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Key statistics

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

1 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

11-0000

corporate_services

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Frontline supervisor overseeing daily non-manufacturing operations staff and workflows. At the Manager (Team Lead) (M1) level, the role owns a single team, day-to-day delivery and people issues, and drives team output and health. Direct people management of one team.

What this level does

Supervises the day-to-day operational tasks of a single unit, allocating work, setting deadlines, and monitoring individual and team performance
Acts as the bridge between frontline employees and upper management within a specific functional area such as logistics, quality control, or supply chain
Implements and maintains established processes that help the team meet defined business objectives
Coordinates workforce scheduling and task assignment to keep operational workflows running against short-term goals

Knowledge applied: Functional expert in a single operational area (logistics, quality, supply chain) who applies established practices to run a unit; leadership exposure is emerging.
Complexity & problem solving: Limited scope; solves routine operational problems within established procedures and known factors.
Collaboration & interaction: Daily interactions with unit staff and peer supervisors to coordinate task flow.
Typical experience: Functional expertise with some initial leadership or lead exposure.

Skills at this level

Operational Workflow Management: — Oversees and optimizes day-to-day operational workflows, allocating tasks, setting deadlines, and monitoring performance to ensure work is completed on time, within scope, and aligned with business objectives.
Operational Budget Management: — Manages operational budgets to ensure efficient resource utilization, formulates cost-aligned objectives, and analyzes financial data to identify opportunities for improving profitability.
Cross-Functional Collaboration: — Works across sales, compliance, logistics, and peer supervisory teams to coordinate interdependent operations, using clear written and verbal communication to keep initiatives aligned.
KPI Tracking and Reporting: — Tracks and reports key operational performance indicators, surfacing deviations and translating operational data into updates for department leadership or executives.
Process Improvement: — Identifies and implements improvements to operational workflows and quality-control measures so products or services consistently meet or exceed expectations.
Data and Analytics for Operations: — Applies data analysis and mid-to-expert Excel capability to interpret operational and financial data and make informed recommendations to senior leadership.
Resource Allocation: — Allocates workforce, vendor, and budget resources across teams and departments to meet operational objectives while controlling cost and quality.
Team Leadership and Talent Development: — Leads, mentors, and evaluates operational teams and managers, partnering with HR to recruit, train, and retain talent and foster a culture of accountability and continuous learning.
Operational Strategy Development: — Develops and executes operations-related strategies and processes across departments, aligning operational execution with broader organizational goals.
Digital Transformation: — Advances digital transformation and innovation across operational functions to improve workflow efficiency, scalability, and competitiveness beyond established practice.
Strategic Planning and Risk Management: — Sets enterprise operational direction and develops long-term planning and risk-management approaches that shape company operating policy at the division and executive level.

Model-authored from 22 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

Operations (Non-Manufacturing Ops) — 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 M1 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$152k

San Francisco-Oakland-Fremont, CA$136k

Washington-Arlington-Alexandria, DC-VA-MD-WV$126k

Boulder, CO$124k

Seattle-Tacoma-Bellevue, WA$124k

Boston-Cambridge-Newton, MA-NH$119k

Lexington Park, MD$116k

Trenton-Princeton, NJ$115k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $91k; 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 M1 (M1 — Manager (Team Lead)) from the levels around it — this level only. Every word is real canon for this profile.

Scope: A single team
Autonomy: Manages within established goals
Complexity: Day-to-day delivery and people issues
Impact: Team output and health
Decision rights: Owns team execution, hiring input, performance
Leadership: Direct people management of one team

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

Promotion

The next rung from M1 is M2.

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
VP of Operations	jf_operations_non_manufacturing_ops::E1	$180,226	confident0.06
Operations Associate	jf_operations_non_manufacturing_ops::P1	$65,793	confident0.06
Operations Coordinator	jf_operations_non_manufacturing_ops::S1	$39,694	confident0.06
Senior Operations Supervisor	jf_operations_non_manufacturing_ops::M2	$117,473	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

M1

jf_operations_non_manufacturing_ops::M1

Operations Supervisor

Operations (Non-Manufacturing Ops)

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: corporate_services
Function: executive_general_management
Family: operations_non_manufacturing_ops
Universal level: 1 / 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").

Administration · M1review0.17

Country Management · M1review0.17

General · M1review0.17

Region Leadership · M1review0.17

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)