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Key statistics

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

3 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

11-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Manages the materials team responsible for inventory, kitting, shipping, and spare parts provisioning. At the Senior Manager (M3) level, the role owns multiple teams or a sub-function, multi-team execution and resourcing trade-offs, and drives sub-function outcomes. Manages managers and/or several teams.

What this level does

Manage the materials function for a department or site, owning inventory performance, MRP integrity, and the operating budget for materials planning
Evaluate inventory and supplier-performance trends across diverse issues to drive optimization projects that improve material flow, quality, and productivity
Build and manage key supplier relationships, aligning firm-order commitments and lead times with production requirements
Work with engineering and product development to assess how material changes affect production, coordinating cross-functional teams to implement them
May lead other supervisors or cross-functional professionals within materials management

Knowledge applied: Applies full-department materials knowledge to manage inventory performance, MRP integrity, and operating budgets.
Complexity & problem solving: Addresses diverse materials issues, evaluating inventory and supplier-performance trends to drive optimization projects.
Collaboration & interaction: Leads functional materials teams and engages engineering, product development, and key suppliers.
Typical experience: 5–7+ years managing materials professionals and budgets.

Skills at this level

Inventory Management: — Manages inventory levels, planning schedules, and materials flow to maintain steady production, adjusting reorder points and safety stock to prevent stockouts and overstock.
Materials Requirements Planning (MRP): — Analyzes and adjusts the Materials Requirements Plan to firm orders with suppliers, aligning material availability with production build schedules.
Inventory Data Analysis: — Monitors and analyzes inventory data to identify trends, investigate discrepancies among demand, supply, and inventory, and recommend improvements to material flow.
Supply Chain Data Analysis / BI: — Uses Power BI, Tableau, SQL, or Python to interrogate supply-chain and inventory data, producing the analysis that drives materials optimization decisions.
Supplier Relationship Management: — Builds and manages key supplier relationships, aligning firm-order commitments, lead times, and vendor performance with production requirements.
Supply Chain Disruption Management: — Responds to unexpected disruptions such as natural disasters or geopolitical events by quickly finding alternative suppliers and managing stakeholder expectations.
Cross-Functional Materials Integration: — Interfaces across Procurement, Supply Chain Planning, Suppliers, R&D, and Logistics to align materials management with strategic objectives and keep flow uninterrupted.
Materials Strategy & Policy: — Formulates materials policies, plans resource use, and develops materials strategies spanning the procurement-to-distribution lifecycle to ensure supply chain resilience.
Engineering Change Impact Assessment: — Works with engineering and product development to determine how material changes affect production of a product, coordinating implementation to protect quality and productivity.
Manufacturing & Supply Chain Knowledge: — Applies knowledge of manufacturing processes and end-to-end supply chain management to keep materials aligned with production requirements.
Strategic Sourcing & Risk Management: — Develops sourcing strategies and manages supply risk, including navigating global supply chain regulations, international logistics, and geopolitical exposure.
Supply Chain Digital Transformation: — Leads innovation and digital transformation initiatives across the materials organization to modernize planning, inventory, and materials-flow systems.

Model-authored from 24 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 11-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+61.3%Transportation, Storage, and Distribution Managers · United States

132,040 → 213,000 jobs

18,500 openings / year, split into:

replacement / exits 17,178

Growth — new jobs (7%)Exits — backfill (93%)

7% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +6.1%
Growth share of hiring: 7%
Exit / retention pressure high: 93%
Openings / year: 18,500

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 M3 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$207k

San Francisco-Oakland-Fremont, CA$186k

Washington-Arlington-Alexandria, DC-VA-MD-WV$172k

Boulder, CO$169k

Seattle-Tacoma-Bellevue, WA$169k

Boston-Cambridge-Newton, MA-NH$162k

Lexington Park, MD$158k

Trenton-Princeton, NJ$157k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $124k; 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 M3 (M3 — Senior Manager) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Multiple teams or a sub-function
Autonomy: Sets goals within functional strategy
Complexity: Multi-team execution and resourcing trade-offs
Impact: Sub-function outcomes
Decision rights: Owns goals, budget input, and people decisions across teams
Leadership: Manages managers and/or several teams

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

Promotion

The next rung from M3 is M4.

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 Coordinator	jf_materials::P3	$85,090	confident0.06
Senior Materials Coordinator	jf_materials::S3	$51,142	confident0.06
Senior Supervisor, Materials	jf_materials::M2	$113,967	confident0.06

Capability cluster

O*NET 11-3071

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

M3

jf_materials::M3

Materials Manager

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: M
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 · M3review0.17

Distribution Center / Warehouse · M3review0.17

Purchasing / Procurement · M3review0.17

Transportation · M3review0.17

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 11-3071	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)