accurate against evolving production schedules.
Coordinates material planning activities with production, procurement, and logistics, networking with senior professionals to resolve supply constraints.
Interprets complex demand and consumption data to adjust forecasts and reorder parameters.

Knowledge applied: Applies data and statistical analysis to interpret consumption and demand data across diverse material lines with independence.
Complexity & problem solving: Evaluates identifiable factors — capacity settings, lead times, supply constraints — to improve material flow.
Collaboration & interaction: Networks with senior professionals across production, procurement, and logistics; may coordinate planning activities.
Typical experience: 5+ years (BA), 3 years (MA), or PhD without experience.

Skills at this level

Material Requirements Forecasting: — Analyzes and forecasts material requirements by translating production schedules and business demand into procurement and inventory plans.
Inventory Level Management: — Sets and maintains stock levels to meet production and customer demand while preventing overstocking and excess carrying cost.
Inventory Auditing & Reconciliation: — Conducts physical and cyclical audits of material inventories, reporting stock levels and investigating and reconciling discrepancies.
MAKE Material Capacity Setting: — Maintains current and accurate capacity settings for MAKE materials so planning reflects true production capability.
Supplier Coordination & Relationship Building: — Coordinates timely material acquisition with suppliers and builds key supplier relationships to secure continuity and mitigate shortage risk.
Supply Chain & Logistics Fundamentals: — Applies knowledge of supply chain processes, goods distribution, and order handling to move materials through the make/build flow.
Data and Statistical Analysis: — Conducts data and statistical analysis to interpret complex consumption and demand data and secure materials that meet demand at quality.
Supply Chain Optimization: — Analyzes material supply chains to identify inefficiencies and develops projects that improve flow, cost, and availability.
Master Production Scheduling Policy: — Establishes policies and procedures that translate customer demand into executable master production schedules within operational capacity limits.
Materials Supply Risk Management: — Leads initiatives to identify and mitigate material supply risks, including geopolitical and multi-tier disruption exposure.
Global Supply Chain Regulations Expertise: — Applies knowledge of global supply chain regulations and international logistics to govern cross-border material movement at the top of the ladder.
Materials Strategy Leadership: — Develops and directs enterprise materials strategy aligning inventory performance and supply resilience with organizational goals across a business unit or region.

Model-authored from 20 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Materials — median with , by . Real canon data.

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 13-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+29.4%Logisticians · United States

182,050 → 235,640 jobs

26,400 openings / year, split into:

growth 4,025

replacement / exits 22,375

Growth — new jobs (15%)Exits — backfill (85%)

15% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +16.7%
Growth share of hiring: 15%
Exit / retention pressure moderate: 85%
Openings / year: 26,400

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level P3 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$142k

San Francisco-Oakland-Fremont, CA$127k

Washington-Arlington-Alexandria, DC-VA-MD-WV$118k

Boulder, CO$116k

Seattle-Tacoma-Bellevue, WA$116k

Boston-Cambridge-Newton, MA-NH$111k

Lexington Park, MD$108k

Trenton-Princeton, NJ$108k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $85k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates P3 (P3 — Mid-Level Professional) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Features or a sub-system end-to-end
Autonomy: Works independently on standard work; reviewed on the non-standard
Complexity: Diverse problems; adapts existing approaches
Impact: Project / team outcomes
Decision rights: Owns implementation decisions for own scope
Leadership: Mentors juniors informally

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

The next rung from P3 is P4.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
Chief Materials Officer	jf_materials::E3	$363,257	confident0.06
Materials Manager	jf_materials::M3	$124,177	confident0.06
Senior Materials Coordinator	jf_materials::S3	$51,142	confident0.06
Materials Coordinator	jf_materials::P2	$74,658	confident0.06

Capability cluster

O*NET 13-1081

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

P3

jf_materials::P3

Materials Coordinator

Materials

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: P
Superfunction: manufacturing
Function: supply_chain
Family: materials
Universal level: 3 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Demand / Forecast Planning · P3review0.17

Distribution Center / Warehouse · P3review0.17

Purchasing / Procurement · P3review0.17

Transportation · P3review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 13-1081	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)