People Analytics Toolbox

Ask about this role

grounded in this role's JobFrame record + structure

Download this job's description (markdown) → derived live from this record.

Key statistics

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

3 of 8

Track

Individual contributor

Family rungs priced

—

11-0000

—

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Independently leads continuous improvement initiatives across manufacturing operations to improve safety, quality, cost, and delivery performance. 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

Independently lead improvement initiatives across a manufacturing area, selecting appropriate Lean/Six Sigma tools for diverse process problems and planning own work.
Manage specific operational areas end-to-end, implementing process improvements and quantifying results in throughput, scrap, and cost savings.
Analyze multi-variable process data using SPC and OEE tracking to diagnose root causes and drive corrective action.
Collaborate with cross-functional teams (production, quality, maintenance) and train junior staff on continuous-improvement methods.
Coordinate the activities of a small improvement project, networking with senior engineers to align solutions with line requirements.

Knowledge applied: Applies broad continuous-improvement expertise across diverse process problems, selecting tools independently and evaluating identifiable factors.
Complexity & problem solving: Diagnoses multi-variable process issues via SPC/OEE and drives root-cause corrective action with day-to-day independence.
Collaboration & interaction: Networks with senior engineers and cross-functional teams; may coordinate a small project.
Typical experience: 5+ years (BA), 3+ years (MA), or PhD without experience.

Skills at this level

Lean Manufacturing: — Applies Toyota-derived Lean techniques — waste elimination, flow, and pull — to reduce production time and cost across manufacturing lines and cells.
Six Sigma / Lean Six Sigma: — Uses the DMAIC (and advanced DMADV) improvement cycle to reduce process defects and variation and drive measurable organizational change on the shop floor.
Value Stream Mapping: — Maps the end-to-end material and information flow of a production process to expose waste, bottlenecks, and improvement opportunities.
Kaizen Event Facilitation: — Plans and runs focused, time-boxed continuous-improvement events that mobilize floor teams to redesign a process and implement changes quickly.
5S and Visual Management: — Organizes and standardizes the workplace and deploys visual controls so process status and abnormalities are immediately apparent to operators and leaders.
Statistical Process Control (SPC): — Monitors process behavior with control charts and statistical analysis to distinguish common-cause from special-cause variation and trigger corrective action.
OEE Analysis: — Tracks Overall Equipment Effectiveness — availability, performance, and quality — to quantify equipment losses and target improvement on production lines.
Standard Work and Daily Management: — Establishes documented best-practice work sequences and daily management routines that sustain process stability and surface deviations for action.
PDCA Problem-Solving: — Drives structured Plan-Do-Check-Act cycles to test process changes iteratively and lock in verified improvements.
Root Cause Analysis: — Investigates process and quality failures to find and eliminate underlying causes, preventing recurrence across production.
Total Productive Maintenance (TPM): — Engages operators and maintenance in preventive care of equipment to minimize unplanned downtime and stabilize production output.
Operational Excellence Framework Design: — Defines the management system, maturity model, and standardized methodology that govern how continuous improvement is executed across sites.
Manufacturing Automation Integration: — Evaluates and deploys automation, AI, and robotics into production lines to raise efficiency while balancing cost, quality, and reliability trade-offs.
Cross-Functional Team Leadership: — Aligns production, quality, maintenance, and engineering stakeholders around shared improvement goals and drives change through influence.

Model-authored from 19 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

No priced rungs for this family's ladder yet.

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

+26.2%Industrial Production Managers · United States

185,790 → 234,380 jobs

17,100 openings / year, split into:

replacement / exits 16,640

Growth — new jobs (3%)Exits — backfill (97%)

3% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +1.9%
Growth share of hiring: 3%
Exit / retention pressure high: 97%
Openings / year: 17,100

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$137k

San Francisco-Oakland-Fremont, CA$123k

Washington-Arlington-Alexandria, DC-VA-MD-WV$114k

Boulder, CO$112k

Seattle-Tacoma-Bellevue, WA$112k

Boston-Cambridge-Newton, MA-NH$108k

Lexington Park, MD$105k

Trenton-Princeton, NJ$104k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $82k; 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
Accountant	jfo_jfm_accounting-controllership-accounting-management::P3	—	confident0.00
General Accountant	jfo_jfm_accounting-general-accounting-management::P3	—	confident0.00
Administrative Services Coordinator	jfo_jfm_administrative-corporate-services-administrative-services-ma::P3	—	confident0.00
Administrative Coordinator	jfo_jfm_administrative-corporate-services-administrative-support::P3	—	confident0.00

Capability cluster

O*NET 11-3051

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

jfo_jfm_manufacturing-operations-operational-excellence-in-manufactu::P3

Operational Excellence Engineer

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
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").

jfo_jfm_accounting-controllership-accounting-management::P3 · P3confident0.00

jfo_jfm_accounting-general-accounting-management::P3 · P3confident0.00

jfo_jfm_administrative-corporate-services-administrative-services-ma::P3 · P3confident0.00

jfo_jfm_administrative-corporate-services-administrative-support::P3 · P3confident0.00

jfo_jfm_business-development-corporate-strategy::P3 · P3confident0.00

jfo_jfm_business-process-operations-management-general-operations::P3 · P3confident0.00

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 11-3051	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 of2026-07-31Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)